diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml deleted file mode 100644 index 54f5794..0000000 --- a/.github/workflows/build.yml +++ /dev/null @@ -1,46 +0,0 @@ -# This is a basic workflow to help you get started with Actions - -name: build - -# Controls when the workflow will run -on: - # Triggers the workflow on push or pull request events but only for the master branch - push: - branches: [ master ] - pull_request: - branches: [ master ] - - # Allows you to run this workflow manually from the Actions tab - workflow_dispatch: - -# A workflow run is made up of one or more jobs that can run sequentially or in parallel -jobs: - # This workflow contains a single job called "build" - build: - # The type of runner that the job will run on - runs-on: ubuntu-latest - strategy: - matrix: - python-version: ['3.5', '3.6', '3.7', '3.8', '3.9', '3.10'] - - # Steps represent a sequence of tasks that will be executed as part of the job - steps: - # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it - - uses: actions/checkout@v2 - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - # Install dependencies - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install pytest-cov - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - python setup.py install - - # Run pytest - - name: Test with pytest - run: pytest \ No newline at end of file diff --git a/.github/workflows/codecov.yml b/.github/workflows/codecov.yml index bf365a6..0f13af2 100644 --- a/.github/workflows/codecov.yml +++ b/.github/workflows/codecov.yml @@ -2,14 +2,7 @@ name: codecov.io # Controls when the workflow will run on: - # Triggers the workflow on push or pull request events but only for the master branch - push: - branches: [ master ] - pull_request: - branches: [ master ] - - # Allows you to run this workflow manually from the Actions tab - workflow_dispatch: + workflow_call: # A workflow run is made up of one or more jobs that can run sequentially or in parallel jobs: @@ -21,22 +14,23 @@ jobs: # Steps represent a sequence of tasks that will be executed as part of the job steps: - name: Checkout - uses: actions/checkout@v2 + uses: actions/checkout@v5 - - name: Set up Python 3.9 - uses: actions/setup-python@v2 + - name: Set up Python + uses: actions/setup-python@v6 with: - python-version: 3.9 + python-version: '3.x' - name: Install dependencies run: | pip install -r requirements.txt pip install pytest-cov - python setup.py install + pip install pytest-mock + pip install . - name: Run tests and collect coverage run: pytest --cov=./mwtab --cov-report=xml # codecov - name: "Upload coverage to Codecov" - uses: codecov/codecov-action@v2 \ No newline at end of file + uses: codecov/codecov-action@v5 \ No newline at end of file diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml new file mode 100644 index 0000000..4139114 --- /dev/null +++ b/.github/workflows/documentation.yml @@ -0,0 +1,48 @@ +name: Publish Documentation + +on: + workflow_call: + +jobs: + publish-documentation: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v5 + - uses: actions/setup-python@v6 + with: + python-version: '3.x' + - name: Upgrade pip, install package, install requirements, build docs + run: | + pip install --upgrade pip + pip install . + if [ -f ./docs/requirements.txt ]; then pip install -r ./docs/requirements.txt; fi + pip install sphinx + sphinx-build docs ./docs/_build/html/ + # Create an artifact of the html output. + - uses: actions/upload-artifact@v4 + with: + name: DocumentationHTML + path: docs/_build/html/ + # Publish built docs to gh-pages branch. + # =============================== + - name: Commit documentation changes + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + git config --global user.name "${GITHUB_ACTOR}" + git config --global user.email "${GITHUB_ACTOR}@users.noreply.github.com" + git clone "https://token:${GITHUB_TOKEN}@github.com/${GITHUB_REPOSITORY}.git" --branch gh-pages --single-branch gh-pages + cp -r docs/_build/html/* gh-pages/ + cd gh-pages + touch .nojekyll + git add . + git commit -m "Update documentation." -a || true + # The above command will fail if no changes were present, so we ignore + # that. + - name: Push changes + uses: ad-m/github-push-action@master + with: + branch: gh-pages + directory: gh-pages + github_token: ${{ secrets.GITHUB_TOKEN }} + # =============================== diff --git a/.github/workflows/package_release.yml b/.github/workflows/package_release.yml new file mode 100644 index 0000000..04b0636 --- /dev/null +++ b/.github/workflows/package_release.yml @@ -0,0 +1,47 @@ +name: Package and Documentation Release + +on: + release: + types: [published] + +jobs: + release-version: + runs-on: ubuntu-latest + steps: + - id: parse-version + name: Parse release version + run: | + echo "version=${RELEASE_VERSION/v/}" >> "$GITHUB_OUTPUT" + env: + RELEASE_VERSION: ${{ github.event.release.tag_name }} + outputs: + version: ${{ steps.parse-version.outputs.version }} + publish-test-pypi: + uses: ./.github/workflows/pypi.yml + with: + repository_url: https://test.pypi.org/legacy/ + secrets: + API_TOKEN: ${{ secrets.TEST_PYPI_API_TOKEN }} + test-test-pypi: + needs: [release-version, publish-test-pypi] + uses: ./.github/workflows/tests.yml + with: + install_command: "python3 -m pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple mwtab==${{ needs.release-version.outputs.version }}" + publish-pypi: + needs: test-test-pypi + uses: ./.github/workflows/pypi.yml + with: + repository_url: https://upload.pypi.org/legacy/ + secrets: + API_TOKEN: ${{ secrets.PYPI_API_TOKEN }} + test-pypi: + needs: [release-version, publish-pypi] + uses: ./.github/workflows/tests.yml + with: + install_command: "python3 -m pip install mwtab==${{ needs.release-version.outputs.version }}" + publish-documentation: + needs: test-pypi + uses: ./.github/workflows/documentation.yml + upload-coverage: + needs: test-pypi + uses: ./.github/workflows/codecov.yml diff --git a/.github/workflows/pull_request.yml b/.github/workflows/pull_request.yml new file mode 100644 index 0000000..eebdbe2 --- /dev/null +++ b/.github/workflows/pull_request.yml @@ -0,0 +1,16 @@ +name: Pull request + +on: + push: + branches: + - main + pull_request: + branches: + - main + workflow_dispatch: + +jobs: + pull-request: + uses: ./.github/workflows/tests.yml + with: + install_command: "python3 -m pip install -e ." diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml new file mode 100644 index 0000000..ca440a6 --- /dev/null +++ b/.github/workflows/pypi.yml @@ -0,0 +1,32 @@ +name: Publish package + +on: + workflow_call: + inputs: + repository_url: + description: The URL of the PyPi distribution + required: true + type: string + secrets: + API_TOKEN: + required: true + +jobs: + publish-package: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.x' + - name: Install dependencies + run: | + python3 -m pip install --upgrade pip + python3 -m pip install build + - name: Build package + run: python3 -m build + - name: Publish package to a PyPi distribution + uses: pypa/gh-action-pypi-publish@release/v1 + with: + password: ${{ secrets.API_TOKEN }} + repository-url: ${{ inputs.repository_url }} diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..7a67942 --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,40 @@ +name: Run Tests + +on: + workflow_call: + inputs: + install_command: + description: The command for installing the package to test. + required: true + type: string + +jobs: + run-tests: + strategy: + matrix: + python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"] + os: [ ubuntu-latest, windows-latest, macOS-latest ] + runs-on: ${{matrix.os}} + steps: + - uses: actions/checkout@v5 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python-version }} + - name: Install testing environment + run: | + python3 -m pip install --upgrade pip + python3 -m pip install pytest pytest-mock pytest-cov deepdiff + - name: Install package + uses: Wandalen/wretry.action@master + with: + command: ${{ inputs.install_command }} + attempt_limit: 10 + attempt_delay: 10000 + - name: Run tests on package + run: pytest + # - name: Debug with tmate on failure + # if: ${{ failure() }} + # uses: mxschmitt/action-tmate@v3 + # with: + # limit-access-to-actor: true diff --git a/.gitignore b/.gitignore index a7ada75..1353022 100755 --- a/.gitignore +++ b/.gitignore @@ -4,4 +4,14 @@ venv/* data/* .DS_Store .ipynb_checkpoints -.idea \ No newline at end of file +.idea +/build +.coverage +/*.json +/htmlcov +/mwtab.egg-info +/src/mwtab.egg-info +src/mwtab/_version.py +/scratch +docs/standard_column_names.json +/docs/_contents diff --git a/CHANGELOG.rst b/CHANGELOG.rst index a18e9c7..5286670 100755 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -1,6 +1,23 @@ Release History =============== +2.0.0 +~~~~~ +-Can now read duplicate keys in "Additional sample data" and reproduce it in write, will validate it as an error. +-Writing out now ensures correct key ordering for JSON. +-Validation now validates the main sections not just the internals of them. +-Validate now checks that metabolites in the Data section are in the Metabolites section and vice versa. +-Batch processing from the command line is more fault tolerant and won't stop the batch for 1 bad file. +-Improved tokenizer so more files can be read in without error. +-Changed schema validation to use jsonschema instead of schema. +-Added validations for METABOLITES columns that try to give warnings for bad values, for example 'kegg_id' column should all be something like C00000. +-Expanded the standard column name functionality to look for many more column names than in the previous version and do it in a much more robust way. +-Changed validation error messages to be aware of whether the input file is JSON or mwTab and print accordingly. +-Reduced many spurious validation messages. +-Added a validation for when certain columns are found in METABOLITES, to look for the implied pair and warn if it isn't there. For example, retention_index and retention_index_type. +-Added validations on some values, such as gender. +-Many more various minor validations were added. + 1.2.5.post1 (2022-05-11) ~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/LICENSE b/LICENSE index d50f164..6aac1dc 100755 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ The Clear BSD License -Copyright (c) 2020, Christian D. Powell, Andrey Smelter, Hunter N.B. Moseley +Copyright (c) 2025, P. Travis Thompson, Christian D. Powell, Andrey Smelter, Hunter N.B. Moseley All rights reserved. Redistribution and use in source and binary forms, with or without @@ -8,15 +8,15 @@ modification, are permitted (subject to the limitations in the disclaimer below) provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. + this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from this - software without specific prior written permission. + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND diff --git a/README.rst b/README.rst index 54551e0..b2e152e 100755 --- a/README.rst +++ b/README.rst @@ -13,10 +13,6 @@ mwtab :target: https://pypi.org/project/mwtab :alt: Supported Python versions -.. image:: https://readthedocs.org/projects/nmrstarlib/badge/?version=latest - :target: http://mwtab.readthedocs.io/en/latest/?badge=latest - :alt: Documentation status - .. image:: https://github.com/MoseleyBioinformaticsLab/mwtab/actions/workflows/build.yml/badge.svg :target: https://github.com/MoseleyBioinformaticsLab/mwtab/actions/workflows/build.yml :alt: Build status @@ -38,7 +34,7 @@ mwtab .. image:: https://raw.githubusercontent.com/MoseleyBioinformaticsLab/mwtab/master/docs/_static/images/mwtab_logo.png :width: 50% :align: center - :target: http://mwtab.readthedocs.io/ + :target: https://moseleybioinformaticslab.github.io/mwtab/ The ``mwtab`` package is a Python library that facilitates reading and writing @@ -77,7 +73,7 @@ Links * mwtab @ GitHub_ * mwtab @ PyPI_ - * Documentation @ ReadTheDocs_ + * Documentation @ Pages_ Installation @@ -139,7 +135,7 @@ Quickstart :align: center -.. note:: Read the User Guide and the ``mwtab`` Tutorial on ReadTheDocs_ +.. note:: Read the User Guide and the ``mwtab`` Tutorial on Pages_ to learn more and to see code examples on using the ``mwtab`` as a library and as a command-line tool. @@ -152,7 +148,7 @@ This package is distributed under the BSD_ `license`. .. _Metabolomics Workbench: http://www.metabolomicsworkbench.org .. _GitHub: https://github.com/MoseleyBioinformaticsLab/mwtab -.. _ReadTheDocs: http://mwtab.readthedocs.io +.. _Pages: https://moseleybioinformaticslab.github.io/mwtab/ .. _PyPI: https://pypi.org/project/mwtab .. _pip: https://pip.pypa.io .. _BSD: https://choosealicense.com/licenses/bsd-3-clause-clear/ diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css new file mode 100644 index 0000000..61dd224 --- /dev/null +++ b/docs/_static/css/custom.css @@ -0,0 +1,9 @@ +.page-toc .nav-link { + font-size: var(--pst-sidebar-header-font-size); + font-weight: var(--pst-sidebar-header-font-weight); +} + +.page-toc .nav-link code.xref, a code { + font-size: var(--pst-sidebar-font-size); + font-weight: ; +} diff --git a/docs/api.rst b/docs/api.rst index bdf12df..c78a1e9 100755 --- a/docs/api.rst +++ b/docs/api.rst @@ -4,9 +4,14 @@ The mwtab API Reference .. automodule:: mwtab -.. automodule:: mwtab.mwtab - :member-order: bysource - :members: +.. automodule:: mwtab.mwtab + + .. autoclass:: MWTabFile + :member-order: bysource + :members: + :exclude-members: validate + + .. automethod:: validate(ms_schema = mwschema.ms_required_schema, nmr_schema = mwschema.nmr_required_schema, verbose = True) .. automodule:: mwtab.cli @@ -31,9 +36,7 @@ The mwtab API Reference .. automodule:: mwtab.validator -.. autofunction:: validate_section - -.. autofunction:: validate_file +.. autofunction:: validate_file(mwtabfile, ms_schema, nmr_schema, verbose = False) .. automodule:: mwtab.mwrest @@ -46,49 +49,8 @@ The mwtab API Reference :members: -.. automodule:: mwtab.mwschema - -.. autodata:: metabolomics_workbench_schema - :annotation: - -.. autodata:: project_schema - :annotation: - -.. autodata:: study_schema - :annotation: - -.. autodata:: analysis_schema - :annotation: - -.. autodata:: subject_schema - :annotation: - -.. autodata:: subject_sample_factors_schema - :annotation: - -.. autodata:: collection_schema - :annotation: - -.. autodata:: treatment_schema - :annotation: - -.. autodata:: sampleprep_schema - :annotation: - -.. autodata:: chromatography_schema - :annotation: - -.. autodata:: ms_schema - :annotation: - -.. autodata:: nmr_schema - :annotation: +.. automodule:: mwtab.metadata_column_matching + :members: -.. autodata:: metabolites_schema - :annotation: -.. autodata:: ms_metabolite_data_schema - :annotation: -.. autodata:: nmr_binned_data_schema - :annotation: diff --git a/docs/conf.py b/docs/conf.py index 5e2bdd1..b3562a2 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -19,10 +19,16 @@ # import os import sys +import json # sys.path.insert(0, os.path.abspath('.')) sys.path.insert(0, os.path.abspath('..')) -from mwtab import __version__ +from mwtab import __version__, metadata_column_matching + +standard_column_names = [name for name in metadata_column_matching.column_finders] +with open('standard_column_names.json','w') as jsonFile: + jsonFile.write(json.dumps(standard_column_names, indent=2)) + # -- General configuration ------------------------------------------------ @@ -33,15 +39,21 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = ['sphinx.ext.autodoc', +extensions = [ + 'sphinx.ext.autodoc', 'sphinx.ext.doctest', + 'sphinx.ext.autosummary', 'sphinx.ext.intersphinx', + 'sphinx.ext.autosectionlabel', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', - 'nbsphinx'] + 'nbsphinx', + 'sphinx_design', + 'sphinx.ext.napoleon', + ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] @@ -74,7 +86,7 @@ # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = None +language = 'en' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. @@ -90,21 +102,39 @@ # -- Options for HTML output ---------------------------------------------- +napoleon_custom_sections = [('attributes', 'params_style')] +autoclass_content = 'class' +autodoc_typehints = 'description' +autodoc_typehints_description_target = 'documented_params' +autodoc_member_order = 'bysource' + # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = 'alabaster' +# html_theme = "sphinx_rtd_theme" +html_theme = "pydata_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} +html_sidebars = { + "**": ['page-toc'] +} +html_theme_options = { + "secondary_sidebar_items": { + "**": [], + }, +} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] +html_css_files = [ + 'css/custom.css', +] # -- Options for HTMLHelp output ------------------------------------------ @@ -188,4 +218,14 @@ # Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = {'https://docs.python.org/3': None} +intersphinx_mapping = {'python': ('https://docs.python.org/3', None)} + + +# ----------------------------------------------------------------------------- +# Autosummary +# ----------------------------------------------------------------------------- + +autosummary_generate = True + +# Autosection options +autosectionlabel_prefix_document = True diff --git a/docs/guide.rst b/docs/guide.rst index 8819d8b..6cc8fac 100755 --- a/docs/guide.rst +++ b/docs/guide.rst @@ -22,7 +22,7 @@ The ``mwtab`` package can be used in several ways: Installation ~~~~~~~~~~~~ -The :mod:`mwtab` package runs under Python 2.7 and Python 3.4+. +The :mod:`mwtab` package runs under Python 3.6+. Starting with Python 3.4, pip_ is included by default. To install system-wide with pip_ run the following: @@ -96,13 +96,29 @@ run the following commands: python3 -m pip install docopt # On Linux, Mac OS X py -3 -m pip install docopt # On Windows - * schema_ for validating functionality of ``mwTab`` files based on ``JSON`` schema. - * To install the schema_ Python library run the following: + * jsonschema_ for validating functionality of ``mwTab`` files based on ``JSON`` schema. + * To install the jsonschema_ Python library run the following: .. code:: bash - python3 -m pip install schema # On Linux, Mac OS X - py -3 -m pip install schema # On Windows + python3 -m pip install jsonschema # On Linux, Mac OS X + py -3 -m pip install jsonschema # On Windows + + * pandas_ for working with tabular data sections within the mwTab format. + * To install the pandas_ Python library run the following: + + .. code:: bash + + python3 -m pip install pandas # On Linux, Mac OS X + py -3 -m pip install pandas # On Windows + + * setuptools-scm_ for handling versioning. + * To install the setuptools-scm_ Python library run the following: + + .. code:: bash + + python3 -m pip install setuptools-scm # On Linux, Mac OS X + py -3 -m pip install setuptools-scm # On Windows Basic usage @@ -133,5 +149,7 @@ The :mod:`mwtab` package can be used in several ways: .. _pip: https://pip.pypa.io/ .. _virtualenv: https://virtualenv.pypa.io/ .. _docopt: https://pypi.org/project/docopt/ -.. _schema: https://pypi.org/project/schema/ +.. _jsonschema: https://pypi.org/project/jsonschema/ +.. _pandas: https://pypi.org/project/pandas/ +.. _setuptools-scm: https://pypi.org/project/setuptools-scm/ .. _Metabolomics Workbench: http://www.metabolomicsworkbench.org/ diff --git a/docs/index.rst b/docs/index.rst index 46c233f..dcedf92 100755 --- a/docs/index.rst +++ b/docs/index.rst @@ -12,8 +12,10 @@ Documentation index: guide tutorial + metadata_column_matching api license + todo Indices and tables diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..7893348 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/docs/metadata_column_matching.rst b/docs/metadata_column_matching.rst new file mode 100644 index 0000000..84e591f --- /dev/null +++ b/docs/metadata_column_matching.rst @@ -0,0 +1,92 @@ +Metadata Column Matching +======================== +The mwTab format gives a lot of freedom to uploaders in what kind of metadata associated +with the assigned metabolites can be given. While this has its merits, a big drawback +is the amount of different names given for the same column of information. For example, +using both 'm/z' and 'moverz'. Another drawback is the variety of value formats for +some columns. For example, a column containing HMDB IDs might leave the 'HMDB' prefix +off of the beginning of the ID, or the 'HMDB' prefix might be lower case. To address +these issues a significant amount of work was done to data mine the most used and +most useful columns and using this information code was created to be able to identify the columns under +their various names as well as evaluate the format of their values. + +This code is utilized in the validation to inform users about the state of their +metadata columns in the METABOLITES section. In order to do this we chose standardized +column names and value formats based on the information obtained during data mining, +so there were some executive decisions we made in deciding what the standard names and +values are or should be. We did our best to base them on the data already in the +Metabolomics Workbench. + +We did our best to test this code on the data in the Metabolomics Workbench, but it +is not perfect. Due to the nature of regular expressions and trying to match a variety +of names and values, inevitably there are going to be false positives and negatives. +You may see a validation warning about a column name or value in error. If you do +find incorrect warnings or issues that are not being warned about in the validation, +please visit our GitHub Issues_ and create a new issue for it. + + + + +Reusing Match Code +~~~~~~~~~~~~~~~~~~ +Although the mwtab package is largely meant to be used through its CLI, the code +created for column name and value matching could have many more use cases outside +of this package. This section will explain the different parts of the code and how +they might be used outside of this package. + +The 3 significant parts are: +1. ColumnFinder class +2. Regular expressions and associated functions +3. Dictionary of ColumnFinders for specific mwTab columns + + +ColumnFinder Class +------------------ +Column matching has 2 components, matching column names and matching column values, +therefore, a ColumnFinder has a NameMatcher and ValueMatcher to handle those functions. +All NameMatcher attributes are lists of strings or lists of lists of strings and all +are used in its only method, dict_match. All ValueMatcher attributes are strings and +all are used in its only method, series_match. ColumnFinder takes both a NameMatcher +and ValueMatcher, along with a standard_name attribute. The "standard_name" attribute +is not used by any method and is simply there to tie the instance of the class to a +name. The :ref:`api:Metadata Column Matching` page for metadata_column_matching.py +contains more detailed information and examples on each class. + + +Regular Expressions +------------------- +In order to create some of the complex regular expressions needed to validate column +names and values, a modular approach to constructing them was taken. This means the +smaller building block regular expressions could have use outside of this particular +one. There is also a function used heavily in creating these regular expressions, +make_list_regex. The :ref:`api:Metadata Column Matching` page for metadata_column_matching.py +contains more detailed information about using this function. There are too many +regular expressions to explain each one, so the source code for creating them is +included below. The names are largely self explanatory, and being able to see the +regular expression for each name can help clear up confusion. + +.. literalinclude:: ../src/mwtab/metadata_column_matching.py + :start-after: # Comment for Sphinx to pull in regular expressions. + :end-before: # Comment for Sphinx to find the end of regular expressions. + + +column_finders Dictionary +------------------------- +The column_finders dictionary is the culmination of the ColumnFinder class and +regular expressions for the purpose of finding and evaluating/validating columns +in Metabolomics Workbench datasets. The keys are standardized column names, and the +values are a ColumnFinder class to find and validate that column. The entire library +of datasets in the Metbolomics Workbench was used to determine the most relevant and +most abundant columns that should go into the column_finders dictionary. The column_finders +dictionary is likely to be useful for other similar metabolomics data, but may vary +outside of that usecase. Some columns, such as database ID columns, like PubChem or +KEGG, are likely to be more widely useable, but any user would have to test and +make that determination for themselves. The list of standardized column names available +in the dictionary is given below. + +.. literalinclude:: standard_column_names.json + + + + +.. _Issues: https://github.com/MoseleyBioinformaticsLab/mwtab/issues \ No newline at end of file diff --git a/docs/todo.rst b/docs/todo.rst new file mode 100644 index 0000000..fe06efb --- /dev/null +++ b/docs/todo.rst @@ -0,0 +1,12 @@ +TODO +==== + +Add options to validate CLI and validate method in mwtab to save out the new JSON file. + +Add --limit or --ignore option to validate to filter out certain types of errors/warnings. Need to first create some classifications to tag them with. + +Think about extending METABOLITES and EXTENDED blocks with an "Attributes" line like "Factors" in DATA block as a way to add more information about the columns themselves. +Hunter also wanted to consider adding things like the _factors properties into the JSON as well. For example, the _factors could be added into ['MS_METABOLITE_DATA'] under a 'Factors' key. + +Think about adding an "UNASSIGNED" data block for the datasets we found that have a results_file instead of having the data in the mwTab file. +Pretty sure most of these if not all are all unnassigned data where there are basically bins and no metabolite assinments. diff --git a/docs/tutorial.ipynb b/docs/tutorial.ipynb index 5804fe6..080262d 100755 --- a/docs/tutorial.ipynb +++ b/docs/tutorial.ipynb @@ -13,7 +13,7 @@ "parsing, accessing, and manipulating data stored in either the ``mwTab`` or\n", "``JSON`` representation of ``mwTab`` files.\n", "\n", - "Also, the :mod:`mwtab` package provides simple command-line interface to convert\n", + "Also, the :mod:`mwtab` package provides a simple command-line interface to convert\n", "between ``mwTab`` and ``JSON`` representations, download entries from\n", "Metabolomics Workbench, access the MW REST interface, validate the consistency\n", "of the ``mwTab`` files, or extract metadata and metabolites from these files." @@ -31,7 +31,7 @@ "\n", ".. note::\n", "\n", - " For full official specification see the following link (``mwTab file specification``):\n", + " For the full official specification see the following link (``mwTab file specification``):\n", " http://www.metabolomicsworkbench.org/data/tutorials.php\n", "\n", "\n", @@ -52,11 +52,11 @@ "\n", ".. note::\n", "\n", - " ``*_SUMMARY`` \"key-value\"-like pairs are typically span through multiple lines.\n", + " Some \"key-value\"-like pairs can span multiple lines.\n", "\n", "\n", - "* ``#SUBJECT_SAMPLE_FACTORS`` block is specially formatted, i.e. it contains header\n", - " specification and tab-separated values.\n", + "* The ``#SUBJECT_SAMPLE_FACTORS`` block is specially formatted, i.e. it contains header\n", + " specification and tab-separated values.\n", "\n", ".. code-block:: none\n", "\n", @@ -69,7 +69,7 @@ " SUBJECT_SAMPLE_FACTORS \t-\tLabF_115898\tArabidopsis Genotype:Wassilewskija (Ws) | Plant Wounding Treatment:Control - Non-Wounded\n", "\n", "\n", - "* ``#MS_METABOLITE_DATA`` (results) block contains ``Samples`` identifiers, ``Factors`` identifiers\n", + "* The ``#MS_METABOLITE_DATA`` (results) block contains ``Samples`` identifiers, ``Factors`` identifiers\n", " as well as tab-separated data between ``*_START`` and ``*_END``.\n", "\n", ".. code-block:: none\n", @@ -84,7 +84,7 @@ " ...\n", " MS_METABOLITE_DATA_END\n", "\n", - "* ``#METABOLITES`` metadata block contains a header specifying fields and\n", + "* The ``#METABOLITES`` metadata block contains a header specifying fields and\n", " tab-separated data between ``*_START`` and ``*_END``.\n", "\n", ".. code-block:: none\n", @@ -99,8 +99,10 @@ " ...\n", " METABOLITES_END\n", "\n", - "* ``#NMR_BINNED_DATA`` metadata block contains a header specifying fields and\n", - " tab-separated data between ``*_START`` and ``*_END``.\n", + "* The ``#NMR_BINNED_DATA`` metadata block contains a header specifying fields and\n", + " tab-separated data between ``*_START`` and ``*_END``. Note that this block has\n", + " been deprecated. Binned data is now uploaded as a separate text file and referenced\n", + " with the “NM:NMR_RESULTS_FILE” tag in the NM metadata block.\n", "\n", ".. code-block:: none\n", "\n", @@ -163,8 +165,8 @@ "~~~~~~~~~~~~~~~~~~~~~~~~\n", "\n", "\n", - "Importing mwtab Package\n", - "-----------------------\n", + "Importing the mwtab Package\n", + "---------------------------\n", "\n", "If the :mod:`mwtab` package is installed on the system, it can be imported:" ] @@ -184,13 +186,13 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Constructing MWTabFile Generator\n", - "--------------------------------\n", + "Constructing a MWTabFile Generator\n", + "----------------------------------\n", "\n", "The :mod:`~mwtab.fileio` module provides the :func:`~mwtab.fileio.read_files`\n", "generator function that yields :class:`~mwtab.mwtab.MWTabFile` instances. Constructing a\n", ":class:`~mwtab.mwtab.MWTabFile` generator is easy - specify the path to a local ``mwTab`` file,\n", - "directory of files, archive of files:" + "directory of files, or archive of files:" ] }, { @@ -216,8 +218,8 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Processing MWTabFile Generator\n", - "------------------------------\n", + "Processing a MWTabFile Generator\n", + "--------------------------------\n", "\n", "The :class:`~mwtab.mwtab.MWTabFile` generator can be processed in several ways:\n", "\n", @@ -312,7 +314,8 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - ".. note:: Once the generator is consumed, :py:class:`StopIteration` will be raised." + ".. note:: Once the generator is consumed, the :py:class:`StopIteration` Exception\n", + " will be raised on the subsequent next call." ] }, { @@ -355,11 +358,11 @@ "Accessing Data From a Single MWTabFile\n", "--------------------------------------\n", "\n", - "Since a :class:`~mwtab.mwtab.MWTabFile` is a Python :py:class:`collections.OrderedDict`,\n", + "Since a :class:`~mwtab.mwtab.MWTabFile` is a Python :py:class:`dict`,\n", "data can be accessed and manipulated as with any regular Python :py:class:`dict` object\n", "using bracket accessors.\n", "\n", - "* Accessing top-level \"keys\" in :class:`~mwtab.mwtab.MWTabFile`:" + "* Accessing the top-level \"keys\" in a :class:`~mwtab.mwtab.MWTabFile`:" ] }, { @@ -425,18 +428,18 @@ { "data": { "text/plain": [ - "OrderedDict([('PROJECT_TITLE', 'Rat Stamina Studies'),\n", - " ('PROJECT_TYPE', 'Feeding'),\n", - " ('PROJECT_SUMMARY', 'Stamina in rats'),\n", - " ('INSTITUTE', 'University of Michigan'),\n", - " ('DEPARTMENT', 'Internal Medicine'),\n", - " ('LABORATORY', 'Burant Lab'),\n", - " ('LAST_NAME', 'Beecher'),\n", - " ('FIRST_NAME', 'Chris'),\n", - " ('ADDRESS', '-'),\n", - " ('EMAIL', 'chrisbee@med.umich.edu'),\n", - " ('PHONE', '734-232-0815'),\n", - " ('FUNDING_SOURCE', 'NIH: R01 DK077200')])" + "{'PROJECT_TITLE': 'Rat Stamina Studies',\n", + " 'PROJECT_TYPE': 'Feeding',\n", + " 'PROJECT_SUMMARY': 'Stamina in rats',\n", + " 'INSTITUTE': 'University of Michigan',\n", + " 'DEPARTMENT': 'Internal Medicine',\n", + " 'LABORATORY': 'Burant Lab',\n", + " 'LAST_NAME': 'Beecher',\n", + " 'FIRST_NAME': 'Chris',\n", + " 'ADDRESS': '-',\n", + " 'EMAIL': 'chrisbee@med.umich.edu',\n", + " 'PHONE': '734-232-0815',\n", + " 'FUNDING_SOURCE': 'NIH: R01 DK077200'}" ] }, "execution_count": 8, @@ -485,7 +488,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "* Accessing data in ``#SUBJECT_SAMPLE_FACTORS`` block:" + "* Accessing data in the ``#SUBJECT_SAMPLE_FACTORS`` block:" ] }, { @@ -498,18 +501,15 @@ { "data": { "text/plain": [ - "[OrderedDict([('Subject ID', '-'),\n", - " ('Sample ID', 'S00009477'),\n", - " ('Factors',\n", - " {'Feeeding': 'Ad lib', 'Running Capacity': 'High'})]),\n", - " OrderedDict([('Subject ID', '-'),\n", - " ('Sample ID', 'S00009478'),\n", - " ('Factors',\n", - " {'Feeeding': 'Ad lib', 'Running Capacity': 'High'})]),\n", - " OrderedDict([('Subject ID', '-'),\n", - " ('Sample ID', 'S00009479'),\n", - " ('Factors',\n", - " {'Feeeding': 'Ad lib', 'Running Capacity': 'High'})])]" + "[{'Subject ID': '-',\n", + " 'Sample ID': 'S00009477',\n", + " 'Factors': {'Feeeding': 'Ad lib', 'Running Capacity': 'High'}},\n", + " {'Subject ID': '-',\n", + " 'Sample ID': 'S00009478',\n", + " 'Factors': {'Feeeding': 'Ad lib', 'Running Capacity': 'High'}},\n", + " {'Subject ID': '-',\n", + " 'Sample ID': 'S00009479',\n", + " 'Factors': {'Feeeding': 'Ad lib', 'Running Capacity': 'High'}}]" ] }, "execution_count": 10, @@ -530,9 +530,9 @@ { "data": { "text/plain": [ - "OrderedDict([('Subject ID', '-'),\n", - " ('Sample ID', 'S00009477'),\n", - " ('Factors', {'Feeeding': 'Ad lib', 'Running Capacity': 'High'})])" + "{'Subject ID': '-',\n", + " 'Sample ID': 'S00009477',\n", + " 'Factors': {'Feeeding': 'Ad lib', 'Running Capacity': 'High'}}" ] }, "execution_count": 11, @@ -572,7 +572,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "* Accessing data in ``#MS_METABOLITE_DATA`` block:" + "* Accessing data in the ``#MS_METABOLITE_DATA`` block:" ] }, { @@ -625,7 +625,7 @@ { "data": { "text/plain": [ - "odict_keys(['Metabolite', 'S00009477', 'S00009478', 'S00009479', 'S00009480', 'S00009481', 'S00009500', 'S00009501', 'S00009502', 'S00009503', 'S00009470', 'S00009471', 'S00009472', 'S00009473', 'S00009474', 'S00009475', 'S00009494', 'S00009495', 'S00009496', 'S00009497', 'S00009498', 'S00009499', 'S00009488', 'S00009489', 'S00009490', 'S00009491', 'S00009492', 'S00009493', 'S00009509', 'S00009510', 'S00009511', 'S00009512', 'S00009513', 'S00009514', 'S00009482', 'S00009483', 'S00009484', 'S00009486', 'S00009504', 'S00009505', 'S00009506', 'S00009507', 'S00009508'])" + "['Metabolite', 'S00009477', 'S00009478', 'S00009479', 'S00009480', 'S00009481', 'S00009500', 'S00009501', 'S00009502', 'S00009503', 'S00009470', 'S00009471', 'S00009472', 'S00009473', 'S00009474', 'S00009475', 'S00009494', 'S00009495', 'S00009496', 'S00009497', 'S00009498', 'S00009499', 'S00009488', 'S00009489', 'S00009490', 'S00009491', 'S00009492', 'S00009493', 'S00009509', 'S00009510', 'S00009511', 'S00009512', 'S00009513', 'S00009514', 'S00009482', 'S00009483', 'S00009484', 'S00009486', 'S00009504', 'S00009505', 'S00009506', 'S00009507', 'S00009508']" ] }, "execution_count": 15, @@ -646,33 +646,33 @@ { "data": { "text/plain": [ - "[OrderedDict([('Metabolite', '11BETA,21-DIHYDROXY-5BETA-PREGNANE-3,20-DIONE'),\n", - " ('moverz_quant', ''),\n", - " ('ri', ''),\n", - " ('ri_type', ''),\n", - " ('pubchem_id', '44263339'),\n", - " ('inchi_key', ''),\n", - " ('kegg_id', 'C05475'),\n", - " ('other_id', '775216_UNIQUE'),\n", - " ('other_id_type', 'UM_Target_ID')]),\n", - " OrderedDict([('Metabolite', '11-BETA-HYDROXYANDROST-4-ENE-3,17-DIONE'),\n", - " ('moverz_quant', ''),\n", - " ('ri', ''),\n", - " ('ri_type', ''),\n", - " ('pubchem_id', '94141'),\n", - " ('inchi_key', ''),\n", - " ('kegg_id', 'C05284'),\n", - " ('other_id', '771312_PRIMARY'),\n", - " ('other_id_type', 'UM_Target_ID')]),\n", - " OrderedDict([('Metabolite', '13(S)-HPODE'),\n", - " ('moverz_quant', ''),\n", - " ('ri', ''),\n", - " ('ri_type', ''),\n", - " ('pubchem_id', '1426'),\n", - " ('inchi_key', ''),\n", - " ('kegg_id', 'C04717'),\n", - " ('other_id', '775541_UNIQUE'),\n", - " ('other_id_type', 'UM_Target_ID')])]" + "[{'Metabolite': '11BETA,21-DIHYDROXY-5BETA-PREGNANE-3,20-DIONE',\n", + " 'moverz_quant': '',\n", + " 'ri': '',\n", + " 'ri_type': '',\n", + " 'pubchem_id': '44263339',\n", + " 'inchi_key': '',\n", + " 'kegg_id': 'C05475',\n", + " 'other_id': '775216_UNIQUE',\n", + " 'other_id_type': 'UM_Target_ID'},\n", + " {'Metabolite': '11-BETA-HYDROXYANDROST-4-ENE-3,17-DIONE',\n", + " 'moverz_quant': '',\n", + " 'ri': '',\n", + " 'ri_type': '',\n", + " 'pubchem_id': '94141',\n", + " 'inchi_key': '',\n", + " 'kegg_id': 'C05284',\n", + " 'other_id': '771312_PRIMARY',\n", + " 'other_id_type': 'UM_Target_ID'},\n", + " {'Metabolite': '13(S)-HPODE',\n", + " 'moverz_quant': '',\n", + " 'ri': '',\n", + " 'ri_type': '',\n", + " 'pubchem_id': '1426',\n", + " 'inchi_key': '',\n", + " 'kegg_id': 'C04717',\n", + " 'other_id': '775541_UNIQUE',\n", + " 'other_id_type': 'UM_Target_ID'}]" ] }, "execution_count": 16, @@ -694,7 +694,7 @@ "Manipulating Data From a Single MWTabFile\n", "-----------------------------------------\n", "\n", - "In order to change values within :class:`~mwtab.mwtab.MWTabFile`, descend into\n", + "In order to change values within the :class:`~mwtab.mwtab.MWTabFile`, descend into\n", "the appropriate level using square bracket accessors and set a new value.\n", "\n", "* Change regular \"key-value\" pairs:" @@ -769,9 +769,9 @@ { "data": { "text/plain": [ - "OrderedDict([('Subject ID', '-'),\n", - " ('Sample ID', 'S00009477'),\n", - " ('Factors', {'Feeeding': 'Ad lib', 'Running Capacity': 'High'})])" + "{'Subject ID': '-',\n", + " 'Sample ID': 'S00009477',\n", + " 'Factors': {'Feeeding': 'Ad lib', 'Running Capacity': 'High'}}" ] }, "execution_count": 20, @@ -802,11 +802,10 @@ { "data": { "text/plain": [ - "OrderedDict([('Subject ID', '-'),\n", - " ('Sample ID', 'S00009477'),\n", - " ('Factors', {'Feeeding': 'Ad lib', 'Running Capacity': 'High'}),\n", - " ('Additional sample data',\n", - " {'Additional detail key': 'Additional detail value'})])" + "{'Subject ID': '-',\n", + " 'Sample ID': 'S00009477',\n", + " 'Factors': {'Feeeding': 'Ad lib', 'Running Capacity': 'High'},\n", + " 'Additional sample data': {'Additional detail key': 'Additional detail value'}}" ] }, "execution_count": 22, @@ -825,8 +824,8 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Printing a MWTabFile and its Components\n", - "---------------------------------------\n", + "Printing an MWTabFile and its Components\n", + "----------------------------------------\n", "\n", "``MWTabFile`` objects provide the ``print_file()`` method which can be used to output the file in either `mwTab` or JSON format. The method takes a ``file_format`` keyword argument which specifices the output format to be displayed.\n", "\n", @@ -940,7 +939,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "* Print single block in ``mwTab`` format." + "* Print a single block in ``mwTab`` format." ] }, { @@ -982,7 +981,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "* Print single block in ``JSON`` format." + "* Print a single block in ``JSON`` format." ] }, { @@ -1022,7 +1021,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Writing data from a MWTabFile object into a file\n", + "Writing Data From a MWTabFile Object Into a File\n", "------------------------------------------------\n", "Data from a :class:`~mwtab.mwtab.MWTabFile` can be written into file\n", "in original ``mwTab`` format or in equivalent JSON format using\n", @@ -1114,7 +1113,7 @@ "Extracting Metabolites Values\n", "*****************************\n", "\n", - "* Extracting metabolite information from multiple ``mwTab`` files and outputing the first three metabolites:" + "* Extracting metabolite information from multiple ``mwTab`` files and outputting the first three metabolites:" ] }, { @@ -1159,7 +1158,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "* Extracting metabolite information from multiple ``mwTab`` files using regualar expressions and outputing the first three metabolites:" + "* Extracting metabolite information from multiple ``mwTab`` files using regular expressions and outputting the first three metabolites:" ] }, { @@ -1260,7 +1259,7 @@ "Many-to-many files conversions\n", "******************************\n", "\n", - "* Converting from the directory of ``mwTab`` formatted files into their equivalent\n", + "* Converting from a directory of ``mwTab`` formatted files into their equivalent\n", " ``JSON`` formatted files:" ] }, @@ -1285,7 +1284,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "* Converting from the directory of ``JSON`` formatted files into their equivalent\n", + "* Converting from a directory of ``JSON`` formatted files into their equivalent\n", " ``mwTab`` formatted files:" ] }, @@ -1345,11 +1344,11 @@ "Usage:\r\n", " mwtab -h | --help\r\n", " mwtab --version\r\n", - " mwtab convert ( ) [--from-format=] [--to-format=] [--validate] [--mw-rest=] [--verbose]\r\n", - " mwtab validate [--mw-rest=] [--verbose]\r\n", + " mwtab convert ( ) [--from-format=] [--to-format=] [--mw-rest=] [--verbose]\r\n", + " mwtab validate [--mw-rest=]\r\n", " mwtab download url [--to-path=] [--verbose]\r\n", - " mwtab download study all [--to-path=] [--input-item=] [--output-format=] [--mw-rest=] [--validate] [--verbose]\r\n", - " mwtab download study [--to-path=] [--input-item=] [--output-item=] [--output-format=] [--mw-rest=] [--validate] [--verbose]\r\n", + " mwtab download study all [--to-path=] [--input-item=] [--output-format=] [--mw-rest=] [--verbose]\r\n", + " mwtab download study [--to-path=] [--input-item=] [--output-item=] [--output-format=] [--mw-rest=] [--verbose]\r\n", " mwtab download (study | compound | refmet | gene | protein) [--output-format=] [--to-path=] [--mw-rest=] [--verbose]\r\n", " mwtab download moverz [--to-path=] [--mw-rest=] [--verbose]\r\n", " mwtab download exactmass [--to-path=] [--mw-rest=] [--verbose]\r\n", @@ -1357,26 +1356,32 @@ " mwtab extract metabolites ( ) ... [--to-format=] [--no-header]\r\n", "\r\n", "Options:\r\n", - " -h, --help Show this screen.\r\n", - " --version Show version.\r\n", - " --verbose Print what files are processing.\r\n", - " --validate Validate the mwTab file.\r\n", - " --from-format= Input file format, available formats: mwtab, json [default: mwtab].\r\n", - " --to-format= Output file format [default: json].\r\n", - " Available formats for convert:\r\n", - " mwtab, json.\r\n", - " Available formats for extract:\r\n", - " json, csv.\r\n", - " --mw-rest= URL to MW REST interface\r\n", - " [default: https://www.metabolomicsworkbench.org/rest/].\r\n", - " --context= Type of resource to access from MW REST interface, available contexts: study,\r\n", - " compound, refmet, gene, protein, moverz, exactmass [default: study].\r\n", - " --input-item= Item to search Metabolomics Workbench with.\r\n", - " --output-item= Item to be retrieved from Metabolomics Workbench.\r\n", - " --output-format= Format for item to be retrieved in, available formats: mwtab, json.\r\n", - " --no-header Include header at the top of csv formatted files.\r\n", + " -h, --help Show this screen.\r\n", + " --version Show version.\r\n", + " --verbose Print what files are processing.\r\n", + " --from-format= Input file format, available formats: mwtab, json [default: mwtab].\r\n", + " --to-format= Output file format [default: json].\r\n", + " Available formats for convert:\r\n", + " mwtab, json.\r\n", + " Available formats for extract:\r\n", + " json, csv.\r\n", + " --mw-rest= URL to MW REST interface\r\n", + " [default: https://www.metabolomicsworkbench.org/rest/].\r\n", + " --to-path= Directory to save outputs into. Defaults to the current working directory.\r\n", + " --prefix= Prefix to add at the beginning of the output file name. Defaults to no prefix.\r\n", + " --suffix= Suffix to add at the end of the output file name. Defaults to no suffix.\r\n", + " --context= Type of resource to access from MW REST interface, available contexts: study,\r\n", + " compound, refmet, gene, protein, moverz, exactmass [default: study].\r\n", + " --input-item= Item to search Metabolomics Workbench with.\r\n", + " --output-item= Item to be retrieved from Metabolomics Workbench.\r\n", + " --output-format= Format for item to be retrieved in, available formats: mwtab, json.\r\n", + " --no-header Include header at the top of csv formatted files.\r\n", + "\r\n", + " For extraction can take a \"-\" which will use stdout.\r\n", + " All 's can be single files, directories, or URLs.\r\n", "\r\n", - " For extraction can take a \"-\" which will use stdout.\r\n" + "Documentation webpage: https://moseleybioinformaticslab.github.io/mwtab/\r\n", + "GitHub webpage: https://github.com/MoseleyBioinformaticsLab/mwtab\r\n" ] } ], @@ -1390,7 +1395,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Converting ``mwTab`` files in bulk\n", + "Converting ``mwTab`` Files in Bulk\n", "----------------------------------\n", "\n", "CLI one-to-one file conversions\n", @@ -1607,19 +1612,25 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Download files through Metabolomics Workbenchs REST API\n", - "------------------------------------------------------\n", + "Download Files Through Metabolomics Workbench's REST API\n", + "--------------------------------------------------------\n", "\n", - "The :mod:`mwtab` package provides the :mod:`mwtab.mwrest` module, which contains a number of functions and classes for working with Metabolomics Workbenchs REST API.\n", + "The :mod:`mwtab` package provides the :mod:`mwtab.mwrest` module, which contains a number of functions and classes for working with Metabolomics Workbench's REST API.\n", "\n", ".. note::\n", - " For full official REST API specification see the following link (``MW REST API (v1.0, 5/7/2019)``):\n", + " For the full official REST API specification see the following link (``MW REST API (v1.0, 5/7/2019)``):\n", " https://www.metabolomicsworkbench.org/tools/MWRestAPIv1.0.pdf\n", "\n", + "\n", + ".. note::\n", + " We do not recommend downloading the JSON version of mwTab files directly from Metabolomics Workbench.\n", + " There are known errors in some of the JSON files they provide. We recommend downloading in mwTab format\n", + " and then using this package to convert them to JSON.\n", + "\n", "Download by URL\n", "***************\n", "\n", - "* To download a file based on a given url, simply call the ``download url`` command with the desired URL and provide an output path:" + "* To download a file based on a given URL, simply call the ``download url`` command with the desired URL and provide an output path:" ] }, { @@ -1637,7 +1648,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "* To download single analysis ``mwTab`` files, simply call ``download study`` and specifiy the analysis ID:" + "* To download a single analysis ``mwTab`` file, simply call ``download study`` and specify the analysis ID:" ] }, { @@ -1655,7 +1666,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "* To download an entire study ``mwTab`` file, simply call ``download study`` and specifiy the study ID:" + "* To download an entire study ``mwTab`` file, simply call ``download study`` and specify the study ID:" ] }, { @@ -1673,8 +1684,16 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - ".. note::\n", - " It is possible to validate downloaded files by adding the ``--validate`` option to the command line." + "* To download a list of studies and/or analyses, simply call ``download study`` and provide a list of IDs in a JSON file:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "! mwtab download study path_to_json.json --to-path=out/" ] }, { @@ -1683,10 +1702,10 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Download study, compound, refmet, gene, and protein files\n", + "Download study, compound, refmet, gene, and protein Files\n", "*********************************************************\n", "\n", - "* To download study, compound, refmet, gene, and protein context files, call the ``download`` command and specify the context, input iten, input value, and output item (optionally specifiy the output format).\n", + "* To download study, compound, refmet, gene, and protein context files, call the ``download`` command and specify the context, input item, input value, and output item (optionally specify the output format).\n", "\n", "* Download a study:" ] @@ -1778,10 +1797,10 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Download all ``mwTab`` formatted files\n", - "**********************************\n", + "Download All ``mwTab`` Formatted Files\n", + "**************************************\n", "\n", - "The :mod:`mwTab` package provides contains a number of command line functions for downloading Metabolomics ``mwtab`` formatted files through the Workbenchs REST API.\n", + "The :mod:`mwTab` package provides a number of command line functions for downloading Metabolomics ``mwtab`` formatted files through the Workbench's REST API.\n", "\n", "* To download all available analysis files, simply call the ``download study all`` command:\n", "\n", @@ -1843,12 +1862,12 @@ ".. note::\n", " It is not necessary to specify an output format for exactmass files.\n", "\n", - "Extracting metabolite data and metadata from ``mwTab`` files\n", + "Extracting Metabolite Data and Metadata From ``mwTab`` Files\n", "------------------------------------------------------------\n", "\n", "The :mod:`mwtab` package provides the :func:`~mwtab.mwextract.extract_metabolites` and :func:`~mwtab.mwextract.extract_metadata` functions that can parse ``mwTab`` formatted files. The :func:`~mwtab.mwextract.extract_metabolites` takes a source (list of ``mwTab`` file) and list of metadata key-value pairs that are used to search for ``mwTab`` files which contain the given metadata pairs. The :func:`~mwtab.mwextract.extract_metadata` takes a source (list of ``mwTab`` file) and list of metadata keys which are used to search the ``mwTab`` files for possible values to the given keys.\n", "\n", - "* To extract metabolite from ``mwTab`` files in a directory, call the ``extract metabolites`` command and provide a list of metadata key value pairs along with an output path and output format:" + "* To extract metabolites from ``mwTab`` files in a directory, call the ``extract metabolites`` command and provide a list of metadata key value pairs along with an output path and output format:" ] }, { @@ -1887,7 +1906,7 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Validating ``mwTab`` files\n", + "Validating ``mwTab`` Files\n", "--------------------------\n", "\n", "The :mod:`mwtab` package provides the :func:`~mwtab.validator.validate_file` function\n", @@ -1896,7 +1915,7 @@ "it lists the types of attributes (e.g. :py:class:`str` as well as specifies which keys are\n", "optional and which are required).\n", "\n", - "* To validate file(s), simply call the ``validate`` command and provide path to file(s):" + "* To validate file(s), simply call the ``validate`` command and provide a path to file(s):" ] }, { @@ -1914,20 +1933,20 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Using the mwtab Python Package to Find Analyses Involving a Specific Disease or Condition\n", + "Using the mwtab Package to Find Analyses Involving a Specific Disease or Condition\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "\n", - "The Metabolomics Workbench data repository stores mass spectroscopy and nuclear magnetic resonanse experimental data and metadata in ``mwTab`` formatted files. Metabolomics Workbench also provides a number of tools for searching or analyzing ``mwTab`` files. The mwtab Python package can also be used to perform similar functions through both a programmatic API and command-line interface, which has more search flexibility.\n", + "The Metabolomics Workbench data repository stores mass spectroscopy and nuclear magnetic resonance experimental data and metadata in ``mwTab`` formatted files. Metabolomics Workbench also provides a number of tools for searching or analyzing ``mwTab`` files. The mwtab Python package can also be used to perform similar functions through both a programmatic API and command-line interface, which has more search flexibility.\n", "\n", "In order to search the repository of ``mwTab`` files for analyses associated with a specific disease, Metabolomics Workbench provides a web-based interface:\n", " * https://www.metabolomicsworkbench.org/data/metsearch_MS_form2.php\n", "\n", "The mwtab Python package can be used in a number of ways to similar effect. The package provides the :meth:`~mwtab.mwextract.extract_metabolites()` method to extract and organize metabolites from multiple ``mwTab`` files through both Python scripts and a command-line interface. This method has more search flexibility, since it can take either a search string or a regular expression.\n", "\n", - "Using mwtab package API to extract study IDs, analysis IDs, and metabolites\n", - "---------------------------------------------------------------------------\n", + "Using the mwtab Package API to Extract Study IDs, Analysis IDs, and Metabolites\n", + "-------------------------------------------------------------------------------\n", "\n", - "The :meth:`~mwtab.mwextract.extract_metabolites()` method takes two parameters: 1) a iterable of :class:`~mwtab.mwtab.MWTabFile` instances and 2) an iterable of :class:`~mwtab.mwextract.ItemMatcher` or :class:`~mwtab.mwextract.ReGeXMatcher` instances. The iterable of :class:`~mwtab.mwtab.MWTabFile` instances can be created using byt passing ``mwTab`` file sources (filenames, analysis IDs, etc.) to the :meth:`~mwtab.fileio.read_files()` method. The iterable of matcher instances can be created using the :meth:`~mwtab.mwextract.generate_matchers()` method.\n", + "The :meth:`~mwtab.mwextract.extract_metabolites()` method takes two parameters: 1) an iterable of :class:`~mwtab.mwtab.MWTabFile` instances and 2) an iterable of :class:`~mwtab.mwextract.ItemMatcher` or :class:`~mwtab.mwextract.ReGeXMatcher` instances. The iterable of :class:`~mwtab.mwtab.MWTabFile` instances can be created by passing ``mwTab`` file sources (filenames, analysis IDs, etc.) to the :meth:`~mwtab.fileio.read_files()` method. The iterable of matcher instances can be created using the :meth:`~mwtab.mwextract.generate_matchers()` method.\n", "\n", "* An example of using the mwtab package API to extract data from analyses associated with diabetes and output the first three metabolites:" ] @@ -1968,8 +1987,8 @@ "raw_mimetype": "text/restructuredtext" }, "source": [ - "Using mwtab CLI to extract study IDs, analysis IDs, and metabolites\n", - "-------------------------------------------------------------------\n", + "Using the mwtab CLI to Extract Study IDs, Analysis IDs, and Metabolites\n", + "-----------------------------------------------------------------------\n", "\n", "The mwtab command line interface includes a ``mwtab extract metabolites`` method which takes a directory of ``mwTab`` files, an output path to save the extracted data in, and a series of ``mwTab`` section item keys and values to be matched (either string values or regular expressions). Additionally an output format can be specified.\n", "\n", @@ -1991,7 +2010,7 @@ "metadata": { "celltoolbar": "Raw Cell Format", "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2005,9 +2024,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.10.5" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/mwtab/cli.py b/mwtab/cli.py deleted file mode 100755 index ff48235..0000000 --- a/mwtab/cli.py +++ /dev/null @@ -1,322 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -The mwtab command-line interface -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Usage: - mwtab -h | --help - mwtab --version - mwtab convert ( ) [--from-format=] [--to-format=] [--validate] [--mw-rest=] [--verbose] - mwtab validate [--mw-rest=] [--verbose] - mwtab download url [--to-path=] [--verbose] - mwtab download study all [--to-path=] [--input-item=] [--output-format=] [--mw-rest=] [--validate] [--verbose] - mwtab download study [--to-path=] [--input-item=] [--output-item=] [--output-format=] [--mw-rest=] [--validate] [--verbose] - mwtab download (study | compound | refmet | gene | protein) [--output-format=] [--to-path=] [--mw-rest=] [--verbose] - mwtab download moverz [--to-path=] [--mw-rest=] [--verbose] - mwtab download exactmass [--to-path=] [--mw-rest=] [--verbose] - mwtab extract metadata ... [--to-format=] [--no-header] - mwtab extract metabolites ( ) ... [--to-format=] [--no-header] - -Options: - -h, --help Show this screen. - --version Show version. - --verbose Print what files are processing. - --validate Validate the mwTab file. - --from-format= Input file format, available formats: mwtab, json [default: mwtab]. - --to-format= Output file format [default: json]. - Available formats for convert: - mwtab, json. - Available formats for extract: - json, csv. - --mw-rest= URL to MW REST interface - [default: https://www.metabolomicsworkbench.org/rest/]. - --context= Type of resource to access from MW REST interface, available contexts: study, - compound, refmet, gene, protein, moverz, exactmass [default: study]. - --input-item= Item to search Metabolomics Workbench with. - --output-item= Item to be retrieved from Metabolomics Workbench. - --output-format= Format for item to be retrieved in, available formats: mwtab, json. - --no-header Include header at the top of csv formatted files. - - For extraction can take a "-" which will use stdout. -""" - -from . import fileio, mwextract, mwrest -from .converter import Converter -from .validator import validate_file -from .mwschema import section_schema_mapping - -from os import getcwd, makedirs, path -from os.path import join, isfile -from urllib.parse import quote_plus - -import json -import re - -# remove -import time -import datetime - - -OUTPUT_FORMATS = { - "txt": "txt", - "mwtab": "txt", - "json": "json", - None: None -} -VERBOSE = False - - -def check_filepath(filepath): - """Method for validating that a given path directory exits. If not, the directory is created. - - :param str filepath: File path string. - :return: None - :rtype: :py:obj:`None` - """ - if not path.exists(path.dirname(filepath)): - dirname = path.dirname(filepath) - if dirname: - makedirs(dirname) - - -def get_file_path(dir_path, filename, extension): - """Helper method for validating that the commandline arguments "--to-path" or _ are not "None". Returns the given - command argument if not none or creates a default file path from the given filename and the current working - directory. - - :param dir_path: Path to directory file is to be saved in. - :type dir_path: :py:class:`str` or :py:class:`None` - :param str filename: Filename processed file is to be saved as. - :param str extension: File extension. - :return: Complete file path. - :rtype: :py:class:`str` - """ - # check to see if given directory path is not None - dir_path = dir_path if dir_path else getcwd() - if path.splitext(dir_path)[1]: - return dir_path - extension = extension if extension else "txt" - return join(dir_path, ".".join([quote_plus(filename).replace(".", "_"), extension])) - - -def download(context, cmdparams): - """Method for creating Metabolomics Workbench REST URLs and requesting files based on given commandline arguments. - Retrieved data is then saved out as specified. - - :param str context: String indicating the type of data ("context") to be accessed from the Metabolomics Workbench. - :param dict cmdparams: Commandline arguments specifying data to be accessed from Metabolomics Workbench. - :return: None - :rtype: :py:obj:`None` - """ - try: - # TODO: Convert to using mwrest.generate_study_urls() method - # create and validate a callable URL to pull data from Metabolomics Workbench's REST API - mwresturl = mwrest.GenericMWURL({ - "context": context, - "input_item": cmdparams.get("") if cmdparams.get("") else "analysis_id", - "input_value": cmdparams[""], - "output_item": cmdparams.get("") if cmdparams.get("") else "mwtab", - "output_format": OUTPUT_FORMATS[cmdparams.get("--output-format")] if cmdparams.get("--output-format") else "txt", - }).url - mwrestfile = next(fileio.read_mwrest(mwresturl)) - - if mwrestfile.text: # if the text file isn't blank - with open(get_file_path( - cmdparams.get("--to-path"), - mwrestfile.source, - OUTPUT_FORMATS[cmdparams.get("--output-format")] - ), "w", encoding="utf-8") as fh: - mwrestfile.write(fh) - else: - print("BLANK FILE") - except Exception as e: - print(e) - - -def cli(cmdargs): - """Implements the command line interface. - - param dict cmdargs: dictionary of command line arguments. - """ - - VERBOSE = cmdargs["--verbose"] - fileio.VERBOSE = cmdargs["--verbose"] - fileio.MWREST = cmdargs["--mw-rest"] - mwrest.VERBOSE = cmdargs["--verbose"] - - # mwtab convert ... - if cmdargs["convert"]: - converter = Converter(from_path=cmdargs[""], - to_path=cmdargs[""], - from_format=cmdargs["--from-format"], - to_format=cmdargs["--to-format"], - validate=cmdargs["--validate"]) - converter.convert() - - # mwtab validate ... - elif cmdargs["validate"]: - for mwfile in fileio.read_files(cmdargs[""], validate=cmdargs["--validate"]): - validate_file( - mwtabfile=mwfile, - section_schema_mapping=section_schema_mapping, - verbose=cmdargs.get("--verbose") - ) - - # mwtab download ... - elif cmdargs["download"]: - - # mwtab download url ... - if cmdargs[""]: - mwrestfile = next(fileio.read_mwrest(cmdargs[""])) - with open(get_file_path( - cmdargs["--to-path"], - mwrestfile.source, - OUTPUT_FORMATS[cmdargs.get("--output-format")]), - "w", - encoding="utf-8" - ) as fh: - mwrestfile.write(fh) - - # mwtab download study ... - elif cmdargs["study"]: - - # mwtab download study all ... - if cmdargs["all"]: - # mwtab download study all ... - # mwtab download study all --input-item=analysis_id ... - # mwtab download study all --input-item=study_id ... - # TODO: mwtab download study all --input-item=project_id ... - if not cmdargs["--input-item"] or cmdargs["--input-item"] in ("analysis_id", "study_id"): - cmdargs[""] = cmdargs["--input-item"] - - id_list = list() - if not cmdargs["--input-item"] or cmdargs["--input-item"] == "analysis_id": - id_list = mwrest.analysis_ids() - elif cmdargs["--input-item"] == "study_id": - id_list = mwrest.study_ids() - - for count, input_id in enumerate(id_list): - if VERBOSE: - print("[{:4}/{:4}]".format(count+1, len(id_list)), input_id, datetime.datetime.now()) - cmdargs[""] = input_id - download("study", cmdargs) - time.sleep(3) - - else: - raise ValueError("Unknown \"--input-item\" {}".format(cmdargs["--input-item"])) - - # mwtab download study ... - elif cmdargs[""] and not cmdargs[""]: - if isfile(cmdargs[""]): - with open(cmdargs[""], "r") as fh: - id_list = json.loads(fh.read()) - - if VERBOSE: - print("Found {} Files to be Downloaded".format(len(id_list))) - for count, input_id in enumerate(id_list): - if VERBOSE: - print("[{:4}/{:4}]".format(count + 1, len(id_list)), input_id, datetime.datetime.now()) - cmdargs[""] = input_id - download("study", cmdargs) - time.sleep(3) - - else: - input_item = cmdargs.get("--input-item") - input_value = cmdargs[""] - if not input_item: - if input_value.isdigit(): - input_value = "AN{}".format(input_value.zfill(6)) - input_item = "analysis_id" - elif re.match(r'(AN[0-9]{6}$)', input_value): - input_item = "analysis_id" - elif re.match(r'(ST[0-9]{6}$)', input_value): - input_item = "study_id" - mwresturl = mwrest.GenericMWURL({ - "context": "study", - "input_item": input_item, - "input_value": input_value, - "output_item": cmdargs.get("--output-item") or "mwtab", - "output_format": cmdargs["--output-format"], - }, cmdargs["--mw-rest"]).url - mwrestfile = next(fileio.read_mwrest(mwresturl)) - with open(cmdargs["--to-path"] or join(getcwd(), - quote_plus(mwrestfile.source).replace(".", "_") + "." + cmdargs[ - "--output-format"]), - "w", encoding="utf-8") as fh: - mwrestfile.write(fh) - - # mwtab download (study | ...) ... - elif cmdargs[""]: - download("study", cmdargs) - - # mwtab download (... compound | refmet | gene | protein) ... - elif cmdargs["compound"]: - download("compound", cmdargs) - elif cmdargs["refmet"]: - download("refmet", cmdargs) - elif cmdargs["gene"]: - download("gene", cmdargs) - elif cmdargs["protein"]: - download("protein", cmdargs) - - # mwtab download moverz [--verbose] - elif cmdargs["moverz"]: - mwresturl = mwrest.GenericMWURL({ - "context": "moverz", - "input_item": cmdargs[""], - "m/z_value": cmdargs[""], - "ion_type_value": cmdargs[""], - "m/z_tolerance_value": cmdargs[""], - }).url - mwrestfile = next(fileio.read_mwrest(mwresturl)) - with open(cmdargs["--to-path"] or join(getcwd(), quote_plus(mwrestfile.source).replace(".", "_") + ".txt"), - "w") as fh: - mwrestfile.write(fh) - - # mwtab download exactmass [--verbose] - elif cmdargs["exactmass"]: - mwresturl = mwrest.GenericMWURL({ - "context": "exactmass", - "LIPID_abbreviation": cmdargs[""], - "ion_type_value": cmdargs[""], - }).url - mwrestfile = next(fileio.read_mwrest(mwresturl)) - with open(cmdargs["--to-path"] or join(getcwd(), quote_plus(mwrestfile.source).replace(".", "_") + ".txt"), - "w") as fh: - mwrestfile.write(fh) - - # mwtab extract ... - elif cmdargs["extract"]: - mwfile_generator = fileio.read_files(cmdargs[""]) - if cmdargs["metabolites"]: - metabolites_dict = mwextract.extract_metabolites( - mwfile_generator, - mwextract.generate_matchers( - [(cmdargs[""][i], - cmdargs[""][i] if not cmdargs[""][i][:2] == "r'" else re.compile(cmdargs[""][i][2:-1])) - for i in range(len(cmdargs[""]))] - ) - ) - - if cmdargs[""] != "-": - if cmdargs["--to-format"] == "csv": - mwextract.write_metabolites_csv(cmdargs[""], metabolites_dict, cmdargs["--no-header"]) - else: - mwextract.write_json(cmdargs[""], metabolites_dict) - else: - print(json.dumps(metabolites_dict, indent=4, cls=mwextract.SetEncoder)) - - elif cmdargs["metadata"]: - metadata = dict() - for mwtabfile in mwfile_generator: - extracted_values = mwextract.extract_metadata(mwtabfile, cmdargs[""]) - [metadata.setdefault(key, set()).update(val) for (key, val) in extracted_values.items()] - if cmdargs[""] != "-": - if cmdargs["--to-format"] == "csv": - mwextract.write_metadata_csv(cmdargs[""], metadata, cmdargs["--no-header"]) - else: - mwextract.write_json(cmdargs[""], metadata) - else: - print(metadata) diff --git a/mwtab/fileio.py b/mwtab/fileio.py deleted file mode 100755 index 66388d7..0000000 --- a/mwtab/fileio.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -mwtab.fileio -~~~~~~~~~~~~ - -This module provides routines for reading ``mwTab`` formatted files -from difference kinds of sources: - - * Single ``mwTab`` formatted file on a local machine. - * Directory containing multiple ``mwTab`` formatted files. - * Compressed zip/tar archive of ``mwTab`` formatted files. - * URL address of ``mwTab`` formatted file. - * ``ANALYSIS_ID`` of ``mwTab`` formatted file. -""" - -import os -import io -import zipfile -import tarfile -import bz2 -import gzip -from re import match - -from . import mwtab -from . import validator -from . import mwschema -from . import mwrest - -from urllib.request import urlopen -from urllib.parse import urlparse - - -VERBOSE = False - - -def _generate_filenames(sources): - """Generate filenames. - - :param tuple sources: Sequence of strings representing path to file(s). - :return: Path to file(s). - :rtype: :py:class:`str` - """ - for source in sources: - if os.path.isdir(source): - for path, _, filelist in os.walk(source): - for fname in filelist: - if os.path.splitext(fname)[1].lower() in {".csv", ".txt", ".json"}: - if GenericFilePath.is_compressed(fname): - if VERBOSE: - print("Skipping compressed file: {}".format(os.path.abspath(fname))) - continue - else: - yield os.path.join(path, fname) - - elif os.path.isfile(source): - yield source - - elif source.isdigit(): - yield next(mwrest.generate_mwtab_urls([source])) - - # TODO: Add ST parsing - elif match(r"(AN[0-9]{6}$)", source): - yield next(mwrest.generate_mwtab_urls([source])) - - elif GenericFilePath.is_url(source): - yield source - - else: - raise TypeError("Unknown file source.") - - -def _generate_handles(filenames): - """Open a sequence of filenames one at time producing file objects. - The file is closed immediately when proceeding to the next iteration. - - :param generator filenames: Generator object that yields the path to each file, one at a time. - :return: Filehandle to be processed into an instance. - """ - for fname in filenames: - path = GenericFilePath(fname) - for filehandle, source in path.open(): - yield filehandle, source - filehandle.close() - - -def read_files(*sources, **kwds): - """Construct a generator that yields file instances. - - :param sources: One or more strings representing path to file(s). - """ - filenames = _generate_filenames(sources) - filehandles = _generate_handles(filenames) - for fh, source in filehandles: - try: - f = mwtab.MWTabFile(source) - f.read(fh) - - if kwds.get('validate'): - validator.validate_file(mwtabfile=f, - section_schema_mapping=mwschema.section_schema_mapping) - - if VERBOSE: - print("Processed file: {}".format(os.path.abspath(source))) - - yield f - - except Exception as e: - if VERBOSE: - print("Error processing file: ", os.path.abspath(source), "\nReason:", e) - raise e - - -def read_mwrest(*sources, **kwds): - """Construct a generator that yields file instances. - - :param sources: One or more strings representing path to file(s). - """ - filenames = _generate_filenames(sources) - filehandles = _generate_handles(filenames) - for fh, source in filehandles: - try: - f = mwrest.MWRESTFile(source) - f.read(fh) - - if VERBOSE: - print("Processed url: {}".format(source)) - - yield f - - except Exception as e: - if VERBOSE: - print("Error processing url: ", source, "\nReason:", e) - pass - - -class GenericFilePath(object): - """`GenericFilePath` class knows how to open local files or files over URL.""" - - def __init__(self, path): - """Initialize path. - - :param str path: String representing a path to local file(s) or valid URL address of file(s). - """ - self.path = path - - def open(self): - """Generator that opens and yields filehandles using appropriate facilities: - test if path represents a local file or file over URL, if file is compressed - or not. - - :return: Filehandle to be processed into an instance. - """ - is_url = self.is_url(self.path) - compression_type = self.is_compressed(self.path) - - if not compression_type: - if is_url: - filehandle = urlopen(self.path) - else: - filehandle = open(self.path, "r", encoding="utf-8") - source = self.path - yield filehandle, source - filehandle.close() - - elif compression_type: - if is_url: - response = urlopen(self.path) - path = response.read() - response.close() - else: - path = self.path - - if compression_type == "zip": - ziparchive = zipfile.ZipFile(io.BytesIO(path), "r") if is_url else zipfile.ZipFile(path) - for name in ziparchive.infolist(): - if not name.filename.endswith("/"): - filehandle = ziparchive.open(name) - source = self.path + "/" + name.filename - yield filehandle, source - filehandle.close() - - elif compression_type in ("tar", "tar.bz2", "tar.gz"): - tararchive = tarfile.open(fileobj=io.BytesIO(path)) if is_url else tarfile.open(path) - for name in tararchive: - if name.isfile(): - filehandle = tararchive.extractfile(name) - source = self.path + "/" + name.name - yield filehandle, source - filehandle.close() - - elif compression_type == "bz2": - filehandle = bz2.BZ2File(io.BytesIO(path)) if is_url else bz2.BZ2File(path) - source = self.path - yield filehandle, source - filehandle.close() - - elif compression_type == "gz": - filehandle = gzip.open(io.BytesIO(path)) if is_url else gzip.open(path) - source = self.path - yield filehandle, source - filehandle.close() - - @staticmethod - def is_compressed(path): - """Test if path represents compressed file(s). - - :param str path: Path to file(s). - :return: String specifying compression type if compressed, "" otherwise. - :rtype: :py:class:`str` - """ - if path.endswith(".zip"): - return "zip" - elif path.endswith(".tar.gz"): - return "tar.gz" - elif path.endswith(".tar.bz2"): - return "tar.bz2" - elif path.endswith(".gz"): - return "gz" - elif path.endswith(".bz2"): - return "bz2" - elif path.endswith(".tar"): - return "tar" - return "" - - @staticmethod - def is_url(path): - """Test if path represents a valid URL. - - :param str path: Path to file. - :return: True if path is valid url string, False otherwise. - :rtype: :py:obj:`True` or :py:obj:`False` - """ - try: - parse_result = urlparse(path) - return all((parse_result.scheme, parse_result.netloc, parse_result.path)) - except ValueError: - return False diff --git a/mwtab/mwschema.py b/mwtab/mwschema.py deleted file mode 100755 index 736cdb9..0000000 --- a/mwtab/mwschema.py +++ /dev/null @@ -1,446 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -mwtab.mwschema -~~~~~~~~~~~~~~ - -This module provides schema definitions for different sections of the -``mwTab`` Metabolomics Workbench format. -""" - -import sys - -from schema import Schema, Optional, Or - -if sys.version_info.major == 2: - str = unicode - - -metabolomics_workbench_schema = Schema( - { - "VERSION": str, - "CREATED_ON": str, - Optional("STUDY_ID"): str, - Optional("ANALYSIS_ID"): str, - Optional("PROJECT_ID"): str, - Optional("HEADER"): str, - Optional("DATATRACK_ID"): str - } -) - -project_schema = Schema( - { - "PROJECT_TITLE": str, - Optional("PROJECT_TYPE"): str, - "PROJECT_SUMMARY": str, - "INSTITUTE": str, - Optional("DEPARTMENT"): str, - Optional("LABORATORY"): str, - "LAST_NAME": str, - "FIRST_NAME": str, - "ADDRESS": str, - "EMAIL": str, - "PHONE": str, - Optional("FUNDING_SOURCE"): str, - Optional("PROJECT_COMMENTS"): str, - Optional("PUBLICATIONS"): str, - Optional("CONTRIBUTORS"): str, - Optional("DOI"): str - } -) - -study_schema = Schema( - { - "STUDY_TITLE": str, - Optional("STUDY_TYPE"): str, - "STUDY_SUMMARY": str, - "INSTITUTE": str, - Optional("DEPARTMENT"): str, - Optional("LABORATORY"): str, - "LAST_NAME": str, - "FIRST_NAME": str, - "ADDRESS": str, - "EMAIL": str, - "PHONE": str, - Optional("NUM_GROUPS"): str, - Optional("TOTAL_SUBJECTS"): str, - Optional("NUM_MALES"): str, - Optional("NUM_FEMALES"): str, - Optional("STUDY_COMMENTS"): str, - Optional("PUBLICATIONS"): str, - Optional("SUBMIT_DATE"): str # assumed - } -) - -subject_schema = Schema( - { - "SUBJECT_TYPE": str, - "SUBJECT_SPECIES": str, - Optional("TAXONOMY_ID"): str, - Optional("GENOTYPE_STRAIN"): str, - Optional("AGE_OR_AGE_RANGE"): str, - Optional("WEIGHT_OR_WEIGHT_RANGE"): str, - Optional("HEIGHT_OR_HEIGHT_RANGE"): str, - Optional("GENDER"): str, - Optional("HUMAN_RACE"): str, - Optional("HUMAN_ETHNICITY"): str, - Optional("HUMAN_TRIAL_TYPE"): str, - Optional("HUMAN_LIFESTYLE_FACTORS"): str, - Optional("HUMAN_MEDICATIONS"): str, - Optional("HUMAN_PRESCRIPTION_OTC"): str, - Optional("HUMAN_SMOKING_STATUS"): str, - Optional("HUMAN_ALCOHOL_DRUG_USE"): str, - Optional("HUMAN_NUTRITION"): str, - Optional("HUMAN_INCLUSION_CRITERIA"): str, - Optional("HUMAN_EXCLUSION_CRITERIA"): str, - Optional("ANIMAL_ANIMAL_SUPPLIER"): str, - Optional("ANIMAL_HOUSING"): str, - Optional("ANIMAL_LIGHT_CYCLE"): str, - Optional("ANIMAL_FEED"): str, - Optional("ANIMAL_WATER"): str, - Optional("ANIMAL_INCLUSION_CRITERIA"): str, - Optional("CELL_BIOSOURCE_OR_SUPPLIER"): str, - Optional("CELL_STRAIN_DETAILS"): str, - Optional("SUBJECT_COMMENTS"): str, - Optional("CELL_PRIMARY_IMMORTALIZED"): str, - Optional("CELL_PASSAGE_NUMBER"): str, - Optional("CELL_COUNTS"): str, - Optional("SPECIES_GROUP"): str - } -) - -subject_sample_factors_schema = Schema( - [ - { - "Subject ID": str, - "Sample ID": str, - "Factors": dict, - Optional("Additional sample data"): { - Optional("RAW_FILE_NAME"): str, - Optional(str): str - } - } - ] -) - -collection_schema = Schema( - { - "COLLECTION_SUMMARY": str, - Optional("COLLECTION_PROTOCOL_ID"): str, - Optional("COLLECTION_PROTOCOL_FILENAME"): str, - Optional("COLLECTION_PROTOCOL_COMMENTS"): str, - Optional("SAMPLE_TYPE"): str, # required as of mwTab file format specification 1.5 - Optional("COLLECTION_METHOD"): str, - Optional("COLLECTION_LOCATION"): str, - Optional("COLLECTION_FREQUENCY"): str, - Optional("COLLECTION_DURATION"): str, - Optional("COLLECTION_TIME"): str, - Optional("VOLUMEORAMOUNT_COLLECTED"): str, - Optional("STORAGE_CONDITIONS"): str, - Optional("COLLECTION_VIALS"): str, - Optional("STORAGE_VIALS"): str, - Optional("COLLECTION_TUBE_TEMP"): str, - Optional("ADDITIVES"): str, - Optional("BLOOD_SERUM_OR_PLASMA"): str, - Optional("TISSUE_CELL_IDENTIFICATION"): str, - Optional("TISSUE_CELL_QUANTITY_TAKEN"): str - } -) - -treatment_schema = Schema( - { - "TREATMENT_SUMMARY": str, - Optional("TREATMENT_PROTOCOL_ID"): str, - Optional("TREATMENT_PROTOCOL_FILENAME"): str, - Optional("TREATMENT_PROTOCOL_COMMENTS"): str, - Optional("TREATMENT"): str, - Optional("TREATMENT_COMPOUND"): str, - Optional("TREATMENT_ROUTE"): str, - Optional("TREATMENT_DOSE"): str, - Optional("TREATMENT_DOSEVOLUME"): str, - Optional("TREATMENT_DOSEDURATION"): str, - Optional("TREATMENT_VEHICLE"): str, - Optional("ANIMAL_VET_TREATMENTS"): str, - Optional("ANIMAL_ANESTHESIA"): str, - Optional("ANIMAL_ACCLIMATION_DURATION"): str, - Optional("ANIMAL_FASTING"): str, - Optional("ANIMAL_ENDP_EUTHANASIA"): str, - Optional("ANIMAL_ENDP_TISSUE_COLL_LIST"): str, - Optional("ANIMAL_ENDP_TISSUE_PROC_METHOD"): str, - Optional("ANIMAL_ENDP_CLINICAL_SIGNS"): str, - Optional("HUMAN_FASTING"): str, - Optional("HUMAN_ENDP_CLINICAL_SIGNS"): str, - Optional("CELL_STORAGE"): str, - Optional("CELL_GROWTH_CONTAINER"): str, - Optional("CELL_GROWTH_CONFIG"): str, - Optional("CELL_GROWTH_RATE"): str, - Optional("CELL_INOC_PROC"): str, - Optional("CELL_MEDIA"): str, - Optional("CELL_ENVIR_COND"): str, - Optional("CELL_HARVESTING"): str, - Optional("PLANT_GROWTH_SUPPORT"): str, - Optional("PLANT_GROWTH_LOCATION"): str, - Optional("PLANT_PLOT_DESIGN"): str, - Optional("PLANT_LIGHT_PERIOD"): str, - Optional("PLANT_HUMIDITY"): str, - Optional("PLANT_TEMP"): str, - Optional("PLANT_WATERING_REGIME"): str, - Optional("PLANT_NUTRITIONAL_REGIME"): str, - Optional("PLANT_ESTAB_DATE"): str, - Optional("PLANT_HARVEST_DATE"): str, - Optional("PLANT_GROWTH_STAGE"): str, - Optional("PLANT_METAB_QUENCH_METHOD"): str, - Optional("PLANT_HARVEST_METHOD"): str, - Optional("PLANT_STORAGE"): str, - Optional("CELL_PCT_CONFLUENCE"): str, - Optional("CELL_MEDIA_LASTCHANGED"): str - } -) - -sampleprep_schema = Schema( - { - "SAMPLEPREP_SUMMARY": str, - Optional("SAMPLEPREP_PROTOCOL_ID"): str, - Optional("SAMPLEPREP_PROTOCOL_FILENAME"): str, - Optional("SAMPLEPREP_PROTOCOL_COMMENTS"): str, - Optional("PROCESSING_METHOD"): str, - Optional("PROCESSING_STORAGE_CONDITIONS"): str, - Optional("EXTRACTION_METHOD"): str, - Optional("EXTRACT_CONCENTRATION_DILUTION"): str, - Optional("EXTRACT_ENRICHMENT"): str, - Optional("EXTRACT_CLEANUP"): str, - Optional("EXTRACT_STORAGE"): str, - Optional("SAMPLE_RESUSPENSION"): str, - Optional("SAMPLE_DERIVATIZATION"): str, - Optional("SAMPLE_SPIKING"): str, - Optional("ORGAN"): str, - Optional("ORGAN_SPECIFICATION"): str, - Optional("CELL_TYPE"): str, - Optional("SUBCELLULAR_LOCATION"): str - } -) - -chromatography_schema = Schema( - { - Optional("CHROMATOGRAPHY_SUMMARY"): str, - "CHROMATOGRAPHY_TYPE": str, - "INSTRUMENT_NAME": str, - "COLUMN_NAME": str, - Optional("FLOW_GRADIENT"): str, - Optional("FLOW_RATE"): str, - Optional("COLUMN_TEMPERATURE"): str, - Optional("METHODS_FILENAME"): str, - Optional("SOLVENT_A"): str, - Optional("SOLVENT_B"): str, - Optional("METHODS_ID"): str, - Optional("COLUMN_PRESSURE"): str, - Optional("INJECTION_TEMPERATURE"): str, - Optional("INTERNAL_STANDARD"): str, - Optional("INTERNAL_STANDARD_MT"): str, - Optional("RETENTION_INDEX"): str, - Optional("RETENTION_TIME"): str, - Optional("SAMPLE_INJECTION"): str, - Optional("SAMPLING_CONE"): str, - Optional("ANALYTICAL_TIME"): str, - Optional("CAPILLARY_VOLTAGE"): str, - Optional("MIGRATION_TIME"): str, - Optional("OVEN_TEMPERATURE"): str, - Optional("PRECONDITIONING"): str, - Optional("RUNNING_BUFFER"): str, - Optional("RUNNING_VOLTAGE"): str, - Optional("SHEATH_LIQUID"): str, - Optional("TIME_PROGRAM"): str, - Optional("TRANSFERLINE_TEMPERATURE"): str, - Optional("WASHING_BUFFER"): str, - Optional("WEAK_WASH_SOLVENT_NAME"): str, - Optional("WEAK_WASH_VOLUME"): str, - Optional("STRONG_WASH_SOLVENT_NAME"): str, - Optional("STRONG_WASH_VOLUME"): str, - Optional("TARGET_SAMPLE_TEMPERATURE"): str, - Optional("SAMPLE_LOOP_SIZE"): str, - Optional("SAMPLE_SYRINGE_SIZE"): str, - Optional("RANDOMIZATION_ORDER"): str, - Optional("CHROMATOGRAPHY_COMMENTS"): str - } -) - -analysis_schema = Schema( - { - "ANALYSIS_TYPE": str, - Optional("LABORATORY_NAME"): str, - Optional("OPERATOR_NAME"): str, - Optional("DETECTOR_TYPE"): str, - Optional("SOFTWARE_VERSION"): str, - Optional("ACQUISITION_DATE"): str, - Optional("ANALYSIS_PROTOCOL_FILE"): str, - Optional("ACQUISITION_PARAMETERS_FILE"): str, - Optional("PROCESSING_PARAMETERS_FILE"): str, - Optional("DATA_FORMAT"): str, - - # not specified in mwTab specification (assumed) - Optional("ACQUISITION_ID"): str, - Optional("ACQUISITION_TIME"): str, - Optional("ANALYSIS_COMMENTS"): str, - Optional("ANALYSIS_DISPLAY"): str, - Optional("INSTRUMENT_NAME"): str, - Optional("INSTRUMENT_PARAMETERS_FILE"): str, - Optional("NUM_FACTORS"): str, - Optional("NUM_METABOLITES"): str, - Optional("PROCESSED_FILE"): str, - Optional("RANDOMIZATION_ORDER"): str, - Optional("RAW_FILE"): str, - } -) - -ms_schema = Schema( - { - "INSTRUMENT_NAME": str, - "INSTRUMENT_TYPE": str, - "MS_TYPE": str, - "ION_MODE": str, - Optional("MS_COMMENTS"): str, # changed to optional mwTab File Format Spec. 1.5 - Optional("CAPILLARY_TEMPERATURE"): str, - Optional("CAPILLARY_VOLTAGE"): str, - Optional("COLLISION_ENERGY"): str, - Optional("COLLISION_GAS"): str, - Optional("DRY_GAS_FLOW"): str, - Optional("DRY_GAS_TEMP"): str, - Optional("FRAGMENT_VOLTAGE"): str, - Optional("FRAGMENTATION_METHOD"): str, - Optional("GAS_PRESSURE"): str, - Optional("HELIUM_FLOW"): str, - Optional("ION_SOURCE_TEMPERATURE"): str, - Optional("ION_SPRAY_VOLTAGE"): str, - Optional("IONIZATION"): str, - Optional("IONIZATION_ENERGY"): str, - Optional("IONIZATION_POTENTIAL"): str, - Optional("MASS_ACCURACY"): str, - Optional("PRECURSOR_TYPE"): str, - Optional("REAGENT_GAS"): str, - Optional("SOURCE_TEMPERATURE"): str, - Optional("SPRAY_VOLTAGE"): str, - Optional("ACTIVATION_PARAMETER"): str, - Optional("ACTIVATION_TIME"): str, - Optional("ATOM_GUN_CURRENT"): str, - Optional("AUTOMATIC_GAIN_CONTROL"): str, - Optional("BOMBARDMENT"): str, - Optional("CDL_SIDE_OCTOPOLES_BIAS_VOLTAGE"): str, - Optional("CDL_TEMPERATURE"): str, - Optional("DATAFORMAT"): str, - Optional("DESOLVATION_GAS_FLOW"): str, - Optional("DESOLVATION_TEMPERATURE"): str, - Optional("INTERFACE_VOLTAGE"): str, - Optional("IT_SIDE_OCTOPOLES_BIAS_VOLTAGE"): str, - Optional("LASER"): str, - Optional("MATRIX"): str, - Optional("NEBULIZER"): str, - Optional("OCTPOLE_VOLTAGE"): str, - Optional("PROBE_TIP"): str, - Optional("RESOLUTION_SETTING"): str, - Optional("SAMPLE_DRIPPING"): str, - Optional("SCAN_RANGE_MOVERZ"): str, - Optional("SCANNING"): str, - Optional("SCANNING_CYCLE"): str, - Optional("SCANNING_RANGE"): str, - Optional("SKIMMER_VOLTAGE"): str, - Optional("TUBE_LENS_VOLTAGE"): str, - Optional("MS_RESULTS_FILE"): Or(str, dict) - } -) - -nmr_schema = Schema( - { - "INSTRUMENT_NAME": str, - "INSTRUMENT_TYPE": str, - "NMR_EXPERIMENT_TYPE": str, - Optional("NMR_COMMENTS"): str, - Optional("FIELD_FREQUENCY_LOCK"): str, - Optional("STANDARD_CONCENTRATION"): str, - "SPECTROMETER_FREQUENCY": str, - Optional("NMR_PROBE"): str, - Optional("NMR_SOLVENT"): str, - Optional("NMR_TUBE_SIZE"): str, - Optional("SHIMMING_METHOD"): str, - Optional("PULSE_SEQUENCE"): str, - Optional("WATER_SUPPRESSION"): str, - Optional("PULSE_WIDTH"): str, - Optional("POWER_LEVEL"): str, - Optional("RECEIVER_GAIN"): str, - Optional("OFFSET_FREQUENCY"): str, - Optional("PRESATURATION_POWER_LEVEL"): str, - Optional("CHEMICAL_SHIFT_REF_CPD"): str, - Optional("TEMPERATURE"): str, - Optional("NUMBER_OF_SCANS"): str, - Optional("DUMMY_SCANS"): str, - Optional("ACQUISITION_TIME"): str, - Optional("RELAXATION_DELAY"): str, - Optional("SPECTRAL_WIDTH"): str, - Optional("NUM_DATA_POINTS_ACQUIRED"): str, - Optional("REAL_DATA_POINTS"): str, - Optional("LINE_BROADENING"): str, - Optional("ZERO_FILLING"): str, - Optional("APODIZATION"): str, - Optional("BASELINE_CORRECTION_METHOD"): str, - Optional("CHEMICAL_SHIFT_REF_STD"): str, - Optional("BINNED_INCREMENT"): str, - Optional("BINNED_DATA_NORMALIZATION_METHOD"): str, - Optional("BINNED_DATA_PROTOCOL_FILE"): str, - Optional("BINNED_DATA_CHEMICAL_SHIFT_RANGE"): str, - Optional("BINNED_DATA_EXCLUDED_RANGE"): str, - Optional("NMR_RESULTS_FILE"): Or(str, dict) # add mwTab File Format Spec. 1.5 - } -) - -data_schema = Schema( - [ - { - Or("Metabolite", "Bin range(ppm)", only_one=True): str, - Optional(str): str, - }, - ] -) - -extended_schema = Schema( - [ - { - "Metabolite": str, - Optional(str): str, - "sample_id": str - }, - ] -) - -ms_metabolite_data_schema = Schema( - { - "Units": str, - "Data": data_schema, - "Metabolites": data_schema, - Optional("Extended"): extended_schema - } -) - -nmr_binned_data_schema = Schema( - { - "Units": str, - "Data": data_schema - } -) - -section_schema_mapping = { - "METABOLOMICS WORKBENCH": metabolomics_workbench_schema, - "PROJECT": project_schema, - "STUDY": study_schema, - "ANALYSIS": analysis_schema, - "SUBJECT": subject_schema, - "SUBJECT_SAMPLE_FACTORS": subject_sample_factors_schema, - "COLLECTION": collection_schema, - "TREATMENT": treatment_schema, - "SAMPLEPREP": sampleprep_schema, - "CHROMATOGRAPHY": chromatography_schema, - "MS": ms_schema, - "NM": nmr_schema, - "MS_METABOLITE_DATA": ms_metabolite_data_schema, - "NMR_METABOLITE_DATA": ms_metabolite_data_schema, - "NMR_BINNED_DATA": nmr_binned_data_schema, -} diff --git a/mwtab/mwtab.py b/mwtab/mwtab.py deleted file mode 100755 index 0695d56..0000000 --- a/mwtab/mwtab.py +++ /dev/null @@ -1,452 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -mwtab.mwtab -~~~~~~~~~~~ - -This module provides the :class:`~mwtab.mwtab.MWTabFile` class -that stores the data from a single ``mwTab`` formatted file in the -form of an :py:class:`~collections.OrderedDict`. Data can be accessed -directly from the :class:`~mwtab.mwtab.MWTabFile` instance using -bracket accessors. - -The data is divided into a series of "sections" which each contain a -number of "key-value"-like pairs. Also, the file contains a specially -formatted ``SUBJECT_SAMPLE_FACTOR`` block and blocks of data between -``*_START`` and ``*_END``. -""" - -from __future__ import print_function, division, unicode_literals -from collections import OrderedDict -import io -import sys -import json -from .tokenizer import tokenizer - - -class MWTabFile(OrderedDict): - """MWTabFile class that stores data from a single ``mwTab`` formatted file in - the form of :py:class:`collections.OrderedDict`. - """ - - prefixes = { - "METABOLOMICS WORKBENCH": "", - "PROJECT": "PR:", - "STUDY": "ST:", - "SUBJECT": "SU:", - "SUBJECT_SAMPLE_FACTORS": "", - "COLLECTION": "CO:", - "TREATMENT": "TR:", - "SAMPLEPREP": "SP:", - "CHROMATOGRAPHY": "CH:", - "ANALYSIS": "AN:", - "MS": "MS:", - "NMR": "NM:", - "MS_METABOLITE_DATA": "", - "NMR_BINNED_DATA": "", - "METABOLITES": "" - } - - def __init__(self, source, *args, **kwds): - """File initializer. - - :param str source: Source a `MWTabFile` instance was created from. - """ - super(MWTabFile, self).__init__(*args, **kwds) - self.source = source - self.study_id = "" - self.analysis_id = "" - self.header = "" - - def read(self, filehandle): - """Read data into a :class:`~mwtab.mwtab.MWTabFile` instance. - - :param filehandle: file-like object. - :type filehandle: :py:class:`io.TextIOWrapper`, :py:class:`gzip.GzipFile`, - :py:class:`bz2.BZ2File`, :py:class:`zipfile.ZipFile` - :return: None - :rtype: :py:obj:`None` - """ - input_str = filehandle.read() - - if not input_str: - raise ValueError("Blank input string retrieved from source.") - - mwtab_str = self._is_mwtab(input_str) - json_str = self._is_json(input_str) - - if not input_str: - pass - elif json_str: - self.update(json_str) - elif mwtab_str: - self._build_mwtabfile(mwtab_str) - else: - raise TypeError("Unknown file format") - - try: - self.study_id = self["METABOLOMICS WORKBENCH"].get("STUDY_ID") - self.analysis_id = self["METABOLOMICS WORKBENCH"].get("ANALYSIS_ID") - # self.header = self["METABOLOMICS WORKBENCH"].get("HEADER") - self.header = " ".join( - ["#METABOLOMICS WORKBENCH"] - + [item[0] + ":" + item[1] for item in self["METABOLOMICS WORKBENCH"].items() if item[0] not in ["VERSION", "CREATED_ON"]] - ) - except KeyError as e: - raise KeyError("File missing header information \"METABOLOMICS WORKBENCH\"", e) - - filehandle.close() - - def write(self, filehandle, file_format): - """Write :class:`~mwtab.mwtab.MWTabFile` data into file. - - :param filehandle: file-like object. - :type filehandle: :py:class:`io.TextIOWrapper` - :param str file_format: Format to use to write data: `mwtab` or `json`. - :return: None - :rtype: :py:obj:`None` - """ - try: - if file_format == "json": - json_str = self._to_json() - filehandle.write(json_str) - elif file_format == "mwtab": - mwtab_str = self._to_mwtab() - filehandle.write(mwtab_str) - else: - raise TypeError("Unknown file format.") - except IOError: - raise IOError('"filehandle" parameter must be writable.') - filehandle.close() - - def writestr(self, file_format): - """Write :class:`~mwtab.mwtab.MWTabFile` data into string. - - :param str file_format: Format to use to write data: `mwtab` or `json`. - :return: String representing the :class:`~mwtab.mwtab.MWTabFile` instance. - :rtype: :py:class:`str` - """ - if file_format == "json": - json_str = self._to_json() - return json_str - elif file_format == "mwtab": - mwtab_str = self._to_mwtab() - return mwtab_str - else: - raise TypeError("Unknown file format.") - - def _build_mwtabfile(self, mwtab_str): - """Build :class:`~mwtab.mwtab.MWTabFile` instance. - - :param mwtab_str: String in `mwtab` format. - :type mwtab_str: :py:class:`str` or :py:class:`bytes` - :return: instance of :class:`~mwtab.mwtab.MWTabFile`. - :rtype: :class:`~mwtab.mwtab.MWTabFile` - """ - mwtab_file = self - lexer = tokenizer(mwtab_str) - token = next(lexer) - - while token.key != "!#ENDFILE": - if token.key.startswith("#"): - name = token.key[1:] - section = self._build_block(lexer) - if section: - if name == "METABOLITES": - data_section = next((n for n in mwtab_file.keys() if n in ("MS_METABOLITE_DATA", "NMR_METABOLITE_DATA", "NMR_BINNED_DATA")), None) - if data_section: - for key in section.keys(): - mwtab_file[data_section][key] = section[key] - elif name == "NMR": - mwtab_file["NM"] = section - else: - mwtab_file[name] = section - token = next(lexer) - return mwtab_file - - def _build_block(self, lexer): - """Build individual text block of :class:`~mwtab.mwtab.MWTabFile` instance. - - :param lexer: instance of the mwtab tokenizer. - :type lexer: :func:`~mwtab.tokenizer.tokenizer` - :return: Section dictionary. - :rtype: :py:class:`collections.OrderedDict` - """ - section = OrderedDict() - token = next(lexer) - alias = { - "subject_type": "Subject ID", - "local_sample_id": "Sample ID", - "factors": "Factors", - "additional_sample_data": "Additional sample data", - } - - while not token.key.startswith("#ENDSECTION"): - - # TODO: Move to separate method (no longer works the same way as the other possibilities in _build_block()) - if token.key.startswith("SUBJECT_SAMPLE_FACTORS"): - if type(section) != list: - section = list() - # sample_dict = OrderedDict({alias[token._fields[x]]: token[x] for x in range(1, len(token._fields))}) - # if not sample_dict.get("Additional sample data"): - # del sample_dict["Additional sample data"] - section.append(token.value) - - elif token.key.endswith("_START"): - data = [] - - token = next(lexer) - header = list(token.value) - - while not token.key.endswith("_END"): - if token.key in ("Samples", "Factors", "metabolite_name", "Bin range(ppm)"): - pass - else: - data.append(OrderedDict(zip(["Metabolite"] + header[1:], token.value))) - - token = next(lexer) - - if token.key.startswith("METABOLITES"): - section["Metabolites"] = data - elif token.key.startswith("EXTENDED_"): - section["Extended"] = data - else: - section["Data"] = data - - elif token.key.endswith("_RESULTS_FILE"): - if len(token) > 2: - key, value, extra = token - section[key] = OrderedDict([(key, value)]) - for pair in extra: - section[key].update((pair,)) - else: - key, value = token - section[key] = value - - else: - key, value, = token - if key in section: - section[key] += " {}".format(value) - else: - section[key] = value - - # load token(s) (from parsing of next line in file) - token = next(lexer) - - return section - - def print_file(self, f=sys.stdout, file_format="mwtab"): - """Print :class:`~mwtab.mwtab.MWTabFile` into a file or stdout. - - :param f: writable file-like stream. - :type f: :py:class:`io.StringIO` - :param str file_format: Format to use: `mwtab` or `json`. - :return: None - :rtype: :py:obj:`None` - """ - if file_format == "mwtab": - for key in self: - if key == "SUBJECT_SAMPLE_FACTORS": - print("#SUBJECT_SAMPLE_FACTORS: \tSUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data", file=f) - self.print_subject_sample_factors(key, f=f, file_format=file_format) - else: - if key == "METABOLOMICS WORKBENCH": - print(self.header, file=f) - elif key == "NM": - print("#NMR", file=f) - else: - print("#{}".format(key), file=f) - - self.print_block(key, f=f, file_format=file_format) - print("#END", file=f) - - elif file_format == "json": - print(self._to_json(), file=f) - - def print_subject_sample_factors(self, section_key, f=sys.stdout, file_format="mwtab"): - """Print `mwtab` `SUBJECT_SAMPLE_FACTORS` section into a file or stdout. - - :param str section_key: Section name. - :param f: writable file-like stream. - :type f: :py:class:`io.StringIO` - :param str file_format: Format to use: `mwtab` or `json`. - :return: None - :rtype: :py:obj:`None` - """ - if file_format == "mwtab": - for item in self[section_key]: - formatted_items = [] - for k in item.keys(): - if k in ["Subject ID", "Sample ID"]: - formatted_items.append(str(item[k])) - elif k == "Factors": - factors = [] - for k2 in item[k]: - factors.append("{}:{}".format(k2, item[k][k2])) - formatted_items.append(" | ".join(factors)) - elif k == "Additional sample data": - additional_sample_data = [] - for k2 in item[k]: - additional_sample_data.append("{}={}".format(k2, item[k][k2])) - formatted_items.append(";".join(additional_sample_data)) - line = "{}{}\t{}".format(section_key, 33 * " ", "\t".join(formatted_items)) - # for file missing "Additional sample data" items - if len(formatted_items) < 4: - line += "\t" - else: - print(line, file=f) - - def print_block(self, section_key, f=sys.stdout, file_format="mwtab"): - """Print `mwtab` section into a file or stdout. - - :param str section_key: Section name. - :param f: writable file-like stream. - :type f: :py:class:`io.StringIO` - :param str file_format: Format to use: `mwtab` or `json`. - :return: None - :rtype: :py:obj:`None` - """ - if file_format == "mwtab": - for key, value in self[section_key].items(): - if section_key == "METABOLOMICS WORKBENCH" and key not in ("VERSION", "CREATED_ON"): - continue - - if key in ("VERSION", "CREATED_ON"): - cw = 20 - len(key) - elif key == "Units": - cw = 33 - len(section_key+":UNITS") - else: - cw = 30 - len(key) - - if key.endswith("_RESULTS_FILE"): - if isinstance(value, dict): - print("{}{} \t{}\t{}:{}".format(self.prefixes.get(section_key, ""), - *[i for pair in value.items() for i in pair]), file=f) - else: - print("{}{}{}\t{}".format(self.prefixes.get(section_key, ""), key, cw * " ", value), file=f) - - # prints #MS_METABOLITE_DATA, #NMR_METABOLITE_DATA, or #NMR_BINNED_DATA sections - elif key == "Units": - print("{}:UNITS{}\t{}".format(section_key, cw * " ", value), file=f) - elif key == "Data": - print("{}_START".format(section_key), file=f) - - if "METABOLITE" in section_key: - # prints "Samples" line at head of data section - print("\t".join(["Samples"] + [k for k in self[section_key][key][0].keys()][1:]), file=f) - # prints "Factors" line at head of data section - factors_list = ["Factors"] - factors_dict = {i["Sample ID"]: i["Factors"] for i in self["SUBJECT_SAMPLE_FACTORS"]} - for k in [k for k in self[section_key][key][0].keys()][1:]: - factors = [fk + ":" + factors_dict[k][fk] for fk in factors_dict[k].keys()] - factors_list.append(" | ".join(factors)) - print("\t".join(factors_list), file=f) - for i in self[section_key][key]: - print("\t".join([i[k] for k in i.keys()]), file=f) - - else: # NMR_BINNED_DATA - print("\t".join(["Bin range(ppm)"] + [k for k in self[section_key][key][0].keys()][1:]), file=f) - for i in self[section_key][key]: - print("\t".join([i[k] for k in i.keys()]), file=f) - - print("{}_END".format(section_key), file=f) - - # prints #METABOLITES section - elif key in ("Metabolites", "Extended"): - if key == "Metabolites": - print("#METABOLITES", file=f) - print("METABOLITES_START", file=f) - else: - print("EXTENDED_{}_START".format(section_key), file=f) - - print("\t".join(["metabolite_name"] + [k for k in self[section_key][key][0].keys()][1:]), file=f) - for i in self[section_key][key]: - print("\t".join(i[k] for k in i.keys()), file=f) - - if key == "Metabolites": - print("METABOLITES_END", file=f) - else: - print("EXTENDED_{}_END".format(section_key), file=f) - - else: - if len(str(value)) > 80: - words = str(value).split(" ") - length = 0 - line = list() - for word in words: - if length + len(word) + len(line) - 1 <= 80: - line.append(word) - length += len(word) - else: - print("{}{}{}\t{}".format(self.prefixes.get(section_key, ""), key, cw * " ", " ".join(line)), file=f) - line = [word] - length = len(word) - print("{}{}{}\t{}".format(self.prefixes.get(section_key, ""), key, cw * " ", " ".join(line)), - file=f) - else: - print("{}{}{}\t{}".format(self.prefixes.get(section_key, ""), key, cw * " ", value), file=f) - - elif file_format == "json": - print(json.dumps(self[section_key], sort_keys=False, indent=4), file=f) - - def _to_json(self): - """Save :class:`~mwtab.mwtab.MWTabFile` into JSON string. - - :return: JSON string. - :rtype: :py:class:`str` - """ - return json.dumps(self, sort_keys=False, indent=4) - - def _to_mwtab(self): - """Save :class:`~mwtab.mwtab.MWTabFile` in `mwtab` formatted string. - - :return: NMR-STAR string. - :rtype: :py:class:`str` - """ - mwtab_str = io.StringIO() - self.print_file(mwtab_str) - return mwtab_str.getvalue() - - @staticmethod - def _is_mwtab(string): - """Test if input string is in `mwtab` format. - - :param string: Input string. - :type string: :py:class:`str` or :py:class:`bytes` - :return: Input string if in mwTab format or False otherwise. - :rtype: :py:class:`str` or :py:obj:`False` - """ - if isinstance(string, str): - lines = string.replace("\r", "\n").split("\n") - elif isinstance(string, bytes): - lines = string.decode("utf-8").replace("\r", "\n").split("\n") - else: - raise TypeError("Expecting or , but {} was passed".format(type(string))) - - lines = [line for line in lines if line] - header = lines[0] - - if header.startswith("#METABOLOMICS WORKBENCH"): - return "\n".join(lines) - return False - - @staticmethod - def _is_json(string): - """Test if input string is in JSON format. - - :param string: Input string. - :type string: :py:class:`str` or :py:class:`bytes` - :return: Input string if in JSON format or False otherwise. - :rtype: :py:class:`str` or :py:obj:`False` - """ - try: - if isinstance(string, bytes): - json_str = json.loads(string.decode("utf-8"), object_pairs_hook=OrderedDict) - elif isinstance(string, str): - json_str = json.loads(string, object_pairs_hook=OrderedDict) - else: - raise TypeError("Expecting or , but {} was passed".format(type(string))) - return json_str - except ValueError: - return False diff --git a/mwtab/tokenizer.py b/mwtab/tokenizer.py deleted file mode 100755 index 650350c..0000000 --- a/mwtab/tokenizer.py +++ /dev/null @@ -1,116 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -mwtab.tokenizer -~~~~~~~~~~~~~~~ - -This module provides the :func:`~mwtab.tokenizer.tokenizer` lexical analyzer for -`mwTab` format syntax. It is implemented as Python generator-based state -machine which generates (yields) tokens one at a time when :py:func:`next()` -is invoked on :func:`~mwtab.tokenizer.tokenizer` instance. - -Each token is a tuple of "key-value"-like pairs, tuple of -``SUBJECT_SAMPLE_FACTORS`` or tuple of data deposited between -``*_START`` and ``*_END`` blocks. -""" - -from __future__ import print_function, division, unicode_literals -from collections import deque, namedtuple, OrderedDict - - -KeyValue = namedtuple("KeyValue", ["key", "value"]) -KeyValueExtra = namedtuple("KeyValueExtra", ["key", "value", "extra"]) - - -def tokenizer(text): - """A lexical analyzer for the `mwtab` formatted files. - - :param text: `mwTab` formatted text. - :type text: py:class:`str` - :return: Tuples of data. - :rtype: py:class:`~collections.namedtuple` - """ - stream = deque(text.split("\n")) - - while len(stream) > 0: - line = stream.popleft() - try: - - # header - if line.startswith("#METABOLOMICS WORKBENCH"): - yield KeyValue("#METABOLOMICS WORKBENCH", "\n") - for identifier in line.split(" "): - if ":" in identifier: - key, value = identifier.split(":") - yield KeyValue(key, value) - - # SUBJECT_SAMPLE_FACTORS header (reached new section) - elif line.startswith("#SUBJECT_SAMPLE_FACTORS:"): - yield KeyValue("#ENDSECTION", "\n") - yield KeyValue("#SUBJECT_SAMPLE_FACTORS", "\n") - - # section header (reached new section) - elif line.startswith("#"): - yield KeyValue("#ENDSECTION", "\n") - yield KeyValue(line.strip(), "\n") - - # SUBJECT_SAMPLE_FACTORS line - elif line.startswith("SUBJECT_SAMPLE_FACTORS"): - line_items = line.split("\t") - subject_sample_factors_dict = OrderedDict({ - "Subject ID": line_items[1], - "Sample ID": line_items[2], - "Factors": {factor_item.split(":")[0].strip(): factor_item.split(":")[1].strip() for factor_item in - line_items[3].split("|")} - }) - if line_items[4]: - subject_sample_factors_dict["Additional sample data"] = { - factor_item.split("=")[0].strip(): factor_item.split("=")[1].strip() for factor_item in line_items[4].split(";") - } - yield KeyValue(line_items[0].strip(), subject_sample_factors_dict) - - # data start header - elif line.endswith("_START"): - yield KeyValue(line, "\n") - - # tokenize lines in data section till line ending with "_END" is reached - while not line.endswith("_END"): - line = stream.popleft() - if line.endswith("_END"): - yield KeyValue(line.strip(), "\n") - else: - data = line.split("\t") - yield KeyValue(data[0], tuple(data)) - - # item line in item section (e.g. PROJECT, SUBJECT, etc..) - elif line: - if "_RESULTS_FILE" in line: - line_items = line.split("\t") - # if len(line_items) > 2: - # extra_items = list() - # for extra_item in line_items[2:]: - # k, v = extra_item.split(":") - # extra_items.append(tuple([k.strip(), v.strip()])) - # yield KeyValueExtra(line_items[0].strip()[3:], line_items[1], extra_items) - # else: - # yield KeyValue(line_items[0].strip()[3:], line_items[1]) - yield KeyValue(line_items[0].strip()[3:], " ".join(line_items[1:])) - else: - key, value = line.split("\t") - if ":" in key: - if ":UNITS" in key: - yield KeyValue("Units", value) - else: - yield KeyValue(key.strip()[3:], value) - else: - yield KeyValue(key.strip(), value) - - except IndexError as e: - raise IndexError("LINE WITH ERROR:\n\t", repr(line), e) - except ValueError as e: - raise ValueError("LINE WITH ERROR:\n\t", repr(line), e) - - # end of file - yield KeyValue("#ENDSECTION", "\n") - yield KeyValue("!#ENDFILE", "\n") # This is to ensure that tokenizer terminates when #END is missing. diff --git a/mwtab/validator.py b/mwtab/validator.py deleted file mode 100755 index fe185bc..0000000 --- a/mwtab/validator.py +++ /dev/null @@ -1,336 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -""" -mwtab.validator -~~~~~~~~~~~~~~~ - -This module contains routines to validate consistency of the ``mwTab`` -formatted files, e.g. make sure that ``Samples`` and ``Factors`` -identifiers are consistent across the file, make sure that all -required key-value pairs are present. -""" - -from copy import deepcopy -from collections import OrderedDict -from datetime import datetime -from .mwschema import section_schema_mapping -from re import match -import io -import sys -import mwtab - - -VERBOSE = False -LOG = None - -METABOLITES_REGEXS = { - "hmdb_id": { - r"(?i)[\s|\S]{,}(HMDB)", - r"(?i)(Human Metabolome D)[\S]{,}", - }, - "inchi_key": { - r"(?i)(inchi)[\S]{,}", - }, - "kegg_id": { - r"(?i)(kegg)$", - r"(?i)(kegg)(\s|_)(i)", - }, - "moverz": { - r"(?i)(m/z)", - }, - "moverz_quant": { - r"(?i)(moverz)(\s|_)(quant)", - r"(?i)(quan)[\S]{,}(\s|_)(m)[\S]{,}(z)", - }, - "other_id": { - r"(?i)(other)(\s|_)(id)$", - }, - "other_id_type": { - r"(?i)(other)(\s|_)(id)(\s|_)(type)$", - }, - "pubchem_id": { - r"(?i)(pubchem)[\S]{,}", - }, - "retention_index": { - r"(?i)(ri)$", - r"(?i)(ret)[\s|\S]{,}(index)", - }, - "retention_index_type": { - r"(?i)(ri)(\s|_)(type)", - }, - "retention_time": { - r"(?i)(r)[\s|\S]{,}(time)[\S]{,}", - }, -} - -ITEM_SECTIONS = { - "METABOLOMICS WORKBENCH", - "PROJECT", - "STUDY", - "ANALYSIS", - "SUBJECT", - "COLLECTION", - "TREATMENT", - "SAMPLEPREP", - "CHROMATOGRAPHY", - "MS", - "NM", -} - -VALIDATION_LOG_HEADER = \ -"""Validation Log -{} -mwtab Python Library Version: {} -Source: {} -Study ID: {} -Analysis ID: {} -File format: {}""" - - -def validate_subject_samples_factors(mwtabfile): - """Validate ``SUBJECT_SAMPLE_FACTORS`` section. - - :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. - :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` or - :py:class:`collections.OrderedDict` - """ - subject_samples_factors_errors = list() - - for index, subject_sample_factor in enumerate(mwtabfile["SUBJECT_SAMPLE_FACTORS"]): - if not subject_sample_factor["Subject ID"]: - subject_samples_factors_errors.append( - "SUBJECT_SAMPLE_FACTORS: Entry #{} missing Subject ID.".format(index+1) - ) - if not subject_sample_factor["Sample ID"]: - subject_samples_factors_errors.append( - "SUBJECT_SAMPLE_FACTORS: Entry #{} missing Sample ID.".format(index + 1) - ) - if subject_sample_factor.get("Factors"): - for factor_key in subject_sample_factor["Factors"]: - if not subject_sample_factor["Factors"][factor_key]: - subject_samples_factors_errors.append( - "SUBJECT_SAMPLE_FACTORS: Entry #{} missing value for Factor {}.".format(index + 1, factor_key) - ) - if subject_sample_factor.get("Additional sample data"): - for additional_key in subject_sample_factor["Additional sample data"]: - if not subject_sample_factor["Additional sample data"][additional_key]: - subject_samples_factors_errors.append( - "SUBJECT_SAMPLE_FACTORS: Entry #{} missing value for Additional sample data {}.".format( - index + 1, additional_key - ) - ) - - return subject_samples_factors_errors - - -def validate_data(mwtabfile, data_section_key, null_values): - """Validates ``MS_METABOLITE_DATA``, ``NMR_METABOLITE_DATA``, and ``NMR_BINNED_DATA`` sections. - - :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. - :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` or - :py:class:`collections.OrderedDict` - :param data_section_key: Section key (either MS_METABOLITE_DATA, NMR_METABOLITE_DATA, or NMR_BINNED_DATA) - :type data_section_key: :py:class:`str` - :param bool null_values: whether null values are present. - """ - data_errors = list() - - subject_sample_factors_sample_id_set = {subject_sample_factor["Sample ID"] for subject_sample_factor in mwtabfile["SUBJECT_SAMPLE_FACTORS"]} - data_sample_id_set = set(list(mwtabfile[data_section_key]["Data"][0].keys())[1:]) - - # Removed for mwTab File Spec. 1.5 - # if subject_sample_factors_sample_id_set - data_sample_id_set: - # data_errors.append("{}: Section missing data entry for sample(s): {}.".format( - # data_section_key, - # subject_sample_factors_sample_id_set - data_sample_id_set - # )) - if data_sample_id_set - subject_sample_factors_sample_id_set: - data_errors.append("SUBJECT_SAMPLE_FACTORS: Section missing sample ID(s) {} found in {} section.".format( - data_sample_id_set - subject_sample_factors_sample_id_set, - data_section_key - )) - - for index, metabolite in enumerate(mwtabfile[data_section_key]["Data"]): - # if set(list(metabolite.keys())[1:]) != subject_sample_factors_sample_id_set: - # print(len(subject_sample_factors_sample_id_set), len(metabolite) - 1) - # print( - # "{}: Metabolite \"{}\" missing data entry for {} samples".format( - # data_section_key, - # metabolite[list(metabolite.keys())[0]], - # len(subject_sample_factors_sample_id_set - set(list(metabolite.keys())[1:])) - # ), - # file=error_stream - # ) - if null_values: - for data_point_key in metabolite.keys(): - if data_point_key != "Metabolite": - try: - float(metabolite[data_point_key]) - except ValueError as e: - metabolite[data_point_key] = "" - data_errors.append( - "{}: Data entry #{} contains non-numeric value converted to \"\".".format(data_section_key, index + 1)) - - return data_errors - - -def validate_metabolites(mwtabfile, data_section_key): - """Validate ``METABOLITES`` section. - - :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. - :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` or - :py:class:`collections.OrderedDict` - :param data_section_key: Section key (either MS_METABOLITE_DATA, NMR_METABOLITE_DATA, or NMR_BINNED_DATA) - :type data_section_key: :py:class:`str` - """ - metabolites_errors = list() - - for index, metabolite in enumerate(mwtabfile[data_section_key]["Metabolites"]): - for field_key in list(metabolite.keys())[1:]: - if not any(k == field_key for k in METABOLITES_REGEXS.keys()): - for regex_key in METABOLITES_REGEXS.keys(): - if any(match(p, field_key) for p in METABOLITES_REGEXS[regex_key]): - metabolites_errors.append("METABOLITES: Data entry #{} contains field name \"{}\" which matches a commonly used field name \"{}\".".format(index + 1, field_key, regex_key)) - field_key = regex_key - break - - return metabolites_errors - - -def validate_extended(mwtabfile, data_section_key): - """Validate ``EXTENDED_MS_METABOLITE_DATA``, ``EXTENDED_NMR_METABOLITE_DATA``, and ``EXTENDED_NMR_BINNED_DATA`` sections. - - :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. - :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` or - :py:class:`collections.OrderedDict` - :param data_section_key: Section key (either MS_METABOLITE_DATA, NMR_METABOLITE_DATA, or NMR_BINNED_DATA) - :type data_section_key: :py:class:`str` - """ - extended_errors = list() - - sample_id_set = {subject_sample_factor["Sample ID"] for subject_sample_factor in - mwtabfile["SUBJECT_SAMPLE_FACTORS"]} - - for index, extended_data in enumerate(mwtabfile[data_section_key]["Extended"]): - if "sample_id" not in extended_data.keys(): - extended_errors.append("EXTENDED_{}: Data entry #{} missing Sample ID.".format(data_section_key, index + 1)) - elif not extended_data["sample_id"] in sample_id_set: - extended_errors.append( - "EXTENDED_{}: Data entry #{} contains Sample ID \"{}\" not found in SUBJECT_SAMPLE_FACTORS section.".format( - data_section_key, index + 1, extended_data["sample_id"] - )) - - return extended_errors - - -def validate_section_schema(section, schema, section_key): - """Validate section of ``mwTab`` formatted file. - - :param section: Section of :class:`~mwtab.mwtab.MWTabFile`. - :type section: :py:class:`collections.OrderedDict` - :param schema: Schema definition. - :type schema: :py:class:`~schema.schema` - :param str section_key: Section key. - - :return: Validated section. - :rtype: :py:class:`collections.OrderedDict` - """ - schema_errors = list() - - if section_key in ITEM_SECTIONS: - for key in section.keys(): - if not section[key]: - schema_errors.append("{}: Contains item \"{}\" with null value.".format(section_key, key)) - del section[key] - - return schema.validate(section), schema_errors - - -def validate_file(mwtabfile, section_schema_mapping=section_schema_mapping, verbose=False, metabolites=True): - """Validate ``mwTab`` formatted file. - - :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. - :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` or - :py:class:`collections.OrderedDict` - :param dict section_schema_mapping: Dictionary that provides mapping between section name and schema definition. - :param bool verbose: whether to be verbose or not. - :param bool metabolites: whether to validate metabolites section. - :return: Validated file. - :rtype: :py:class:`collections.OrderedDict` - """ - # setup - if not verbose: - error_stout = io.StringIO() - else: - error_stout = sys.stdout - validated_mwtabfile = deepcopy(OrderedDict(mwtabfile)) - - # generate validation log header(s) - file_format = mwtabfile.source.split("/")[-1] if "https://www.metabolomicsworkbench.org/" in mwtabfile.source else \ - mwtabfile.source.split(".")[1] - print(VALIDATION_LOG_HEADER.format( - str(datetime.now()), - mwtab.__version__, - mwtabfile.source, - mwtabfile.study_id, - mwtabfile.analysis_id, - file_format - ), file=error_stout) - - # create list to collect validation errors - errors = list() - - # validate PROJECT, STUDY, ANALYSIS... and Schemas - for section_key, section in mwtabfile.items(): - try: - schema = section_schema_mapping[section_key] - # section = validate_section_schema(section, schema, section_key, error_stout) - section, schema_errors = validate_section_schema(section, schema, section_key) - errors.extend(schema_errors) - validated_mwtabfile[section_key] = section - except Exception as e: - errors.append("SCHEMA: Section \"{}\" does not match the allowed schema. ".format(section_key) + str(e)) - - # validate SUBJECT_SAMPLE_FACTORS - # validate_subject_samples_factors(validated_mwtabfile, error_stout) - errors.extend(validate_subject_samples_factors(validated_mwtabfile)) - - # validate ..._DATA sections - data_section_key = list(set(validated_mwtabfile.keys()) & - {"MS_METABOLITE_DATA", "NMR_METABOLITE_DATA", "NMR_BINNED_DATA"}) - if data_section_key: - data_section_key = data_section_key[0] - # validate_data(validated_mwtabfile, data_section_key, error_stout, False) - errors.extend(validate_data(validated_mwtabfile, data_section_key, False)) - - if data_section_key in ("MS_METABOLITE_DATA", "NMR_METABOLITE_DATA"): - # temp for testing - if metabolites: - if "Metabolites" in validated_mwtabfile[data_section_key].keys(): - errors.extend(validate_metabolites(validated_mwtabfile, data_section_key)) - else: - errors.append("DATA: Missing METABOLITES section.") - if "Extended" in validated_mwtabfile[data_section_key].keys(): - errors.extend(validate_extended(validated_mwtabfile, data_section_key)) - - else: - if "MS" in validated_mwtabfile.keys(): - if not validated_mwtabfile["MS"].get("MS_RESULTS_FILE"): - errors.append("DATA: Missing MS_METABOLITE_DATA section or MS_RESULTS_FILE item in MS section.") - elif "NM" in validated_mwtabfile.keys(): - if not validated_mwtabfile['NM'].get('NMR_RESULTS_FILE'): - errors.append("DATA: Missing either NMR_METABOLITE_DATA or NMR_BINNED_DATA section or NMR_RESULTS_FILE item in NM secction.") - - # finish writing validation/error log - if errors: - print("Status: Contains Validation Errors", file=error_stout) - print("Number Errors: {}\n".format(len(errors)), file=error_stout) - print("Error Log:\n" + "\n".join(errors), file=error_stout) - else: - print("Status: Passing", file=error_stout) - - if verbose: - return validated_mwtabfile, None - else: - return validated_mwtabfile, error_stout.getvalue() diff --git a/setup.py b/old_setup.py old mode 100755 new mode 100644 similarity index 100% rename from setup.py rename to old_setup.py diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..ba76228 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,44 @@ +[build-system] +requires = ["setuptools", "wheel", "setuptools_scm[toml]>=6.2"] +build-backend = "setuptools.build_meta" + +[project] +name = "mwtab" +description = "Parser for mwtab files from the Metabolomics Workbench" +readme = "README.rst" +requires-python = ">=3.10" +keywords = ["mwtab", "metabolomics workbench"] +license = {file = "LICENSE"} +classifiers = [ + 'Environment :: Console', + 'Intended Audience :: Developers', + 'Intended Audience :: Science/Research', + 'License :: OSI Approved :: BSD License', + 'Operating System :: OS Independent', + 'Programming Language :: Python :: 3.10', + 'Programming Language :: Python :: 3.11', + 'Programming Language :: Python :: 3.12', + 'Programming Language :: Python :: 3.13', + 'Programming Language :: Python :: 3.14', + 'Topic :: Scientific/Engineering :: Bio-Informatics', + 'Topic :: Software Development :: Libraries :: Python Modules', +] +dynamic = ["version", "dependencies"] + +[project.urls] +"Homepage" = "https://github.com/MoseleyBioinformaticsLab/mwtab" +"Documentation" = "https://moseleybioinformaticslab.github.io/mwtab/" +"GitHub" = "https://github.com/MoseleyBioinformaticsLab/mwtab" +"Issues" = "https://github.com/MoseleyBioinformaticsLab/mwtab/issues" + +[tool.setuptools.dynamic] +dependencies = {file = "requirements.txt"} + +[project.scripts] +mwtab = "mwtab.__main__:main" + +[tool.setuptools_scm] +write_to = "src/mwtab/_version.py" + +[tool.coverage.run] +patch = ["subprocess"] \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index fd00bb9..7c8eedb 100755 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,5 @@ docopt >= 0.6.2 -schema >= 0.6.6 \ No newline at end of file +jsonschema >= 4.18.6 +pandas >= 2.2.3 +setuptools_scm >= 7.0.5 +pyarrow>=19.0.0 \ No newline at end of file diff --git a/mwtab/__init__.py b/src/mwtab/__init__.py old mode 100755 new mode 100644 similarity index 62% rename from mwtab/__init__.py rename to src/mwtab/__init__.py index c24918b..630c27e --- a/mwtab/__init__.py +++ b/src/mwtab/__init__.py @@ -41,16 +41,38 @@ python dictionary representation of a Metabolomics Workbench REST URL. The class is used to validate query parameters and to generate a URL path which can be used to request data from Metabolomics Workbench through their REST API. + +``metadata_column_matching`` + This module provides the :class:`~mwtab.metadata_column_matching.ColumnFinder` class + which is composed of a :class:`~mwtab.metadata_column_matching.NameMatcher` and + :class:`~mwtab.metadata_column_matching.ValueMatcher`. They are used to match + column names and values, respecitively. Matching is done using regular expressions + the "in" operator, equality operator, and type matching for values. This module + also includes the "column_finders" dicitonary which is a dictionary of ColumnFinders + created to match the most common columns found in the Metabolomics Workbench datasets. + More information about this module can be found on the :doc:`metadata_column_matching` page. """ - from logging import getLogger, NullHandler from .fileio import read_files, read_mwrest from .validator import validate_file from .mwrest import GenericMWURL - -__version__ = "1.2.5.post1" +# __version__ = "1.2.5.post1" # Setting default logging handler getLogger(__name__).addHandler(NullHandler()) + + +try: + # -- Distribution mode -- + # import from _version.py generated by setuptools_scm during release + from ._version import version as __version__ +except ImportError: + # -- Source mode -- + # use setuptools_scm to get the current version from src using git + from setuptools_scm import get_version as _gv + from os import path as _path + __version__ = _gv(_path.join(_path.dirname(__file__), _path.pardir)) + + diff --git a/mwtab/__main__.py b/src/mwtab/__main__.py old mode 100755 new mode 100644 similarity index 63% rename from mwtab/__main__.py rename to src/mwtab/__main__.py index 4957695..262a6a0 --- a/mwtab/__main__.py +++ b/src/mwtab/__main__.py @@ -8,7 +8,9 @@ def main(): - + doc = [line for line in cli.__doc__.split('\n')] + doc = doc[:3] + [line.lstrip() for line in doc[5:]] + doc = '\n'.join(doc) args = docopt.docopt(cli.__doc__, version=__version__) cli.cli(args) diff --git a/src/mwtab/cli.py b/src/mwtab/cli.py new file mode 100644 index 0000000..82435f3 --- /dev/null +++ b/src/mwtab/cli.py @@ -0,0 +1,432 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# Note that this docstring has Sphinx directives in it. Those are removed in __main__ before passing to docopt. +""" +The mwtab Command Line Interface +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code-block:: text + + Usage: + mwtab -h | --help + mwtab --version + mwtab convert ( ) [--from-format=] [--to-format=] [--mw-rest=] [--force] [--verbose] + mwtab validate [--to-path=] [--mw-rest=] [--force] [--silent] + mwtab download url [--to-path=] [--verbose] + mwtab download study all [--to-path=] [--input-item=] [--output-format=] [--mw-rest=] [--verbose] + mwtab download study [--to-path=] [--input-item=] [--output-item=] [--output-format=] [--mw-rest=] [--verbose] + mwtab download (study | compound | refmet | gene | protein) [--output-format=] [--to-path=] [--mw-rest=] [--verbose] + mwtab download moverz [--to-path=] [--mw-rest=] [--verbose] + mwtab download exactmass [--to-path=] [--mw-rest=] [--verbose] + mwtab extract metadata ... [--to-format=] [--no-header] [--force] + mwtab extract metabolites ( ) ... [--to-format=] [--no-header] [--force] + + Options: + -h, --help Show this screen. + --version Show version. + --verbose Print what files are processing. + --silent Silence all standard output. + --from-format= Input file format, available formats: mwtab, json [default: mwtab]. + --to-format= Output file format [default: json]. + Available formats for convert: + mwtab, json. + Available formats for extract: + json, csv. + --mw-rest= URL to MW REST interface + [default: https://www.metabolomicsworkbench.org/rest/]. + --to-path= Directory to save outputs into. Defaults to the current working directory. + For the validate command, if the given path ends in '.json', then + all JSON file outputs will be condensed into that 1 file. Also for + the validate command no output files are saved unless this option is given. + --prefix= Prefix to add at the beginning of the output file name. Defaults to no prefix. + --suffix= Suffix to add at the end of the output file name. Defaults to no suffix. + --context= Type of resource to access from MW REST interface, available contexts: study, + compound, refmet, gene, protein, moverz, exactmass [default: study]. + --input-item= Item to search Metabolomics Workbench with. + --output-item= Item to be retrieved from Metabolomics Workbench. + --output-format= Format for item to be retrieved in, available formats: mwtab, json. + --no-header Include header at the top of csv formatted files. + --force Ignore non-dictionary values in METABOLITES_DATA, METABOLITES, and EXTENDED tables for JSON files. + + For extraction can take a "-" which will use stdout. + All 's can be single files, directories, or URLs. + + Documentation webpage: https://moseleybioinformaticslab.github.io/mwtab/ + GitHub webpage: https://github.com/MoseleyBioinformaticsLab/mwtab +""" + +from os import getcwd +from os.path import join, isfile +from urllib.parse import quote_plus +import traceback +import json +import re +import sys +import time +import datetime +import pathlib + +from . import fileio, mwextract, mwrest +from .converter import Converter +from .validator import validate_file +from .mwschema import ms_required_schema, nmr_required_schema +from .mwtab import MWTabFile + + +OUTPUT_FORMATS = { + "txt": "txt", + "mwtab": "txt", + "json": "json", + None: None +} +VERBOSE = False +# Note that 'url' is not a context for the Metabolomics Workbench REST API, but the code works better to have it in this list. +CONTEXTS = ['study', 'compound', 'refmet', 'gene', 'protein', 'moverz', 'exactmass', 'url'] + + + +def download(rest_params: dict, mwrest_base_url: str = mwrest.BASE_URL, full_url: str|None = None) -> mwrest.MWRESTFile: + """Create Metabolomics Workbench REST URL and request file. + + Args: + rets_params: A dictionary with values corresponding to the keywords in the Metabolomics Workbench REST specification. + For instance would correspond to 'context' and would correspond to 'input_item'. + mwrest_base_url: String for the base URL to use for accessing the Metabolomics Workbench REST interface. + full_url: String representing a fully constructed URL to a Metabolomics Workbench REST endpoint. + If given, all other parameters are ignored and this URL is used to download. + + Returns: + The downloaded file as an MWRESTFile object if no exceptions occured, otherwise None with a raised exception. + """ + if full_url: + mwrestfile = next(fileio.read_mwrest(full_url)) + else: + # create and validate a callable URL to pull data from Metabolomics Workbench's REST API + fileurl = mwrest.GenericMWURL(rest_params, mwrest_base_url).url + mwrestfile = next(fileio.read_mwrest(fileurl)) + + return mwrestfile + + +def save_mwrest_file(mwrestfile: mwrest.MWRESTFile, to_path: str|None = None, output_format: str = 'txt') -> bool: + """Save the given MWRESTFile object to the given path if it has text. + + Args: + mwrestfile: The MWRESTFile object to save. + to_path: The path to save the file to. Defaults to the current working directory. + output_format: The format to save the file to. Should be 'txt', 'json', or 'mwtab'. + + Returns: + True if the mwrestfile had text and was therefore saved, False otherwise. + """ + if mwrestfile.text: # if the text file isn't blank + filename = quote_plus(mwrestfile.source).replace(".", "_") + extension = OUTPUT_FORMATS[output_format] + if to_path: + fileio._create_save_path(to_path) + if pathlib.Path(to_path).suffix: + path_to_save = to_path + else: + path_to_save = join(to_path, filename + "." + extension) + else: + path_to_save = join(getcwd(), filename + "." + extension) + + with open(path_to_save, "w", encoding="utf-8") as fh: + mwrestfile.write(fh) + return True + return False + + +def download_and_save_mwrest_file(rest_params: dict, to_path: str|None = None, + mwrest_base_url: str = mwrest.BASE_URL, full_url: str|None = None) -> None: + """DRY function to combine downloading, saving, and error printing.""" + mwrestfile = download(rest_params, mwrest_base_url, full_url) + extension = output_format if (output_format := rest_params.get('output_format')) else 'txt' + if not save_mwrest_file(mwrestfile, to_path, extension): + value = full_url if full_url else rest_params['input_value'] + print(f'When trying to download a file for the value, "{value}", ' + 'a blank file or an error was returned, so no file was created for it.') + + +def download_and_save_ID_list(rest_params: dict, id_list: list[tuple[str, str]], verbose: bool, + to_path: str|None = None, + mwrest_base_url: str = mwrest.BASE_URL, full_url: str|None = None) -> None: + """Download and save a list of study and/or analysis IDs. + + Args: + rest_params: A dictionary with values corresponding to the keywords in the Metabolomics Workbench REST specification. + For instance would correspond to 'context' and would correspond to 'input_item'. + id_list: A list of tuples that are pairs of IDs and their classification. + verbose: If True, print more information about each ID being downloaded. + to_path: The directory path to save the files to. Defaults to the current working directory. + mwrest_base_url: String for the base URL to use for accessing the Metabolomics Workbench REST interface. + full_url: String representing a fully constructed URL to a Metabolomics Workbench REST endpoint. + If given, all other parameters are ignored and this URL is used to download. + """ + for count, (input_id, input_item) in enumerate(id_list): + rest_params['input_value'] = input_id + rest_params['input_item'] = input_item + if verbose: + print("[{:4}/{:4}]".format(count+1, len(id_list)), input_id, datetime.datetime.now()) + try: + download_and_save_mwrest_file(rest_params, to_path, mwrest_base_url, full_url) + except Exception: + print("Something went wrong and " + input_id + " could not be downloaded.") + traceback.print_exc(file=sys.stdout) + print() + time.sleep(3) + + +def classify_input_value(input_value: str) -> tuple[str, str]: + """Classify input_value as either 'analysis_id' or 'study_id'. + + If input_value is just a number, such as 000001, then 'AN' is added + to the front in the return value. The default classification is 'analysis_id'. + + Args: + input_value: A string that should be a study ID or analysis ID. + + Returns: + A tuple where the first element is the input_value, possibly modified, + and the second element is the classification, either 'study_id' or 'analysis_id'. + """ + if input_value.isdigit(): + input_value = "AN{}".format(input_value.zfill(6)) + input_item = "analysis_id" + elif re.match(r'(AN[0-9]{6}$)', input_value): + input_item = "analysis_id" + elif re.match(r'(ST[0-9]{6}$)', input_value): + input_item = "study_id" + else: + input_item = "analysis_id" + + return input_value, input_item + + +def cli(cmdargs): + """Implements the command line interface. + + param dict cmdargs: dictionary of command line arguments. + """ + + VERBOSE = cmdargs["--verbose"] + force = cmdargs['--force'] + fileio.VERBOSE = cmdargs["--verbose"] + silent = cmdargs['--silent'] + mwrest_base_url = cmdargs['--mw-rest'] + fileio.MWREST_URL = mwrest_base_url + mwrest.BASE_URL = mwrest_base_url + mwrest.VERBOSE = cmdargs["--verbose"] + output_format = OUTPUT_FORMATS[cmdargs.get("--output-format")] if cmdargs.get("--output-format") else "txt" + required_input_value = cmdargs.get('') + required_input_item = cmdargs.get('') + optional_input_item = cmdargs.get('--input-item') + optional_to_path = cmdargs.get('--to-path') + optional_output_item = cmdargs.get('--output-item') + required_output_item = cmdargs.get('') + + + # mwtab convert ... + if cmdargs["convert"]: + converter = Converter(from_path=cmdargs[""], + to_path=cmdargs[""], + from_format=cmdargs["--from-format"], + to_format=cmdargs["--to-format"], + force=force) + converter.convert() + + # mwtab validate ... + elif cmdargs["validate"]: + save_files = False + consolidate_files = False + consolidated_json = {} + if optional_to_path: + save_files = True + fileio._create_save_path(optional_to_path) + to_path = pathlib.Path(optional_to_path) + if pathlib.Path(to_path).suffix == '.json': + consolidate_files = True + + for i, (mwfile, e) in enumerate(fileio.read_with_class(cmdargs[""], + MWTabFile, + {'duplicate_keys':True, 'force':force}, + return_exceptions=True)): + if e is not None: + file_source = mwfile if isinstance(mwfile, str) else cmdargs[""] + print("Something went wrong when trying to read " + file_source) + traceback.print_exception(e, file=sys.stdout) + print() + continue + _, errors_list = validate_file( + mwtabfile = mwfile, + ms_schema = ms_required_schema, + nmr_schema = nmr_required_schema, + verbose = not silent + ) + if save_files: + if consolidate_files: + consolidated_json[pathlib.Path(mwfile.source).stem] = errors_list + else: + filename = pathlib.Path(mwfile.source).stem + '_validations.json' + with open(join(to_path, filename), "w", encoding="utf-8") as fh: + fh.write(json.dumps(errors_list, indent=2)) + + if consolidate_files: + with open(to_path, "w", encoding="utf-8") as fh: + fh.write(json.dumps(consolidated_json, indent=2)) + + # mwtab download ... + elif cmdargs["download"]: + context = [_context for _context in CONTEXTS if cmdargs.get(_context)][0] + + # mwtab download url ... + if cmdargs[""]: + download_and_save_mwrest_file({}, optional_to_path, full_url = cmdargs['']) + + # mwtab download study ... + elif cmdargs["study"]: + # mwtab download study all ... + if cmdargs["all"]: + # mwtab download study all ... + # mwtab download study all --input-item=analysis_id ... + # mwtab download study all --input-item=study_id ... + if not optional_input_item or optional_input_item in ("analysis_id", "study_id"): + if not optional_input_item or optional_input_item == "analysis_id": + id_list = [(an_id, 'analysis_id') for an_id in mwrest.analysis_ids()] + elif optional_input_item == "study_id": + id_list = [(study_id, 'study_id') for study_id in mwrest.study_ids()] + rest_params = {'context': 'study', + 'output_item': 'mwtab', + 'output_format': output_format} + download_and_save_ID_list(rest_params, id_list, VERBOSE, optional_to_path, mwrest_base_url) + + else: + raise ValueError("Unknown \"--input-item\" {}".format(optional_input_item)) + + # mwtab download study ... + elif required_input_value and not required_input_item: + # Read a json file with a list and make a best attempt to parse and download studies and analyses from the list. + if isfile(required_input_value): + with open(required_input_value, "r") as fh: + id_list = json.loads(fh.read()) + + if len(id_list) > 1 and optional_to_path and pathlib.Path(optional_to_path).suffix: + print('Error: The given "--to-path" option is a path to a file, ' + 'but the given list of IDs to download is greater ' + 'than 1. Please specify a directory to save the files to.') + sys.exit(0) + + if optional_input_item: + if optional_input_item in ("analysis_id", "study_id"): + id_list = [(input_id, optional_input_item) for input_id in id_list] + else: + raise ValueError("Unknown \"--input-item\" {}".format(optional_input_item)) + else: + id_list = [classify_input_value(_input_value) for _input_value in id_list] + + if VERBOSE: + print("Found {} Files to be Downloaded".format(len(id_list))) + + rest_params = {'context': 'study', + 'output_item': optional_output_item if optional_output_item else 'mwtab', + 'output_format': output_format} + download_and_save_ID_list(rest_params, id_list, VERBOSE, optional_to_path, mwrest_base_url) + + # Assume input value is a single analysis or study id and use --input-item to decide which, default to analysis_id + else: + input_item = cmdargs.get("--input-item") + input_value = required_input_value + if not input_item: + input_value, input_item = classify_input_value(input_value) + rest_params = {'context': 'study', + 'input_value': input_value, + 'input_item': input_item, + 'output_item': optional_output_item if optional_output_item else 'mwtab', + 'output_format': output_format} + download_and_save_mwrest_file(rest_params, optional_to_path, mwrest_base_url) + + # mwtab download (study | ...) ... + elif required_input_item: + rest_params = {'context': 'study', + 'input_value': required_input_value, + 'input_item': required_input_item, + 'output_item': required_output_item, + 'output_format': output_format} + download_and_save_mwrest_file(rest_params, optional_to_path, mwrest_base_url) + + # mwtab download (... | compound | refmet | gene | protein) ... + elif context in ['compound', 'refmet', 'gene', 'protein']: + rest_params = {'context': context, + 'input_value': required_input_value, + 'input_item': required_input_item, + 'output_item': required_output_item, + 'output_format': output_format} + download_and_save_mwrest_file(rest_params, optional_to_path, mwrest_base_url) + + # mwtab download moverz [--verbose] + elif cmdargs["moverz"]: + rest_params = {'context': 'moverz', + 'input_item': required_input_item, + "m/z_value": cmdargs[""], + "ion_type_value": cmdargs[""], + "m/z_tolerance_value": cmdargs[""]} + download_and_save_mwrest_file(rest_params, optional_to_path, mwrest_base_url) + + # mwtab download exactmass [--verbose] + elif cmdargs["exactmass"]: + rest_params = {'context': 'exactmass', + "LIPID_abbreviation": cmdargs[""], + "ion_type_value": cmdargs[""],} + download_and_save_mwrest_file(rest_params, optional_to_path, mwrest_base_url) + + # mwtab extract ... + elif cmdargs["extract"]: + mwfile_generator = fileio.read_with_class(cmdargs[""], + MWTabFile, + {'duplicate_keys':True, "force": force}, + return_exceptions=True) + if cmdargs["metabolites"]: + metabolites_dict = mwextract.extract_metabolites( + mwfile_generator, + mwextract.generate_matchers( + [(cmdargs[""][i], + cmdargs[""][i] if not cmdargs[""][i][:2] == "r'" else re.compile(cmdargs[""][i][2:-1])) + for i in range(len(cmdargs[""]))] + ) + ) + + if metabolites_dict: + if cmdargs[""] != "-": + if cmdargs["--to-format"] == "csv": + mwextract.write_metabolites_csv(cmdargs[""], metabolites_dict, cmdargs["--no-header"]) + else: + mwextract.write_json(cmdargs[""], metabolites_dict) + else: + print(json.dumps(metabolites_dict, indent=4, cls=mwextract.SetEncoder)) + else: + print("No metabolites extracted. No file was saved. " + "This is likely due to key value pairs filtering all of the studies out. " + "Check your key value pairs and data.") + + elif cmdargs["metadata"]: + metadata = dict() + for mwtabfile, e in mwfile_generator: + if e is not None: + file_source = mwtabfile if isinstance(mwtabfile, str) else cmdargs[""] + print("Something went wrong when trying to read " + file_source) + traceback.print_exception(e, file=sys.stdout) + print() + continue + extracted_values = mwextract.extract_metadata(mwtabfile, cmdargs[""]) + [metadata.setdefault(key, set()).update(val) for (key, val) in extracted_values.items()] + if metadata: + if cmdargs[""] != "-": + if cmdargs["--to-format"] == "csv": + mwextract.write_metadata_csv(cmdargs[""], metadata, cmdargs["--no-header"]) + else: + mwextract.write_json(cmdargs[""], metadata) + else: + print(metadata) + else: + print("No metadata extracted. No file was saved.") + diff --git a/mwtab/converter.py b/src/mwtab/converter.py old mode 100755 new mode 100644 similarity index 74% rename from mwtab/converter.py rename to src/mwtab/converter.py index 0c46a21..0954db3 --- a/mwtab/converter.py +++ b/src/mwtab/converter.py @@ -108,14 +108,17 @@ import tarfile import bz2 import gzip +import traceback +import sys from . import fileio +from .mwtab import MWTabFile class Translator(object): """Translator abstract class.""" - def __init__(self, from_path, to_path, from_format=None, to_format=None, validate=False): + def __init__(self, from_path, to_path, from_format=None, to_format=None, force=False): """Translator initializer. :param str from_path: Path to input file(s). :param str to_path: Path to output file(s). @@ -128,7 +131,7 @@ def __init__(self, from_path, to_path, from_format=None, to_format=None, validat self.to_format = to_format self.from_path_compression = fileio.GenericFilePath.is_compressed(from_path) self.to_path_compression = fileio.GenericFilePath.is_compressed(to_path) - self.validate = validate + self.force = force def __iter__(self): """Abstract iterator must be implemented in a subclass.""" @@ -141,37 +144,48 @@ class MWTabFileToMWTabFile(Translator): file_extension = {"json": ".json", "mwtab": ".txt"} - def __init__(self, from_path, to_path, from_format=None, to_format=None, validate=False): + def __init__(self, from_path, to_path, from_format=None, to_format=None, force=False): """MWTabFileToMWTabFile translator initializer. :param str from_path: Path to input file(s). :param str to_path: Path to output file(s). :param str from_format: Input format: `mwtab` or `json`. :param str to_format: Output format: `mwtab` or `json`. - :param bool validate: whether to validate or not. + :param bool force: If True, replace non-dictionary values in METABOLITES_DATA, METABOLITES, + and EXTENDED with empty dicts on read in for JSON. """ - super(MWTabFileToMWTabFile, self).__init__(from_path, to_path, from_format, to_format, validate) + super(MWTabFileToMWTabFile, self).__init__(from_path, to_path, from_format, to_format, force) def __iter__(self): """Iterator that yields instances of :class:`~mwtab.mwtab.MWTabFile` instances. :return: instance of :class:`~mwtab.mwtab.MWTabFile` object instance. :rtype: :class:`~mwtab.mwtab.MWTabFile` """ - for mwtabfile in fileio.read_files(self.from_path, validate=self.validate): + for mwtabfile, e in fileio.read_with_class(self.from_path, + MWTabFile, + {'duplicate_keys':True, 'force':self.force}, + return_exceptions=True): + if e is not None: + file_source = mwtabfile if isinstance(mwtabfile, str) else self.from_path + print("Something went wrong when trying to read " + file_source) + traceback.print_exception(e, file=sys.stdout) + print() + continue yield mwtabfile class Converter(object): """Converter class to convert ``mwTab`` files from ``mwTab`` to ``JSON`` or from ``JSON`` to ``mwTab`` format.""" - def __init__(self, from_path, to_path, from_format="mwtab", to_format="json", validate=False): + def __init__(self, from_path, to_path, from_format="mwtab", to_format="json", force=False): """Converter initializer. :param str from_path: Path to input file(s). :param str to_path: Path to output file(s). :param str from_format: Input format: `mwtab` or `json`. :param str to_format: Output format: `mwtab` or `json`. - :param bool validate: whether to validate or not. + :param bool force: If True, replace non-dictionary values in METABOLITES_DATA, METABOLITES, + and EXTENDED with empty dicts on read in for JSON. """ - self.file_generator = MWTabFileToMWTabFile(from_path, to_path, from_format, to_format, validate) + self.file_generator = MWTabFileToMWTabFile(from_path, to_path, from_format, to_format, force) def convert(self): """Convert file(s) from ``mwTab`` format to ``JSON`` format or from ``JSON`` format to ``mwTab`` format. @@ -194,6 +208,8 @@ def convert(self): self._one_to_one() elif self.file_generator.from_path.isdigit(): self._one_to_one() + elif not os.path.exists(self.file_generator.from_path): + raise FileNotFoundError(f'No such file or directory: "{self.file_generator.from_path}"') else: raise TypeError('Unknown input file format: "{}"'.format(self.file_generator.from_path)) @@ -229,7 +245,7 @@ def _one_to_one(self): raise TypeError('One-to-many conversion, cannot convert "{}" into "{}"'.format(self.file_generator.from_path, self.file_generator.to_path)) else: - raise TypeError('Unknown format: "{}"'.format(self.file_generator.to_path)) + raise TypeError('Unknown output file format: "{}"'.format(self.file_generator.to_path)) def _to_dir(self, file_generator): """Convert files to directory. @@ -237,13 +253,21 @@ def _to_dir(self, file_generator): :rtype: :py:obj:`None` """ for f in file_generator: - outpath = self._output_path(f.source, file_generator.to_format) - - if not os.path.exists(os.path.dirname(outpath)): - os.makedirs(os.path.dirname(outpath)) - - with open(outpath, mode="w") as outfile: - f.write(outfile, file_generator.to_format) + try: + outpath = self._output_path(f.source, file_generator.to_format) + + if not os.path.exists(os.path.dirname(outpath)): + os.makedirs(os.path.dirname(outpath)) + + with open(outpath, mode="w", encoding="utf-8") as outfile: + f.write(outfile, file_generator.to_format) + except Exception as e: + print("Something went wrong when trying to convert " + f.source) + traceback.print_exception(e, file=sys.stdout) + print() + + if os.path.exists(outpath): + os.remove(outpath) def _to_zipfile(self, file_generator): """Convert files to zip archive. @@ -252,8 +276,13 @@ def _to_zipfile(self, file_generator): """ with zipfile.ZipFile(file_generator.to_path, mode="w", compression=zipfile.ZIP_DEFLATED) as outfile: for f in file_generator: - outpath = self._output_path(f.source, file_generator.to_format, archive=True) - outfile.writestr(outpath, f.writestr(file_generator.to_format)) + try: + outpath = self._output_path(f.source, file_generator.to_format, archive=True) + outfile.writestr(outpath, f.writestr(file_generator.to_format)) + except Exception as e: + print("Something went wrong when trying to convert " + f.source) + traceback.print_exception(e, file=sys.stdout) + print() def _to_tarfile(self, file_generator): """Convert files to tar archive. @@ -271,11 +300,16 @@ def _to_tarfile(self, file_generator): with tarfile.open(file_generator.to_path, mode=tar_mode) as outfile: for f in file_generator: - outpath = self._output_path(f.source, file_generator.to_format, archive=True) - info = tarfile.TarInfo(outpath) - data = f.writestr(file_generator.to_format).encode() - info.size = len(data) - outfile.addfile(tarinfo=info, fileobj=io.BytesIO(data)) + try: + outpath = self._output_path(f.source, file_generator.to_format, archive=True) + info = tarfile.TarInfo(outpath) + data = f.writestr(file_generator.to_format).encode() + info.size = len(data) + outfile.addfile(tarinfo=info, fileobj=io.BytesIO(data)) + except Exception as e: + print("Something went wrong when trying to convert " + f.source) + traceback.print_exception(e, file=sys.stdout) + print() def _to_bz2file(self, file_generator): """Convert file to bz2-compressed file. @@ -284,7 +318,12 @@ def _to_bz2file(self, file_generator): """ with bz2.BZ2File(file_generator.to_path, mode="wb") as outfile: for f in file_generator: - outfile.write(f.writestr(file_generator.to_format).encode()) + try: + outfile.write(f.writestr(file_generator.to_format).encode()) + except Exception as e: + print("Something went wrong when trying to convert " + f.source) + traceback.print_exception(e, file=sys.stdout) + print() def _to_gzipfile(self, file_generator): """Convert file to gzip-compressed file. @@ -293,7 +332,12 @@ def _to_gzipfile(self, file_generator): """ with gzip.GzipFile(file_generator.to_path, mode="wb") as outfile: for f in file_generator: - outfile.write(f.writestr(file_generator.to_format).encode()) + try: + outfile.write(f.writestr(file_generator.to_format).encode()) + except Exception as e: + print("Something went wrong when trying to convert " + f.source) + traceback.print_exception(e, file=sys.stdout) + print() def _to_textfile(self, file_generator): """Convert file to regular text file. @@ -304,9 +348,18 @@ def _to_textfile(self, file_generator): if file_generator.to_path.endswith(file_generator.file_extension[file_generator.to_format]) \ else file_generator.to_path + file_generator.file_extension[file_generator.to_format] - with open(to_path, mode="w") as outfile: - for f in file_generator: - outfile.write(f.writestr(file_generator.to_format)) + + for f in file_generator: + try: + with open(to_path, mode="w", encoding="utf-8") as outfile: + outfile.write(f.writestr(file_generator.to_format)) + except Exception as e: + print("Something went wrong when trying to convert " + f.source) + traceback.print_exception(e, file=sys.stdout) + print() + + if os.path.exists(to_path): + os.remove(to_path) def _output_path(self, input_path, to_format, archive=False): """Construct an output path string from an input path string. @@ -327,5 +380,8 @@ def _output_path(self, input_path, to_format, archive=False): outdirpath = os.path.join(*outparts) if outparts else "" else: outdirpath = os.path.join(self.file_generator.to_path, *outparts) + + if not fname.endswith(self.file_generator.file_extension[to_format]): + fname = fname + self.file_generator.file_extension[to_format] - return os.path.join(outdirpath, fname + self.file_generator.file_extension[to_format]) \ No newline at end of file + return os.path.join(outdirpath, fname) \ No newline at end of file diff --git a/src/mwtab/duplicates_dict.py b/src/mwtab/duplicates_dict.py new file mode 100644 index 0000000..bb80332 --- /dev/null +++ b/src/mwtab/duplicates_dict.py @@ -0,0 +1,112 @@ +# -*- coding: utf-8 -*- +""" +mwtab.duplicates_dict +~~~~~~~~~~~ + +This module provides the :class:`~mwtab.duplicates_dict.DuplicatesDict` class +that is a dictionary that can handle duplicate keys. +""" + +import re +import copy + +DUPLICATE_KEY_REGEX = r'(.*)\{\{\{.*\}\}\}' + +class DuplicatesDict(dict): + """ + This needs to inherit from a dict type class in order for it to be printed out + correctly by the json module. i.e. json.dumps(). + + Note: + The C encoder implementation in the json module checks whether a dictionary + is empty, and if so, short cuts to just printing {} instead of iterating + over items. A dummy value must be set so that we don't trip that short cut. + This is set in the init under the key 'dummy' if set_dummy is True. It is + possible this could cause other issues trying to use this class with other + modules, if so, either set set_dummy to False, or delete the 'dummy' key. + """ + def __init__(self, obj: dict|None = None, set_dummy: bool = True): + obj = {} if obj is None else obj + if isinstance(obj, DuplicatesDict): + self.data = obj.data + self._key_count = obj._key_count + else: + self.data = obj + self._key_count = {key:0 for key in obj.keys()} + + self._set_dummy = set_dummy + if set_dummy: + self.setdefault('dummy', None) + + def __len__(self): + return len(self.data) + + def __bool__(self): + return len(self.data) > 0 + + def __iter__(self): + return self.data.__iter__() + + def __getitem__(self, key): + return self.data[key] + + def __setitem__(self, key, value): + if key in self.data: + self._key_count[key] += 1 + self.data[key + '{{{_' + str(self._key_count[key]) + '_}}}'] = value + else: + self._key_count[key] = 0 + self.data[key] = value + + def __contains__(self, key): + return key in self.data + + def __eq__(self, compare): + return self.data == compare + + def __ne__(self, compare): + return self.data != compare + + def __repr__(self): + return self.data.__repr__().replace('OrderedDict', 'DuplicatesDict') + + def __delitem__(self, key): + del self.data[key] + + def keys(self): + return [key if not key.endswith('}}}') else re.match(DUPLICATE_KEY_REGEX, key).group(1) for key in self.data] + + def raw_keys(self): + return self.data.keys() + + def items(self): + items = [] + for key, value in self.data.items(): + key = key if not key.endswith('}}}') else re.match(DUPLICATE_KEY_REGEX, key).group(1) + items.append((key, value)) + return items + + def raw_items(self): + return self.data.items() + + def values(self): + return self.data.values() + + def update(self, iterable): + self.data.update(iterable) + + def __deepcopy__(self, memo): + new_dict = DuplicatesDict({}, self._set_dummy) + memo[id(new_dict)] = new_dict + for key, value in self.raw_items(): + new_dict[key] = copy.deepcopy(value, memo) + return new_dict + + def __copy__(self): + new_dict = DuplicatesDict({}, self._set_dummy) + for key, value in self.raw_items(): + new_dict[key] = copy.copy(value) + return new_dict + + + diff --git a/src/mwtab/fileio.py b/src/mwtab/fileio.py new file mode 100644 index 0000000..f8f0ef8 --- /dev/null +++ b/src/mwtab/fileio.py @@ -0,0 +1,320 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +mwtab.fileio +~~~~~~~~~~~~ + +This module provides routines for reading ``mwTab`` formatted files +from difference kinds of sources: + + * Single ``mwTab`` formatted file on a local machine. + * Directory containing multiple ``mwTab`` formatted files. + * Compressed zip/tar archive of ``mwTab`` formatted files. + * URL address of ``mwTab`` formatted file. + * ``ANALYSIS_ID`` of ``mwTab`` formatted file. +""" + +import os +import io +import zipfile +import tarfile +import bz2 +import gzip +from re import match +import pathlib +from typing import Any +from functools import partial + +from . import mwtab +from . import mwrest + +from urllib.request import urlopen +from urllib.parse import urlparse + + +VERBOSE = False +MWREST_URL = mwrest.BASE_URL + + +def _create_save_path(path): + """Create directories in the path that don't already exist. + + :param str path: Path to save something to. + """ + path = pathlib.Path(path) + # Assume if there is a suffix (file extension) that it's a file and only make the parent path. + if path.suffix: + path.parent.mkdir(parents=True, exist_ok=True) + else: + path.mkdir(parents=True, exist_ok=True) + + +def _return_correct_yield(*payload, exception, return_exceptions): + """Simple helper to yield either payload or payload and exception for generators. + + :param Any payload: Any object(s) to be yielded back. + :param Exception or None exception: Any excpetion that occurred during the execution of the generator + :param bool return_exceptions: Whether to yield a tuple with payload and exception or just the payload. + :return: If return_exceptions is True return a tuple of payload and exception, else return only payload. + """ + if exception is not None and not return_exceptions: + raise exception + return (*payload, exception) if return_exceptions else payload[0] if len(payload) == 1 else payload + + +def _generate_filenames(sources, return_exceptions=False): + """Generate filenames. + + :param tuple sources: Sequence of strings representing path to file(s). + :param bool return_exceptions: Whether to yield a tuple with path and exception or just the path. + :return: Path to file(s), or a tuple of path and exceptions. + :rtype: :py:class:`str` + """ + for source in sources: + try: + if os.path.isdir(source): + for path, _, filelist in os.walk(source): + for fname in sorted(filelist): + if os.path.splitext(fname)[1].lower() in {".csv", ".txt", ".json"}: + yield _return_correct_yield(os.path.join(path, fname), + exception=None, + return_exceptions=return_exceptions) + + elif os.path.isfile(source): + yield _return_correct_yield(source, + exception=None, + return_exceptions=return_exceptions) + + elif source.isdigit(): + url, e = next(mwrest.generate_mwtab_urls([source], base_url=MWREST_URL, return_exceptions=True)) + yield _return_correct_yield(url, + exception=e, + return_exceptions=return_exceptions) + + # TODO: Add ST parsing + elif match(r"(AN[0-9]{6}$)", source): + url, e = next(mwrest.generate_mwtab_urls([source], base_url=MWREST_URL, return_exceptions=True)) + yield _return_correct_yield(url, + exception=e, + return_exceptions=return_exceptions) + + elif GenericFilePath.is_url(source): + yield _return_correct_yield(source, + exception=None, + return_exceptions=return_exceptions) + + else: + yield _return_correct_yield(source, + exception=TypeError("Unknown file source."), + return_exceptions=return_exceptions) + except Exception as e: + yield _return_correct_yield(source, + exception=e, + return_exceptions=return_exceptions) + + +def _generate_handles(filenames, return_exceptions=False): + """Open a sequence of filenames one at time producing file objects. + The file is closed immediately when proceeding to the next iteration. + + :param generator filenames: Generator object that yields the path to each file, one at a time. + :param bool return_exceptions: Whether to yield a tuple with filehandle and exception or just the filehandle. + :return: Filehandle to be processed into an instance. + """ + for fname, exc in filenames: + if exc is not None: + yield _return_correct_yield(None, fname, + exception=exc, + return_exceptions=return_exceptions) + continue + try: + path = GenericFilePath(fname) + for filehandle, source in path.open(): + yield _return_correct_yield(filehandle, source, + exception=None, + return_exceptions=return_exceptions) + filehandle.close() + except Exception as e: + yield _return_correct_yield(None, fname, + exception=e, + return_exceptions=return_exceptions) + + +def read_with_class(sources: str|list[str], read_class: type, class_kwds: dict, return_exceptions: bool = False) -> tuple[Any, Exception]|Any: + """Read from sources using the given read_class. + + This is really created to use functools partial to create a read mwthod for a particular class. + + Args: + sources: A string or list of strings to read from. + read_class: A class with a read() method to instantiate to read from source. + class_kwds: A dictionary of keyword arguments to pass to the class constructor. + return_exceptions: Whether to yield a tuple with file instance and exception or just the file instance. + + Returns: + Returns the instantiated class and any exceptions, or None and any exceptions, or the source and any exceptions. + """ + sources = [sources] if not isinstance(sources, list) else sources + try: + filenames = _generate_filenames(sources, True) + filehandles = _generate_handles(filenames, True) + except Exception as e: + yield _return_correct_yield(None, + exception=e, + return_exceptions=return_exceptions) + for fh, source, exc in filehandles: + if exc is not None: + yield _return_correct_yield(source, + exception=exc, + return_exceptions=return_exceptions) + continue + try: + f = read_class(source, **class_kwds) + f.read(fh) + fh.close() + + if VERBOSE: + print("Processed file: {}".format(os.path.abspath(source))) + + yield _return_correct_yield(f, + exception=None, + return_exceptions=return_exceptions) + + except Exception as e: + fh.close() + if VERBOSE: + print("Error processing file: ", os.path.abspath(source), "\nReason:", e) + yield _return_correct_yield(source, + exception=e, + return_exceptions=return_exceptions) + + +read_files = partial(read_with_class, read_class = mwtab.MWTabFile, class_kwds = {"duplicate_keys": True}) +read_mwrest = partial(read_with_class, read_class = mwrest.MWRESTFile, class_kwds = {}) + +class ReadLines(): + def __init__(self, source, *args, **kwargs): + self.source = source + self.lines = [] + + def read(self, filehandle): + """ + """ + string = filehandle.read() + filehandle.close() + if isinstance(string, str): + lines = string.replace("\r", "\n").split("\n") + elif isinstance(string, bytes): + lines = string.decode("utf-8").replace("\r", "\n").split("\n") + else: + raise TypeError("Expecting or , but {} was passed".format(type(string))) + + self.lines = [line for line in lines if line] + + +read_lines = partial(read_with_class, read_class = ReadLines, class_kwds = {}) + + +class GenericFilePath(object): + """`GenericFilePath` class knows how to open local files or files over URL.""" + + def __init__(self, path): + """Initialize path. + + :param str path: String representing a path to local file(s) or valid URL address of file(s). + """ + self.path = path + + def open(self): + """Generator that opens and yields filehandles using appropriate facilities: + test if path represents a local file or file over URL, if file is compressed + or not. + + :return: Filehandle to be processed into an instance. + """ + is_url = self.is_url(self.path) + compression_type = self.is_compressed(self.path) + + if not compression_type: + if is_url: + filehandle = urlopen(self.path) + else: + filehandle = open(self.path, "r", encoding="utf-8") + source = self.path + yield filehandle, source + filehandle.close() + + elif compression_type: + if is_url: + response = urlopen(self.path) + path = response.read() + response.close() + else: + path = self.path + + if compression_type == "zip": + ziparchive = zipfile.ZipFile(io.BytesIO(path), "r") if is_url else zipfile.ZipFile(path) + for name in ziparchive.infolist(): + if not name.filename.endswith("/"): + filehandle = ziparchive.open(name) + source = self.path + "/" + name.filename + yield filehandle, source + filehandle.close() + + elif compression_type in ("tar", "tar.bz2", "tar.gz"): + tararchive = tarfile.open(fileobj=io.BytesIO(path)) if is_url else tarfile.open(path) + for name in tararchive: + if name.isfile(): + filehandle = tararchive.extractfile(name) + source = self.path + "/" + name.name + yield filehandle, source + filehandle.close() + + elif compression_type == "bz2": + filehandle = bz2.BZ2File(io.BytesIO(path)) if is_url else bz2.BZ2File(path) + source = self.path + yield filehandle, source + filehandle.close() + + elif compression_type == "gz": + filehandle = gzip.open(io.BytesIO(path)) if is_url else gzip.open(path) + source = self.path + yield filehandle, source + filehandle.close() + + @staticmethod + def is_compressed(path): + """Test if path represents compressed file(s). + + :param str path: Path to file(s). + :return: String specifying compression type if compressed, "" otherwise. + :rtype: :py:class:`str` + """ + if path.endswith(".zip"): + return "zip" + elif path.endswith(".tar.gz"): + return "tar.gz" + elif path.endswith(".tar.bz2"): + return "tar.bz2" + elif path.endswith(".gz"): + return "gz" + elif path.endswith(".bz2"): + return "bz2" + elif path.endswith(".tar"): + return "tar" + return "" + + @staticmethod + def is_url(path): + """Test if path represents a valid URL. + + :param str path: Path to file. + :return: True if path is valid url string, False otherwise. + :rtype: :py:obj:`True` or :py:obj:`False` + """ + try: + parse_result = urlparse(path) + return all((parse_result.scheme, parse_result.netloc, parse_result.path)) + except ValueError: + return False diff --git a/src/mwtab/metadata_column_matching.py b/src/mwtab/metadata_column_matching.py new file mode 100644 index 0000000..42202fd --- /dev/null +++ b/src/mwtab/metadata_column_matching.py @@ -0,0 +1,1057 @@ +# -*- coding: utf-8 -*- +""" +Metadata Column Matching +~~~~~~~~~~~~~~~~~~~~~~~~ +Regular expressions, functions, and classes to match column names and values in mwtab METABOLITES blocks. + +More information can be found on the :doc:`metadata_column_matching` page. +""" + +import re + +import pandas + + + + +# Sometimes 0 is also an NA value, but it can be hard tell unless you see it in an ID column like KEGG ID or something. +# Note the slightly different hyphen character. It is not a duplicate. +NA_VALUES = ['', '-', '−', '--', '---', '.', ',', + 'NA', 'na', 'n.a.', 'N.A.', 'n/a', 'N/A', '', '#N/A', 'NaN', 'nan', 'N', 'null', 'Null', 'NULL', 'NF', + 'No result', 'NOT Found in Database', 'No ID', 'no data', 'unknown', 'undefined', 'No record', 'NIDB', + 'Not available', 'TBC', 'Internal Standard', 'Intstd', 'internal standard', 'Internal standard', + 'Spiked Stable Isotope Labeled Internal Standards', 'Int Std'] + +#: Used for wrapping regexes in certain functions. +WRAP_STRING = r'[^a-zA-Z0-9]' +def _create_column_regex_any(match_strings: list[str]) -> str: + """Return a regular expression string that will match any of the strings in match_strings. + + Automatically adds ``WRAP_STRING`` to either side of each string to help with some fuzzy matching. + Intended to be used to match column names in METABOLITES data. + + Args: + match_strings: list of strings to create a regex for. + + Returns: + A regular expression string that will match any of the strings in match_strings. + """ + regex = '|'.join(['(' + WRAP_STRING + '|^)' + match_string + '(' + WRAP_STRING + '|$)' for match_string in match_strings]) + return regex + +def _create_column_regex_all(match_strings_sets: list[list[str]]) -> str: + """Return a regular expression string that will match any of the string sets in match_strings_sets. + + Automatically adds ``WRAP_STRING`` to either side of each string to help with some fuzzy matching. + Intended to be used to match column names in METABOLITES data. The "all" refers to requiring each + string within a set to be present to match, but if multiple sets are given, then any set will match. + "set" does not mean the actual "set" data type, any iterable collection is fine. + + Args: + match_strings_sets: list of string sets to create a regex for. + + Returns: + A regular expression string that will match any of the string sets in match_strings_sets. + """ + regex = '|'.join([''.join(['(?=.*(' + WRAP_STRING + '|^)' + match_string + '(' + WRAP_STRING + '|$))' for match_string in match_strings]) for match_strings in match_strings_sets]) + return regex + + +class NameMatcher(): + """Used to filter names that match certain criteria. + + Mostly intended to be used through the ColumnFinder class. Created for the purpose + of matching tabular column names based on regular expressions and "in" criteria. + + Parameters: + regex_search_strings: A collection of strings to deliver to re.search() to match a column name. + If any string in the collection matches, then the name is matched. This does not simply + look for any of the strings within a column name to match. Each string is wrapped with + ``WRAP_STRING`` before searching, so the string 'bar' would not be found in + the column name "foobarbaz", but would be found in the name "foo bar baz". + not_regex_search_strings: The same as regex_search_strings, except a match to a column name + eliminates that name. Attributes that begin with "not" take precedence over the others. + So if a column name matches a string in regex_search_strings and not_regex_search_strings, + then it will be filtered OUT. + regex_search_sets: A collection of sets of strings. Each string in the set of strings must + be found in the column name to match, but any set could be found. For example, [('foo', 'bar'), ('baz', 'asd')] + will match the name "foo bar" or "bar foo", but not "foobar", due to the aforementioned + wrapping with ``WRAP_STRING``. The names "foo", "baz", or "asd" would not match either, but "var asd baz" would. + in_strings: Similar to regex_search_strings except instead of using re.search() the "in" operator + is used. For example, ['foo'] would match the column name "a fool", since 'foo' is in "a fool". + not_in_strings: The same as in_strings, but matches to a column name eliminate or filter OUT that name. + in_string_sets: The same as regex_search_sets, but each string in a set is determined to match + using the "in" operator instead of re.search(). For example, [('foo', 'bar'), ('baz', 'asd')] + WILL match the column name "foobar" because both 'foo' and 'bar' are in the name. + exact_strings: A collection of strings that must exactly match the column name. For example, + ['foo', 'bar'] would only the match the column names "foo" or "bar". + + Examples: + Find a column for "moverz". + + >>> NameMatcher(regex_search_strings = ['m/z', 'mz', 'moverz', 'mx'], + ... not_regex_search_strings = ['id'], + ... in_strings = ['m.z', 'calcmz', 'medmz', 'm_z', 'obsmz', 'mass to charge', 'mass over z'], + ... not_in_strings = ['spec', 'pectrum', 'structure', 'regno', 'retention']) + + This is a real example based on the datasets in the Metabolomics Workbench. We can examine + some of the strings to illustrate the attributes' function. The "id" string needs to be in + not_regex_search_strings, rather than not_in_strings, because "id" is a very small substring + that could easily be in a longer word. Putting in not_regex_search_strings means it will + most likely match "ID" fields, such as "PubChem ID". Note that it is recommend to lower + all column names before filtering and thus use lower case strings, but in general NameMatcher + is case sensitive. The "spec" string is in not_in_strings, rather than not_regex_search_strings + because the risk of it being in a name that should not be filtered out is low. Also it + catches both the full word "spectrum" and its common abbreviation "spec". Hopefully, + these 2 explanations of "id" and "spec" have illustrated some of the tradeoffs and + advantages of the "in" style attributes versus the "search" style ones. + + Find a column for "retention time". + + >>> NameMatcher(regex_search_strings = ['rt'], + ... regex_search_sets = [['ret', 'time']], + ... in_strings = ['rtimes', 'r.t.', 'medrt', 'rtsec', 'bestrt', 'compoundrt', 'rtmed'], + ... in_string_sets = [['retention', 'time'], ['rentetion', 'time'], ['retension', 'time']], + ... not_in_strings = ['type', 'error', 'index', 'delta', 'feature', 'm/z']) + + This is another real example based on the datasets in the Metabolomics Workbench. It + illustrates the "set" style attributes quite well. For multi-word column names the + "set" style attributes are usually what you want to use. It is possible to to give + a string like "retention time", note the space character, to an attribute like "in_strings", + but this is more fragile than it seems and won't match some common alternate spellings or + mistakes, such as "retention_time" or "retention time". Using "set" style attributes + means you don't have to add as many strings to an attribute like "in_strings". You + can still see some repetition in the in_string_sets attribute here though to cover the + many mispellings of "retention". "set" style attributes would not be a good use case + if the strings in the set must be in a certain order though. The set ['ret', 'time'] + will match 'ret' and 'time' in any order. Generally, this will not be a problem because + there aren't many instances where you will get a false positive match for a multi-word + column due to the order of the words. + + Find a column for "other_id". + + >>> NameMatcher(not_regex_search_strings = ['cas'], + ... in_strings = ['other'], + ... in_string_sets = [['database', 'identifier'], ['chemical', 'id'], ['cmpd', 'id'], + ... ['database', 'id'], ['database', 'match'], ['local', 'id'], + ... ['row', 'id'], ['comp', 'id'], ['chem', 'id'], ['chro', 'lib', 'id'], + ... ['lib', 'id']], + ... not_in_strings = ['type', 'pubchem', 'chemspider', 'kegg'], + ... exact_strings = ['id'],) + + This is another real example based on the datasets in the Metabolomics Workbench. It is shown + to demonstrate the "exact_strings" attribute. There are many columns that contain the "id" + string. There are specific database ID columns, such as those from PubChem or KEGG, but there + are often lesser known or individual lab IDs. This example is trying to lump many of the lesser + ones into a single "other_id" column. Trying to have "id" in an in_strings or regex_search_strings + attribute would cause far too many false positive matches for reasons described in the first + example, but there are columns simply labeled "ID", so the only recourse is to use the exact_strings + attribute to match them exactly. + + Typical usage. + + >>> df = pandas.read_csv('some_file.csv') + >>> name_matcher = NameMatcher(exact_strings = ['foo']) + >>> modified_columns = {{column_name: column_name.lower().strip() for column_name in df.columns}} + >>> matching_columns = name_matcher.dict_match(modified_columns) + + NameMatcher is really meant to be used as part of a ColumnFinder, but this example uses it + directly for simplicity. The instantiated NameMatcher is also very simple in this example + because it is trying to show the usage of the dict_match method more than anything else. + dict_match requires a dictionary as input, rather than a simple list so that column names + can be modified if necessary for easier matching, but then still be linked back to the original + name in the dataframe. + + Attributes: + regex_search_strings (list[str]): The current list of strings used for regex searching. + not_regex_search_strings (list[str]): The current list of strings used for regex searching to exclude names. + regex_search_sets (list[list[str]]): The current list of string sets used for regex searching. + in_strings (list[str]): The current list of strings used for "in" operator matching. + not_in_strings (list[str]): The current list of strings used for "in" operator matching to exclude names. + in_string_sets (list[list[str]]): The current list of string sets used for "in" operator matching. + exact_strings (list[str]): The current collection of strings used for "==" operator matching. + """ + + def __init__(self, regex_search_strings: None|list[str] = None, + not_regex_search_strings: None|list[str] = None, regex_search_sets: None|list[list[str]] = None, + in_strings: None|list[str] = None, not_in_strings: None|list[str] = None, + in_string_sets: None|list[list[str]] = None, exact_strings: None|list[str] = None): + self.regex_search_strings: None|list[str] = regex_search_strings if regex_search_strings else [] + self.not_regex_search_strings = not_regex_search_strings if not_regex_search_strings else [] + self.regex_search_sets = regex_search_sets if regex_search_sets else [] + self.in_strings = in_strings if in_strings else [] + self.not_in_strings = not_in_strings if not_in_strings else [] + self.in_string_sets = in_string_sets if in_string_sets else [] + self.exact_strings = exact_strings if exact_strings else [] + + def dict_match(self, name_map: dict[str, str]) -> list[str]: + """Return a list of names that match based on the NameMatcher attributes. + + Find all names in name_map that match. name_map should be a dictionary of original names + to modified names. The value is used for matching, but the key is what will be returned. + Each of the name regex, in_string, and exact strings attributes are ORed together, + meaning any of them can be used to match, except for the "not" parameters. If a column + name is matched by a "not" parameter, then it overrides other matches and will be filtered out. + + Args: + name_map: a dictionary of original names to the modified version of that name to use for matching. + + Returns: + A list of names that match based on the NameMatcher attributes. + """ + search_regex = _create_column_regex_any(self.regex_search_strings) + search_regex_sets = _create_column_regex_all(self.regex_search_sets) + not_search_regex = _create_column_regex_any(self.not_regex_search_strings) + has_regex_search_strings = len(self.regex_search_strings) > 0 + has_regex_search_sets = len(self.regex_search_sets) > 0 + has_in_string_sets = len(self.in_string_sets) > 0 + has_no_not_regex_search_strings = len(self.not_regex_search_strings) == 0 + has_no_not_in_strings = len(self.not_in_strings) == 0 + columns_of_interest = [original_name for original_name, modified_name in name_map.items() if \ + ( + (has_regex_search_strings and re.search(search_regex, modified_name)) or \ + (has_regex_search_sets and re.search(search_regex_sets, modified_name)) or \ + any([word in modified_name for word in self.in_strings]) or \ + (has_in_string_sets and any([all([word in modified_name for word in word_set]) for word_set in self.in_string_sets])) or \ + any([word == modified_name for word in self.exact_strings]) + ) and \ + (has_no_not_regex_search_strings or not re.search(not_search_regex, modified_name)) and \ + (has_no_not_in_strings or all([word not in modified_name for word in self.not_in_strings]))] + return columns_of_interest + +class ValueMatcher(): + """Used to find a mask for certain values in a column. + + Mostly intended to be used through the ColumnFinder class. Created for the purpose + of matching tabular column data based on regular expressions and type criteria. + + Parameters: + values_type: A string whose only relevant values are 'integer', 'numeric', and 'non-numeric'. + 'integer' will only match values in a column that are integer numbers. 'numeric' will only + match values that are numbers, this includes integers. 'non-numeric' will only match values + that are non-numeric. Numeric values can be in the value, but cannot be the whole value. + For example, '123 id' is considered non-numeric. + values_regex: A regular expression to positively identify values in a column. + inverse_values_regex: A regular expression to negatively identify values in a column. This + is mutually exclusive with values_regex. If both are given, values_regex takes precedence + and inverse_values_regex is ignored. values_type can be combined with either regex and + values must match both criteria to match overall. + + Examples: + Simple type example. + + >>> vm = ValueMatcher(values_type = 'numeric') + >>> test = pandas.Series([1, '1', 'foo']) + >>> vm.series_match(test) + 0 True + 1 True + 2 False + dtype: bool + + This ValueMatcher is very simple and will only match numeric values. Note that numeric + values in string form are also recognized as numeric. This is intentional. + + Simple regex example. + + >>> vm = ValueMatcher(values_regex = 'foo.*') + >>> test = pandas.Series(['foo', 'bar', 'foobar', 1]) + >>> vm.series_match(test) + 0 True + 1 False + 2 True + 3 False + dtype: bool + + Simple inverse regex example. + >>> vm = ValueMatcher(values_inverse_regex = 'foo.*') + >>> test = pandas.Series(['foo', 'bar', 'foobar', 1]) + >>> vm.series_match(test) + 0 False + 1 True + 2 False + 3 False + dtype: bool + + Note that 1 is False in both examples. In general this was designed with strings in mind, so it + is recommended to convert all values to strings in any Series delivered to series_match. + It is also HIGHLY recommended to use the 'string[pyarrow]' dtype when using the regex attributes. + This dtype uses much faster regular expression algorithms and can make orders of magnitude speed + differences over Python's built-in regular expressions. There are some features of regular + expressions that cannot be used with the 'string[pyarrow]' dtype though. For example, lookahead + assertions. More information can be found at https://pypi.org/project/re2/. + + Attributes: + values_type: The current type of the values being matched. + values_regex: The regular expression to positively identify values in a column. + inverse_values_regex: The regular expression to positively exclude values in a column. + """ + + def __init__(self, values_type: None|str = None, values_regex: None|str = None, values_inverse_regex: None|str = None): + self.values_type = values_type if isinstance(values_type, str) else '' + self.values_regex = values_regex if isinstance(values_regex, str) else '' + self.values_inverse_regex = values_inverse_regex if isinstance(values_inverse_regex, str) else '' + + def series_match(self, series: pandas.Series, na_values: list|None = None, match_na_values: bool = True) -> pandas.Series: + """Return a mask for the series based on type and regex matching. + + "values_regex" and "values_inverse_regex" are mutually exclusive and "values_regex" will take precedence if both are given. + "values_type" and one of the regex parameters can both be used, the intermediate masks are ANDed together. + "values_type" can only be "integer", "numeric", or "non-numeric" to match those types, respectively. + + Args: + series: series to match values based on type and/or regex. + na_values: list of values to consider NA values. + match_na_values: if True, NA values will be consider a match and return True, False otherwise. + + Returns: + A pandas Series the same length as "series" with Boolean values that can be used to select the matching values in the series. + """ + if na_values is None: + na_values = [] + + stripped_series = series.str.strip() + stripped_series = stripped_series.str.strip('\u200e') + old_NAs = (stripped_series.isna()) | (stripped_series.isin(na_values)) + + if self.values_regex: + regex_match = stripped_series.str.fullmatch(self.values_regex, na=False) + elif self.values_inverse_regex: + regex_match = ~stripped_series.str.fullmatch(self.values_inverse_regex, na=True) + else: + regex_match = pandas.Series([True]*len(series), index=series.index) + + if match_na_values: + regex_match = regex_match | old_NAs + + + column_to_numeric = pandas.to_numeric(stripped_series, errors='coerce') + column_to_numeric_NAs = column_to_numeric.isna() + new_NAs = column_to_numeric_NAs ^ old_NAs + + if self.values_type == "integer": + # The top line will return True for values like '1.0', but the bottom line won't. + # type_match = (column_to_numeric % 1 == 0) & ~new_NAs + type_match = ~stripped_series.str.contains('.', regex=False, na=False) & ~new_NAs + elif self.values_type == "numeric": + type_match = ~new_NAs + elif self.values_type == "non-numeric": + type_match = new_NAs | old_NAs + else: + type_match = pandas.Series([True]*len(series), index=series.index) + + return regex_match & type_match + +class ColumnFinder: + """Used to find columns in a DataFrame that match a NameMatcher and values in the column that match a ValueMatcher. + + This is pretty much just a convenient way to keep the standard_name, NameMatcher, and ValueMatcher + together in a single object. Convenience methods to utilize the NameMatcher and ValueMatcher are + provided as name_dict_match and values_series_match, respectively. + + Parameters: + standard_name: A string to give a standard name to the column you are trying to find. + Not used by any methods. + name_matcher: The NameMatcher object used to match column names. + value_matcher: The ValueMatcher object used to match column values. + + Examples: + Basic usage. + + >>> df = pandas.DataFrame({'foo':[1, 2, 'asdf'], 'bar':[1, 2, 3]}) + >>> df + foo bar + 0 1 1 + 1 2 2 + 2 asdf 3 + >>> column_finder = ColumnFinder('FOO', NameMatcher(exact_strings = ['foo']), ValueMatcher(values_type = 'numeric')) + >>> modified_columns = {column_name: column_name.lower().strip() for column_name in df.columns} + >>> matching_columns = column_finder.name_dict_match(modified_columns) + >>> matched_column_name = matching_columns[0] + >>> matched_column_name + foo + >>> matching_values = column_finder.values_series_match(df.loc[:, matched_column_name]) + >>> matching_values + 0 True + 1 True + 2 False + dtype: bool + + Attributes: + standard_name: The standard name of the column trying to be found. + name_matcher: The NameMatcher object used to match column names. + value_matcher: The ValueMatcher object used to match column values. + """ + def __init__(self, standard_name: str, name_matcher: NameMatcher, value_matcher: ValueMatcher): + self.standard_name = standard_name + self.name_matcher = name_matcher + self.value_matcher = value_matcher + + def name_dict_match(self, name_map): + """Convenience method to use the dict_match method for name_matcher. + """ + return self.name_matcher.dict_match(name_map) + + def values_series_match(self, series, na_values = None, match_na_values = True): + """Convenience method to use the series_match method for value_matcher. + """ + return self.value_matcher.series_match(series, na_values, match_na_values) + + + + + + +def make_list_regex(element_regex: str, delimiter: str , quoted_elements: bool = False, empty_string: bool = False) -> str: + r"""Creates a regular expression that will match a list of element_regex delimited by delimiter. + + Note that delimiter can be a regular expression like (,|;) to match 2 different types of delimiters. + If quoted_elements is True, then allow element_regex to be surrounded by single or double quotes. + Note that this allows mixed elements, so quoted and unquoted elements are both allowed in the same list. + If empty_string is True, then the list regex will match a single element_regex and the empty string. + empty_string = True will actually match anything, but the length of the match for strings that + are not appropriate will be 0. So this parameter could be useful in some edge case scenarios, + but you must investigate the specific match more closely. If the match is the empty string, + but the given string is not itself the empty string, then it is not really a match. + + Args: + element_regex: A regular expression in the form of a string that matches the elements of the list to match. + delimiter: The character(s) that seperate list elements. + quoted_elements: If True, list elements can be surrounded by single or double quotes. + empty_string: If True, then allow the returned regular exression to match the empty string. + + Returns: + A regular expression in str form that will match a list of element_regexes delimited by delimiter. + + Examples: + Regular expression to match a list of 4 digit numbers. + + >>> regex = make_list_regex(r'\d\d\d\d', r',') + '((\\d\\d\\d\\d\\s*,\\s*)+(\\d\\d\\d\\d\\s*|\\s*))' + >>> bool(re.match(regex, '1234')) + False + >>> bool(re.match(regex, '1234, 5678')) + True + >>> bool(re.match(regex, '')) + False + + Allow the empty string. + + >>> regex = make_list_regex(r'\d\d\d\d', r',', empty_string = True) + '((\\d\\d\\d\\d\\s*,\\s*)*(\\d\\d\\d\\d\\s*|\\s*))' + >>> bool(re.match(regex, '1234')) + True + >>> bool(re.match(regex, '1234, 5678')) + True + >>> bool(re.match(regex, '')) + True + >>> bool(re.match(regex, 'asdf')) + True + >>> re.match(regex, 'asdf') + + + Allow numbers to be surrounded with quotation marks. + + >>> regex = make_list_regex(r'\d\d\d\d', r',', quoted_elements = True) + '(((\\d\\d\\d\\d\\s*,\\s*)+(\\d\\d\\d\\d\\s*|\\s*))|((\'\\d\\d\\d\\d\'\\s*,\\s*)+(\'\\d\\d\\d\\d\'\\s*|\\s*))|(("\\d\\d\\d\\d"\\s*,\\s*)+("\\d\\d\\d\\d"\\s*|\\s*)))' + >>> bool(re.match(regex, '1234')) + False + >>> bool(re.match(regex, '1234, 5678')) + True + >>> bool(re.match(regex, '1234, "5678"')) + True + >>> bool(re.match(regex, '\'1234\', "5678"')) + True + >>> bool(re.match(regex, '')) + False + """ + if quoted_elements: + # The simplest regex to add quotes around the element, but allows elements like 'element" through. + # element_regex = r'(\'|"|)' + element_regex + r'(\'|"|)' + # Forces an element to be surrounded by the same thing on both sides, but still allows for mixed elements. + # element_regex = r"('" + element_regex + r"'" + '|' + r'"' + element_regex + r'"' + '|' + element_regex + ')' + + # All elements must have the same type of quotation marks. + return '(' + make_list_regex(element_regex, delimiter, empty_string=empty_string) + '|' + \ + make_list_regex(f"'{element_regex}'", delimiter, empty_string=empty_string) + '|' + \ + make_list_regex(f'"{element_regex}"', delimiter, empty_string=empty_string) + ')' + + repetition_symbol = '*' if empty_string else '+' + + return r'((' + element_regex + r'\s*' + delimiter + r'\s*)' + repetition_symbol + '(' + element_regex + r'\s*|\s*))' + + + +# # Testing make_list_regex +# pass_values = ['a,', 'a,a', 'a,a,', 'a ,', 'a , a', 'a , a ,'] +# fail_values = ['a', ''] + +# # pass should pass and fail should fail +# regex = make_list_regex('a', ',') +# # pass should pass, but fail should also pass now. +# regex = make_list_regex('a', ',', False, True) + +# for value in pass_values: +# if not re.fullmatch(regex, value): +# print(value) + +# for i, value in enumerate(fail_values): +# if re.fullmatch(regex, value): +# print(i, value) + + +# pass_values = ['["a",]', '["a","a"]', '["a","a",]', '["a" ,]', '["a" , "a"]', '["a" , "a" ,]', '["a"]', '[]'] +# fail_values = [''] + +# regex = r'\[' + make_list_regex('a', ',', True, True) + r'\]' + + +# Comment for Sphinx to pull in regular expressions. +INTEGER = r'-?\d+' +FLOAT = r'-?\d*\.\d+' +SCIENTIFIC_NOTATION = r'-?\d*\.\d+E(-|\+)?\d+' +NUMS = '(' + FLOAT + '|' + SCIENTIFIC_NOTATION + '|' + INTEGER + ')' +NUM_RANGE = NUMS + r'(-|\sto\s|−)' + NUMS +LIST_OF_NUMS = make_list_regex(NUMS, ',') +BRACKETED_LIST_OF_NUMS = r'\[' + LIST_OF_NUMS + r'\]' +PARENTHESIZED_LIST_OF_NUMS = r'\(' + LIST_OF_NUMS + r'\)' +LIST_OF_NUMS_UNDERSCORE = make_list_regex(NUMS, '_') +LIST_OF_NUMS_SLASH = make_list_regex(NUMS, '/') +POSITIVE_NUMS = NUMS.replace('-?', '') +POSITIVE_INTS = r'\d+' +LIST_OF_POS_INTS = make_list_regex(POSITIVE_INTS, ',') +LIST_OF_POS_INTS_OR = make_list_regex(POSITIVE_INTS, 'or') +LIST_OF_POS_INTS_BAR = make_list_regex(POSITIVE_INTS, r'\|') +LIST_OF_POS_INTS_SLASH = make_list_regex(POSITIVE_INTS, '/') +LIST_OF_POS_INTS_SEMICOLON = make_list_regex(POSITIVE_INTS, ';') +LIST_OF_POS_INTS_SPACE = make_list_regex(POSITIVE_INTS, ' ') + +POSITIVE_FLOATS = r'\d*.\d+' +POSITIVE_SCIENTIFIC_NOTATION = r'\d*\.\d*E(-|\+)?\d+' +POSITIVE_FLOAT_RANGE = POSITIVE_FLOATS + r'\s*(_|-)\s*' + POSITIVE_FLOATS +LIST_OF_POS_FLOATS_UNDERSCORE = make_list_regex(POSITIVE_FLOATS, '_') +POS_FLOAT_PAIRS = '(' + POSITIVE_FLOATS + r'(_|@)' + POSITIVE_FLOATS + ')' +LIST_OF_POS_FLOAT_PAIRS_UNDERSCORE = make_list_regex(POS_FLOAT_PAIRS, '_') +LIST_OF_POS_FLOAT_PAIRS_NO_SPACE = make_list_regex(POS_FLOAT_PAIRS, '') +LIST_OF_POS_FLOAT_PAIRS_MIXED = make_list_regex(POS_FLOAT_PAIRS, '(//|,)') +POS_INT_FLOAT_PAIR = '(' + POSITIVE_INTS + r'_' + POSITIVE_FLOATS + ')' + +ELEMENT_SYMBOL = (r'([BCFHIKNOPSUVWY]|[ISZ][nr]|[ACELP][ru]|A[cglmst]|B[aehikr]|' + r'C[adeflos]|D[bsy]|Es|F[elmr]|G[ade]|H[efgos]|Kr|L[aiv]|M[cdgnot]|' + r'N[abdehiop]|O[gs]|P[abdmot]|R[abe-hnu]|S[bcegim]|T[abcehilms]|Xe|Yb)') +ELEMENT_COUNT = r'([1-9]\d*)*' +FORMULA_ELEMENT = ELEMENT_SYMBOL + ELEMENT_COUNT +FORMULA = '(' + FORMULA_ELEMENT + ')+' +LIST_OF_FORMULAS = make_list_regex(FORMULA, ',', True) +BRACKETED_LIST_OF_FORMULAS = r'\[' + LIST_OF_FORMULAS + r'\]' +# C12H22O11, C12 H22 O11, [13C]4H7NO4, [13]C6 H14 [15]N4 O2, [C13]C4H6O5, C21C(13)2H38N7O17P3S, 12C12+14N4+16O19+1H32 +ISOTOPIC_NUM = r'\d+' +# Add dueterium. +ISOTOPIC_SYMBOL = ELEMENT_SYMBOL[0:-1] + r'|D)' +ISOTOPIC_ELEMENT = ISOTOPIC_SYMBOL + ELEMENT_COUNT +ISOTOPIC_FORMULA = '(' + ISOTOPIC_ELEMENT + '|' + \ + r'\[' + ISOTOPIC_NUM + ISOTOPIC_SYMBOL + r'\]' + ELEMENT_COUNT + '|' + \ + r'\[' + ISOTOPIC_NUM + r'\]' + ISOTOPIC_ELEMENT + '|' + \ + r'\[' + ISOTOPIC_SYMBOL + ISOTOPIC_NUM + r'\]' + ELEMENT_COUNT + '|' + \ + ISOTOPIC_SYMBOL + r'\(' + ISOTOPIC_NUM + r'\)' + ELEMENT_COUNT + '|' + \ + make_list_regex(ISOTOPIC_NUM + ISOTOPIC_SYMBOL + ELEMENT_COUNT, r'\+') + ')+' +LIST_OF_ISOTOPIC_FORMULAS = make_list_regex(ISOTOPIC_FORMULA, ',', True) +BRACKETED_LIST_OF_ISOTOPIC_FORMULAS = r'\[' + LIST_OF_ISOTOPIC_FORMULAS + r'\]' +# C12H22O11+, [C12H22O11]+ +CHARGE_FORMULA = r'(\[' + FORMULA + r'\](-|\+)' + '|' + FORMULA + r'(-|\+)' + ')' +# CH3(CH2)16COOH +GROUP_FORMULA = '(' + FORMULA_ELEMENT + '|' + r'\(' + FORMULA + r'\)\d+' + r')+' + +ORGANIC_ELEMENT_SYMBOL = r'([CHNOPS])' +ORGANIC_FORMULA_ELEMENT = ORGANIC_ELEMENT_SYMBOL + ELEMENT_COUNT +ORGANIC_FORMULA = '(' + ORGANIC_FORMULA_ELEMENT + '){4,}' + +SMILES_ELEMENT_SYMBOL = r'\d?[a-zA-Z][a-z]?' +# Note the , , , and  symbols at the end of the character set are '\x01', 'x02', '\x03', and '\x04'. +# I don't think they are apart of the SMILES characters, but they appeared in the SMILES of some datasets. +# There are some SMILES in AN003143 like 'C[n]1c[n]cc1C[C@@H](NC(=O)CCN)C(O)=O |&1:7|'. +# I don't think the end bit between '|' is part of a legit SMILES, but I added it to pass this dataset. +SMILES = r'(\[?' + SMILES_ELEMENT_SYMBOL + r'[0-9@+\-[\]()\\/%=#$.*]*)+' + r'( \|[0-9:&,w]+\|)?' +LIST_OF_SMILES_SEMICOLON = make_list_regex(SMILES, ';') + +INCHIKEY = r'(InChIKey=)?[a-zA-Z]{14}-[a-zA-Z]{10}-[a-zA-Z]?' +INCHIKEY_OR_NULL = '(' + INCHIKEY + r'|null|No record)' +LIST_OF_INCHIKEYS = '(' + INCHIKEY + r'\s*,\s*)+' + '(' + INCHIKEY + r'\s*|\s*)' +LIST_OF_INCHIKEYS_SLASH = LIST_OF_INCHIKEYS.replace(',', '/') + +# InChI=1 or InChI=1 where "1" is a version number and "S" means it's a standard InChI. +INCHI_HEAD = r'InChI=' +INCHI_VERSION = r'\d+S?' +INCHI_FORMULA = r'/' + FORMULA +INCHI_SKELETAL_LAYER = r'/c(\d+([-,()])?)+' +INCHI_HYDROGEN_LAYER = r'/h' + '(' + make_list_regex(r'\d+(-\d+)?H?\d*', ',') + r')?' + r'(\(H\d*-?(,\d+)+\))*' +INCHI_CHARGE_LAYER = r'/q(-|\+)\d+' +INCHI_PROTONATION_LAYER = r'/p(-|\+)\d+' +INCHI_STEREOCHEMISTRY_LAYER = r'/t' + make_list_regex(r'(\d+(-|\+|\?|u)|M)', ',') +INCHI_STEREOCHEMISTRY_SUBLAYER1 = r'/m\d+' +INCHI_STEREOCHEMISTRY_SUBLAYER2 = r'/s\d+' +INCHI_FIXED_HYDROGEN_LAYER = r'/f(' + FORMULA + r')?' + \ + '(' + INCHI_HYDROGEN_LAYER + r')?' + \ + '(' + INCHI_STEREOCHEMISTRY_SUBLAYER2 + r')?' + \ + '(' + INCHI_CHARGE_LAYER + r')?' +INCHI_DOUBLEBOND_LAYER = r'/b' + make_list_regex(r'\d+(-|\+)\d+(-|\+)', ',') +INCHI_ISOTOPIC_LAYER = r'/i' + make_list_regex(r'(\d+(-|\+)\d+|\d+[A-Z]\d*|\d+(-|\+)\d+[A-Z]?\d*)', ',') + '(' + INCHI_STEREOCHEMISTRY_SUBLAYER2 + r')?' + +FULL_INCHI = '(' + INCHI_HEAD + r')?' + \ + INCHI_VERSION + \ + '(' + INCHI_FORMULA + r')?' + \ + '(' + INCHI_SKELETAL_LAYER + r')?' + \ + '(' + INCHI_HYDROGEN_LAYER + r')?' + \ + '(' + INCHI_CHARGE_LAYER + r')?' + \ + '(' + INCHI_PROTONATION_LAYER + r')?' + \ + '(' + INCHI_DOUBLEBOND_LAYER + r')?' + \ + '(' + INCHI_STEREOCHEMISTRY_LAYER + r')?' + \ + '(' + INCHI_STEREOCHEMISTRY_SUBLAYER1 + r')?' + \ + '(' + INCHI_STEREOCHEMISTRY_SUBLAYER2 + r')?' + \ + '(' + INCHI_ISOTOPIC_LAYER + r')?' + \ + '(' + INCHI_FIXED_HYDROGEN_LAYER + r')?' + +CHEAP_INCHI = r'\s*' + '(' + INCHI_HEAD + r')?' + INCHI_VERSION + r'((/c|/h|/i|/t|/m|/s|/q|/f|/p|/b|/[A-Z])(\S)+)+' + r'\s*' +LIST_OF_INCHI = make_list_regex(CHEAP_INCHI, ',', True) +BRACKETED_LIST_OF_INCHI = r'\[' + LIST_OF_INCHI + r'\]' + +# M+H, [M+H]+, -H(-), M+AGN+H, M+Acid, (M-H)/2, [M+NH4] +_[M+Na]+, [M+H–H2O]+, [M-2H](2-) +# Cat, Hac, and Chol-head are wierd special cases. +ION_ELEMENTS = r'(\d?(m|M)' + '|' + \ + r'(-|\+)?\d*' + FORMULA + r'\d*(\((-|\+)\))?' + '|' + \ + r'[ [a-zA-Z]*(A|a)cid' + '|' + \ + r'Cat' + '|' + r'Chol-head' + '|' + r'Hac' + '|' + r'\di' + '|' + r'FA|NA|A' + ')' +ION = '(' + make_list_regex(ION_ELEMENTS, r'(-|\+)?') + ')' +ION = '(' + ION + '|' + \ + r'\[' + ION + r'\]\(?\d?\s?(-|\+)?\d?\)?' + '|' + \ + ION + r'\](-|\+)?' + '|' + \ + r'\(' + ION + r'\)((/\d)|(-|\+))?' + ')' +LIST_OF_IONS = make_list_regex(ION, ',') +LIST_OF_IONS_SPACE = make_list_regex(ION, ' ') +LIST_OF_IONS_UNDERSCORE = make_list_regex(ION, '_') +LIST_OF_IONS_MIXED = make_list_regex(ION, '(_| )') +LIST_OF_IONS_NO_DELIMITER = make_list_regex(ION, '') +BRACKETED_LIST_OF_IONS = r'\[' + make_list_regex(ION, ',', True) + r'\]' + +# There are values like CA1511 that I can't confirm are KEGG values. You can't find them in the compound database, but they appear often. +# The prefixes CE, UP, Z, and U are the same. There is a common mistake of only having 4 numbers after C, so that is accounted for as well. +KEGG = '(' + r'(cpd:)?[CDMGKRZU]0?\d{5}\?{0,2}' + '|' + r'(DG|ko)\d{5}' + '|' + r'(CA|CE|UP|C)\d{4}' + '|' + r'(NA|n/a)' + ')' +LIST_OF_KEGG = make_list_regex(KEGG, ',') +LIST_OF_KEGG_SEMICOLON = make_list_regex(KEGG, ';') +LIST_OF_KEGG_SLASH = make_list_regex(KEGG, '/') +LIST_OF_KEGG_SPACE = make_list_regex(KEGG, ' ') +LIST_OF_KEGG_DOUBLE_SLASH = make_list_regex(KEGG, '//') +LIST_OF_KEGG_UNDERSCORE = make_list_regex(KEGG, '_') +LIST_OF_KEGG_HYPHEN = make_list_regex(KEGG, '-') +LIST_OF_KEGG_MIXED = make_list_regex(KEGG, '(/|,)') +LIST_OF_KEGG_BAR = make_list_regex(KEGG, r'(\|)') + +HMDB = '(' + r'(HMDB|HDMB|YMDB|HMBD)\d+(\*|\?)?' + '|' + r'n/a' + ')' +LIST_OF_HMDB = make_list_regex(HMDB, ',') +LIST_OF_HMDB_SLASH = make_list_regex(HMDB, '/') +LIST_OF_HMDB_AMPERSAND = make_list_regex(HMDB, '&') +LIST_OF_HMDB_SEMICOLON = make_list_regex(HMDB, ';') +LIST_OF_HMDB_SPACE = make_list_regex(HMDB, ' ') +LIST_OF_HMDB_UNDERSCORE = make_list_regex(HMDB, '_') +HMDB_INT = r'\d{,5}' +LIST_OF_HMDB_INTS = make_list_regex(HMDB_INT, ',') +LIST_OF_HMDB_INTS_SLASH = make_list_regex(HMDB_INT, '/') + +LIPID_MAPS = '(' + r'LM(PK|ST|GL|FA|SP|GP|PR|SL)[0-9A-Z]{8,10}\*?' + '|' + r'(ST|FA|PR|GP|PK|GL|SP)\d{4,6}-' + FORMULA + ')' +LIST_OF_LMP = make_list_regex(LIPID_MAPS, ',') +LIST_OF_LMP_UNDERSCORE = make_list_regex(LIPID_MAPS, '_') +LIST_OF_LMP_SLASH = make_list_regex(LIPID_MAPS, '/') + +# When using Excel, a CAS number can get mistaken for a date and it will automatically change the value. +DATE = r'\d{1,2}/\d{1,2}/(\d{4}|\d{2})' +CAS = r'(CAS: ?)?\d+-\d\d-0?\d' + '|' + DATE +LIST_OF_CAS = make_list_regex(CAS, ',') +LIST_OF_CAS_SEMICOLON = make_list_regex(CAS, ';') +# Comment for Sphinx to find the end of regular expressions. + + +# Important NOTE! All of the regular expressions delivered to ValueMatchers are surrounded with an additional set of +# parenthesis. This is because they are later used with the pyarrow integration in pandas, and the behavior is +# slightly different between python's built in regular expressions and pyarrow's. TLDR, add wrapping parenthesis +# to avoid problems matching using pyarrow. +column_finders = [ + ColumnFinder("moverz_quant", + NameMatcher(regex_search_strings = ['m/z', 'mz', 'moverz', 'mx'], + not_regex_search_strings = ['id'], + in_strings = ['m.z', 'calcmz', 'medmz', 'm_z', 'obsmz', 'mass to charge', 'mass over z'], + not_in_strings = ['spec', 'pectrum', 'structure', 'regno', 'retention'],), + ValueMatcher(values_regex = '(' + NUMS + '|' + \ + LIST_OF_NUMS + '|' + \ + LIST_OF_NUMS_UNDERSCORE + '|' + \ + NUMS + r'\s*/\s*' + NUMS + '|' + \ + '(' + NUMS + r'\s*\(' + NUMS + r'\)' + r'\s*;\s*)+' + '(' + NUMS + r'\s*\(' + NUMS + r'\)' + r'\s*|\s*)' + '|' + \ + NUMS + r'(\s*>\s*|\s*<\s*)' + NUMS + '|' + \ + '(' + NUMS + r'\s*)+' + '|' + \ + NUMS + r'\s*\(\s*' + NUMS + r'\s*\)' + '|' + \ + NUMS + r'\s*-\s*' + NUMS + ')',)), + + ColumnFinder("mass", + NameMatcher(regex_search_strings = ['mass', 'quantmass', 'masses', 'mw', 'weight'], + not_regex_search_strings = ['id'], + in_strings = ['exactmass', 'obsmass', 'calcmass', 'monoisotopicmass', 'molwt'], + not_in_strings = ['spec', 'pectrum', 'structure', 'regno', 'charge', 'over z', 'rsd', 'm/z'], + exact_strings = ['m meas.'],), + ValueMatcher(values_regex = '(' + NUMS + '|' + \ + LIST_OF_NUMS + '|' + \ + NUMS + r'\s*/\s*' + NUMS + r'(\s*Da)?|' + \ + NUMS + r'\s*-\s*' + NUMS + ')',)), + + ColumnFinder("parent_moverz_quant", + NameMatcher(exact_strings = ['parent'],), + ValueMatcher(values_type = 'numeric',)), + + ColumnFinder("mass_spectrum", + NameMatcher(in_strings = ['spec', 'pectrum'], + not_in_strings = ['species', 'composite'],), + ValueMatcher(values_regex = '(' + '(' + NUMS + ':' + NUMS + r'(_|\s+|$))+' + '|' + \ + NUMS + '|' + \ + LIST_OF_NUMS + ')',)), + + ColumnFinder("composite_mass_spectrum", + NameMatcher(in_string_sets = [['composite', 'spectrum']], + not_in_strings = ['species'],), + ValueMatcher(values_regex = '(' + '(' + PARENTHESIZED_LIST_OF_NUMS + r'\s*)+' + ')',)), + + ColumnFinder("inchi_key", + NameMatcher(in_strings = ['inchikey', 'inchi-key', 'inchi_key', 'inchi key'],), + ValueMatcher(values_regex = '(' + INCHIKEY + r'(\*?|\?*)' + '|' +\ + INCHIKEY_OR_NULL + r'(\s*or\s*|\s*;\s*|\s*_\s*|\s*&\s*)' + INCHIKEY_OR_NULL + '|' +\ + r'Sum\s*\(\s*' + INCHIKEY + r'(\s*\+\s*)' + INCHIKEY + r'\s*\)' + '|' +\ + LIST_OF_INCHIKEYS_SLASH + ')',)), + + ColumnFinder("inchi", + NameMatcher(in_strings = ['inchi'], + not_in_strings = ['key'],), + ValueMatcher(values_regex = '(' + CHEAP_INCHI + '|' + BRACKETED_LIST_OF_INCHI + ')',)), + + ColumnFinder("smiles", + NameMatcher(in_strings = ['smile'],), + ValueMatcher(values_regex = '(' + SMILES + '|' + LIST_OF_SMILES_SEMICOLON + ')',)), + + ColumnFinder("formula", + NameMatcher(in_strings = ['formula'],), + ValueMatcher(values_type = 'non-numeric', + values_regex = '(' + ISOTOPIC_FORMULA.replace(r'\d*', r'\d*\s*') + '|' + \ + CHARGE_FORMULA + '|' + \ + GROUP_FORMULA + '|' + \ + BRACKETED_LIST_OF_FORMULAS + '|' + \ + BRACKETED_LIST_OF_ISOTOPIC_FORMULAS + ')',)), + + # Metabolite is the first left-most column in every table for mwtab. This Finder will only create false positives. + # ColumnFinder("metabolite", + # NameMatcher(exact_strings = ['metabolite'],), + # ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("compound", + NameMatcher(in_strings = ['compound', 'compund'], + not_in_strings = ['kegg', 'formula', 'pubchem', 'mass', 'rt', 'algo', 'id', 'name'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("name", + NameMatcher(in_strings = ['name'], + in_string_sets = [['name', 'refmet']], + not_in_strings = ['adduct', 'named', 'internal', 'ion', 'metabolite_name'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("refmet", + NameMatcher(in_strings = ['refmet'], + not_in_strings = ['name', 'in'],), + ValueMatcher(values_regex = '(' + POSITIVE_INTS + ')',)), + + ColumnFinder("class", + NameMatcher(in_strings = ['class']), + ValueMatcher(values_type = 'non-numeric', + values_inverse_regex = '(' + ORGANIC_FORMULA + ')',)), + + ColumnFinder("pathway", + NameMatcher(in_strings = ['pathway'], + not_in_strings = ['sort'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("pathway_sortorder", + NameMatcher(in_string_sets = [['pathway', 'sort']],), + ValueMatcher(values_type = 'integer',)), + + ColumnFinder("ion", + NameMatcher(regex_search_strings = ['ion', 'ions'], + not_in_strings = ['adduct', 'm/z', 'mass'],), + ValueMatcher(values_regex = '(' + ION + '|' + \ + LIST_OF_IONS + '|' + \ + LIST_OF_IONS_SPACE + '|' + \ + NUMS + '|' + \ + LIST_OF_NUMS + '|' + \ + NUMS + r'(\s*>\s*|\s*<\s*)' + NUMS + '|' + \ + LIST_OF_NUMS_SLASH + ')',)), + + ColumnFinder("adduct", + NameMatcher(in_strings = ['adduct'], + not_in_strings = ['formula'],), + ValueMatcher(values_regex = '(' + ION + '|' + \ + LIST_OF_IONS + '|' + \ + LIST_OF_IONS_SPACE + '|' + \ + LIST_OF_IONS_UNDERSCORE + '|' + \ + LIST_OF_IONS_MIXED + '|' + \ + LIST_OF_IONS_NO_DELIMITER + '|' + \ + BRACKETED_LIST_OF_IONS + ')',)), + + ColumnFinder("species", + NameMatcher(in_strings = ['species'], + not_in_strings = ['is_species', 'ion'],), + ValueMatcher(values_regex = '(' + ION + '|' + \ + LIST_OF_IONS + '|' + \ + LIST_OF_IONS_UNDERSCORE + ')',)), + + ColumnFinder("pubchem_id", + NameMatcher(regex_search_strings = ['cid'], + in_strings = ['pubchem'], + not_in_strings = ['formula', 'kegg'],), + ValueMatcher(values_regex = '(' + POSITIVE_INTS + r'[&?]?' + '|' + \ + LIST_OF_POS_INTS + '|' + \ + LIST_OF_POS_INTS_OR + '|' + \ + LIST_OF_POS_INTS_SLASH + '|' + \ + LIST_OF_POS_INTS_SPACE + '|' + \ + LIST_OF_POS_INTS_SEMICOLON + '|' + \ + r'Sum \(\d+ \+ \d+\)' + '|' + \ + r'CID' + POSITIVE_INTS + ')',)), + + ColumnFinder("kegg_id", + NameMatcher(in_strings = ['kegg'], + not_in_strings = ['name'],), + ValueMatcher(values_regex = '(' + KEGG + '|' + \ + LIST_OF_KEGG + '|' + \ + LIST_OF_KEGG_SEMICOLON + '|' + \ + LIST_OF_KEGG_SLASH + '|' + \ + LIST_OF_KEGG_DOUBLE_SLASH + '|' + \ + LIST_OF_KEGG_UNDERSCORE + '|' + \ + LIST_OF_KEGG_HYPHEN + '|' + \ + LIST_OF_KEGG_MIXED + '|' + \ + LIST_OF_KEGG_SPACE + '|' + \ + LIST_OF_KEGG_BAR + '|' + \ + KEGG + r'-' + FORMULA + '|' + \ + KEGG + r';\d+' + ')',)), + + ColumnFinder("hmdb_id", + NameMatcher(in_strings = ['hmdb', 'human metabolome'], + in_string_sets = [['hmp', 'id']], + not_in_strings = ['class'],), + ValueMatcher(values_regex = '(' + HMDB + '|' + \ + HMDB_INT + '|' + \ + LIST_OF_HMDB + '|' + \ + LIST_OF_HMDB_SLASH + '|' + \ + LIST_OF_HMDB_INTS + '|' + \ + LIST_OF_HMDB_INTS_SLASH + '|' + \ + r'Sum \(HMDB\d+ \+ HMDB\d+\)' + '|' + \ + LIST_OF_HMDB_AMPERSAND + '|' + \ + LIST_OF_HMDB_SEMICOLON + '|' + \ + LIST_OF_HMDB_SPACE + '|' + \ + r'METPA\d+' + '|' + \ + LIST_OF_HMDB_UNDERSCORE + ')',)), + + ColumnFinder("lm_id", + NameMatcher(in_strings = ['lipidmaps', 'lmid'], + in_string_sets = [['lmp', 'id'], ['lipid', 'map'], ['lm', 'id']],), + ValueMatcher(values_regex = '(' + LIPID_MAPS + '|' + \ + LIST_OF_LMP_UNDERSCORE + '|' + \ + LIST_OF_LMP + '|' + \ + LIST_OF_LMP_SLASH + '|' + \ + POSITIVE_INTS + ')',)), + + ColumnFinder("chemspider_id", + NameMatcher(in_strings = ['chemspider'],), + ValueMatcher(values_regex = '(' + POSITIVE_INTS + r'[&?]?' + '|' + \ + LIST_OF_POS_INTS + '|' + \ + LIST_OF_POS_INTS_OR + '|' + \ + LIST_OF_POS_INTS_SLASH + '|' + \ + LIST_OF_POS_INTS_SPACE + '|' + \ + LIST_OF_POS_INTS_SEMICOLON + '|' + \ + r'Sum \(\d+ \+ \d+\)' + '|' + \ + 'CID' + POSITIVE_INTS + '|' + \ + 'CSID' + POSITIVE_INTS + '|' + \ + r'[CD]\d{5}' + '|' + \ + make_list_regex(r'[CD]\d{5}', r'\|') + ')',)), + + ColumnFinder("metlin_id", + NameMatcher(in_strings = ['metlin'],), + ValueMatcher(values_regex = '(' + POSITIVE_INTS + '|' + r'METLIN:' + POSITIVE_INTS + ')',)), + + ColumnFinder("cas_number", + NameMatcher(regex_search_strings = ['cas'],), + ValueMatcher(values_regex = '(' + CAS + '|' + LIST_OF_CAS + '|' + LIST_OF_CAS_SEMICOLON + ')',)), + + ColumnFinder("binbase_id", + NameMatcher(regex_search_strings = ['bb'], + in_strings = ['binbase'],), + ValueMatcher(values_regex = '(' + POSITIVE_INTS + ')',)), + + ColumnFinder("chebi_id", + NameMatcher(in_strings = ['chebi'],), + ValueMatcher(values_regex = '(' + POSITIVE_INTS + ')',)), + + ColumnFinder("mw_regno", + NameMatcher(in_strings = ['regno'], + in_string_sets = [['mw', 'structure']],), + ValueMatcher(values_regex = '(' + POSITIVE_INTS + '|' + LIST_OF_POS_INTS + ')',)), + + ColumnFinder("mzcloud_id", + NameMatcher(in_string_sets = [['mz', 'cloud', 'id']],), + ValueMatcher(values_regex = '(' + r'((Reference|Autoprocessed)-)?' + POSITIVE_INTS + ')',)), + + ColumnFinder("identifier", + NameMatcher(in_strings = ['identifier', 'retention time_m/z', 'feature@rt'], + not_in_strings = ['pubchem', 'study', 'database'],), + ValueMatcher(values_regex = '(' + POS_FLOAT_PAIRS + r'(n|m/z)?' + '|' + \ + POS_INT_FLOAT_PAIR + '|' + \ + LIST_OF_POS_FLOAT_PAIRS_UNDERSCORE + '|' + \ + LIST_OF_POS_FLOAT_PAIRS_NO_SPACE + '|' + \ + LIST_OF_POS_FLOAT_PAIRS_MIXED + '|' + \ + r'CHEBI:\d+' + ')',)), + + ColumnFinder("other_id", + NameMatcher(not_regex_search_strings = ['cas'], + in_strings = ['other'], + in_string_sets = [['database', 'identifier'], ['chemical', 'id'], ['cmpd', 'id'], ['database', 'id'], ['database', 'match'], ['local', 'id'], ['row', 'id'], ['comp', 'id'], ['chem', 'id'], ['chro', 'lib', 'id'], ['lib', 'id']], + not_in_strings = ['type', 'pubchem', 'chemspider', 'kegg'], + exact_strings = ['id'],), + ValueMatcher(values_inverse_regex = '(' + FLOAT + '|' + SCIENTIFIC_NOTATION + ')',)), + + ColumnFinder("other_id_type", + NameMatcher(in_string_sets = [['other', 'type'], ['source', 'database']],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("retention_time", + NameMatcher(regex_search_strings = ['rt'], + regex_search_sets = [['ret', 'time']], + in_strings = ['rtimes', 'r.t.', 'medrt', 'rtsec', 'bestrt', 'compoundrt', 'rtmed'], + in_string_sets = [['retention', 'time'], ['rentetion', 'time'], ['retension', 'time']], + not_in_strings = ['type', 'error', 'index', 'delta', 'feature', 'm/z'],), + ValueMatcher(values_regex = '(' + POSITIVE_FLOATS + '|' + \ + r'\d' + '|' + \ + POSITIVE_SCIENTIFIC_NOTATION + '|' + \ + POSITIVE_FLOAT_RANGE + '|' + \ + LIST_OF_POS_FLOATS_UNDERSCORE + ')',)), + + ColumnFinder("delta_rt", + NameMatcher(in_strings = ['deltart'], + in_string_sets = [['delta', 'rt']], + not_in_strings = ['type', 'error', 'index'],), + ValueMatcher(values_regex = FLOAT + '|' + r'0',)), + + ColumnFinder("retention_index", + NameMatcher(regex_search_strings = ['ri'], + regex_search_sets = [['ret', 'ind'], ['ret', 'index']], + in_strings = ['rindex'], + in_string_sets = [['retention', 'index'], ['rentetion', 'index'], ['reten', 'index']], + not_in_strings = ['type', 'error'],), + ValueMatcher(values_regex = '(' + POSITIVE_FLOATS + '|' + \ + r'\d' + '|' + \ + POSITIVE_SCIENTIFIC_NOTATION + '|' + \ + POSITIVE_FLOAT_RANGE + '|' + \ + LIST_OF_POS_FLOATS_UNDERSCORE + ')',)), + + ColumnFinder("retention_index_type", + NameMatcher(in_string_sets = [['retention', 'index', 'type'], ['ri', 'type']], + not_in_strings = ['error'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("abbreviation", + NameMatcher(in_strings = ['abbreviation'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("assignment_certainty", + NameMatcher(in_string_sets = [['assignment', 'certainty']],), + ValueMatcher(values_regex = '(' + POSITIVE_INTS + ')',)), + + ColumnFinder("comment", + NameMatcher(in_strings = ['comment'],), + ValueMatcher(values_inverse_regex = '(' + FLOAT + '|' + SCIENTIFIC_NOTATION + '|' + r'\d{2,}' + ')',)), + + ColumnFinder("assignment_method", + NameMatcher(in_string_sets = [['assignment', 'method']],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("isotopologue", + NameMatcher(in_strings = ['isotopologue'], + in_string_sets = [['isotope', 'count']], + not_in_strings = ['type'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("isotopologue_type", + NameMatcher(in_strings = ['isotopologue%type', 'isotope'], + not_in_strings = ['count'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("peak_description", + NameMatcher(in_string_sets = [['peak', 'description']],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("peak_pattern", + NameMatcher(in_string_sets = [['peak', 'pattern']],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("transient_peak", + NameMatcher(in_string_sets = [['transient', 'peak']], + not_in_strings = ['type'],), + ValueMatcher(values_regex = '(' + POSITIVE_INTS + ')',)), + + ColumnFinder("transient_peak_type", + NameMatcher(in_string_sets = [['transient', 'peak', 'type']],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("fish_coverage", + NameMatcher(in_string_sets = [['fish', 'coverage']],), + ValueMatcher(values_regex = '(' + POSITIVE_NUMS + ')',)), + + ColumnFinder("msi_category", + NameMatcher(in_strings = ['msicategory'],), + ValueMatcher(values_regex = r'1',)), + + ColumnFinder("annotations", + NameMatcher(not_regex_search_strings = ['id'], + in_strings = ['annotation'], + not_in_strings = ['source', 'approach', 'confidence', 'level'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("istd", + NameMatcher(in_strings = ['internal'], + exact_strings = ['istd'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("platform", + NameMatcher(in_strings = ['platform'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("ms_method", + NameMatcher(in_strings = ['method'], + not_in_strings = ['assignment'],), + ValueMatcher(values_type = 'non-numeric',)), + + ColumnFinder("polarity", + NameMatcher(in_strings = ['polarity'],), + ValueMatcher(values_regex = r'(?i)((neg|pos|1|-1|\+|positive|negative)' + r'|\[M\+H\]\+|\[M-H\]-|5MM\+|5MM-)',)), + + ColumnFinder("esi_mode", + NameMatcher(in_strings = ['esi'],), + ValueMatcher(values_regex = r'(neg|pos|1|-1|(ESI )?\(\+\)( ESI)?|(ESI )?\(-\)( ESI| ES\))?|positive|negative)',)), + + ColumnFinder("ionization_mode", + NameMatcher(regex_search_sets = [['pos', 'neg']], + in_strings = ['ionization', 'ionisation'], + not_in_strings = ['confirmed'], + exact_strings = ['mode', 'ms mode'],), + ValueMatcher(values_regex = r'(?i)((neg|pos|1|-1|(ES)?\+|(ES)?-|positive|negative|TOF|Splitless|Split30))',)), + + ColumnFinder("frequency", + NameMatcher(in_strings = ['frequency'],), + ValueMatcher(values_regex = '(' + POSITIVE_INTS + ')',)), +] + +column_finders = {finder.standard_name: finder for finder in column_finders} + + +implied_pairs = {'other_id': ['other_id_type'], 'retention_index': ['retention_index_type']} + + diff --git a/mwtab/mwextract.py b/src/mwtab/mwextract.py old mode 100755 new mode 100644 similarity index 83% rename from mwtab/mwextract.py rename to src/mwtab/mwextract.py index 2ec9ff6..e03f245 --- a/mwtab/mwextract.py +++ b/src/mwtab/mwextract.py @@ -3,7 +3,7 @@ """ mwtab.mwextract -~~~~~~~~~~~ +~~~~~~~~~~~~~~~ This module provides a number of functions and classes for extracting and saving data and metadata stored in ``mwTab`` formatted files in the form of :class:`~mwtab.mwtab.MWTabFile`. @@ -12,6 +12,10 @@ import json import os import re +import traceback +import sys + +from mwtab import fileio class ItemMatcher(object): @@ -97,28 +101,35 @@ def generate_matchers(items): yield ItemMatcher(item[0], item[1]) -def extract_metabolites(sources, matchers): +def extract_metabolites(sources, matcher_generator): """Extract metabolite data from ``mwTab`` formatted files in the form of :class:`~mwtab.mwtab.MWTabFile`. :param generator sources: Generator of mwtab file objects (:class:`~mwtab.mwtab.MWTabFile`). - :param generator matchers: Generator of matcher objects (:class:`~mwtab.mwextract.ItemMatcher` or - :class:`~mwtab.mwextract.ReGeXMatcher`). + :param generator matcher_generator: Generator of matcher objects (:class:`~mwtab.mwextract.ItemMatcher` or + :class:`~mwtab.mwextract.ReGeXMatcher`). :return: Extracted metabolites dictionary. :rtype: :py:class:`dict` """ + matchers = [matcher for matcher in matcher_generator] metabolites = dict() - for mwtabfile in sources: - if all(matcher(mwtabfile) for matcher in matchers): - data_section_key = list(set(mwtabfile.keys()) & {"MS_METABOLITE_DATA", "NMR_METABOLITE_DATA", "NMR_BINNED_DATA"})[0] - for data_list in mwtabfile[data_section_key]["Data"]: - for test_key in (key for key in data_list.keys() if key != "Metabolite"): + for mwtabfile, e in sources: + if e is not None: + file_source = mwtabfile if isinstance(mwtabfile, str) else "from the given input." + print("Something went wrong when trying to read " + file_source) + traceback.print_exception(e, file=sys.stdout) + print() + continue + if all([matcher(mwtabfile) for matcher in matchers]): + data_section_key = mwtabfile.data_section_key + for data_dict in mwtabfile[data_section_key]["Data"]: + for test_key in (key for key in data_dict.keys() if key != "Metabolite"): try: - if float(data_list[test_key]) > 0: - metabolites.setdefault(data_list["Metabolite"], dict())\ + if float(data_dict[test_key]) > 0: + metabolites.setdefault(data_dict["Metabolite"], dict())\ .setdefault(mwtabfile.study_id, dict())\ .setdefault(mwtabfile.analysis_id, set())\ .add(test_key) - except Exception as e: + except Exception: pass return metabolites @@ -155,10 +166,7 @@ def write_metadata_csv(to_path, extracted_values, no_header=False): :return: None :rtype: :py:obj:`None` """ - if not os.path.exists(os.path.dirname(os.path.splitext(to_path)[0])): - dirname = os.path.dirname(to_path) - if dirname: - os.makedirs(dirname) + fileio._create_save_path(to_path) if not os.path.splitext(to_path)[1]: to_path += ".csv" @@ -207,10 +215,7 @@ def write_metabolites_csv(to_path, extracted_values, no_header=False): num_samples ]) - if not os.path.exists(os.path.dirname(os.path.splitext(to_path)[0])): - dirname = os.path.dirname(to_path) - if dirname: - os.makedirs(dirname) + fileio._create_save_path(to_path) if not os.path.splitext(to_path)[1]: to_path += ".csv" @@ -246,35 +251,34 @@ def default(self, obj): def write_json(to_path, extracted_dict): """Write extracted data or metadata :py:class:`dict` into json file. - Metabolites example: - { - "1,2,4-benzenetriol": { - "ST000001": { - "AN000001": [ - "LabF_115816", - ... - ] + .. code-block:: text + + Metabolites example: + { + "1,2,4-benzenetriol": { + "ST000001": { + "AN000001": [ + "LabF_115816", + ... + ] + } } } - } - - Metadata example: - { - "SUBJECT_TYPE": [ - "Plant", - "Human" - ] - } + + Metadata example: + { + "SUBJECT_TYPE": [ + "Plant", + "Human" + ] + } :param str to_path: Path to output file. :param dict extracted_dict: Metabolites data or metadata dictionary to be saved. :return: None :rtype: :py:obj:`None` """ - if not os.path.exists(os.path.dirname(os.path.splitext(to_path)[0])): - dirname = os.path.dirname(to_path) - if dirname: - os.makedirs(dirname) + fileio._create_save_path(to_path) if not os.path.splitext(to_path)[1]: to_path += ".json" diff --git a/mwtab/mwrest.py b/src/mwtab/mwrest.py old mode 100755 new mode 100644 similarity index 87% rename from mwtab/mwrest.py rename to src/mwtab/mwrest.py index 57681e5..e0463e5 --- a/mwtab/mwrest.py +++ b/src/mwtab/mwrest.py @@ -70,7 +70,7 @@ def _pull_study_analysis(base_url=BASE_URL): {"context": "study", "input_item": "study_id", "input_value": "ST", "output_item": "analysis"}, base_url ).url - mwrestfile = next(fileio.read_mwrest(url, **{"convertJSON": True})) + mwrestfile = next(fileio.read_mwrest(url)) json_object = mwrestfile._is_json(mwrestfile.text) study_analysis_dict = dict() @@ -83,7 +83,7 @@ def _pull_study_analysis(base_url=BASE_URL): return study_analysis_dict -def generate_mwtab_urls(input_items, base_url=BASE_URL, output_format='txt'): +def generate_mwtab_urls(input_items, base_url=BASE_URL, output_format='txt', return_exceptions=False): """ Method for generating URLS to be used to retrieve `mwtab` files for analyses and studies through the REST API of the Metabolomics Workbench database. @@ -91,51 +91,75 @@ def generate_mwtab_urls(input_items, base_url=BASE_URL, output_format='txt'): :param list input_items: List of Metabolomics Workbench input values for mwTab files. :param str base_url: Base url to Metabolomics Workbench REST API. :param str output_format: Output format for the mwTab files to be retrieved in. + :param bool return_exceptions: Whether to yield a tuple with url and exception or just the url. :return: Metabolomics Workbench REST URL string(s). :rtype: :py:class:`str` """ for input_item in input_items: - if input_item.isdigit(): - analysis_id = "AN{}".format(input_item.zfill(6)) - yield GenericMWURL({ - "context": "study", - "input_item": "analysis_id", - "input_value": analysis_id, - "output_item": "mwtab", - "output_format": output_format - }, base_url).url - elif re.match(r'(AN[0-9]{6}$)', input_item): - yield GenericMWURL({ - "context": "study", - "input_item": "analysis_id", - "input_value": input_item, - "output_item": "mwtab", - "output_format": output_format - }, base_url).url - elif re.match(r'(ST[0-9]{1,6}$)', input_item): - yield GenericMWURL({ - "context": "study", - "input_item": "study_id", - "input_value": input_item, - "output_item": "mwtab", - "output_format": output_format - }, base_url).url - - -def generate_urls(input_items, base_url=BASE_URL, **kwds): - """ - Method for creating a generator which yields validated Metabolomics Workbench REST urls. - - :param list input_items: List of Metabolomics Workbench input values for mwTab files. - :param str base_url: Base url to Metabolomics Workbench REST API. - :param dict kwds: Keyword arguments of Metabolomics Workbench URL Path items. - :return: Metabolomics Workbench REST URL string(s). - :rtype: :py:class:`str` - """ - for input_item in input_items: - params = dict(kwds) - params["input_item"] = input_item - yield GenericMWURL(params, base_url).url + try: + if input_item.isdigit(): + analysis_id = "AN{}".format(input_item.zfill(6)) + yield fileio._return_correct_yield( + GenericMWURL({ + "context": "study", + "input_item": "analysis_id", + "input_value": analysis_id, + "output_item": "mwtab", + "output_format": output_format + }, base_url).url, + exception=None, + return_exceptions=return_exceptions) + elif re.match(r'(AN[0-9]{6}$)', input_item): + yield fileio._return_correct_yield( + GenericMWURL({ + "context": "study", + "input_item": "analysis_id", + "input_value": input_item, + "output_item": "mwtab", + "output_format": output_format + }, base_url).url, + exception=None, + return_exceptions=return_exceptions) + elif re.match(r'(ST[0-9]{1,6}$)', input_item): + yield fileio._return_correct_yield( + GenericMWURL({ + "context": "study", + "input_item": "study_id", + "input_value": input_item, + "output_item": "mwtab", + "output_format": output_format + }, base_url).url, + exception=None, + return_exceptions=return_exceptions) + except Exception as e: + yield fileio._return_correct_yield(None, + exception=e, + return_exceptions=return_exceptions) + + +# Unused function. Leaving here for now. +# def generate_urls(input_items, base_url=BASE_URL, return_exceptions=False, **kwds): +# """ +# Method for creating a generator which yields validated Metabolomics Workbench REST urls. + +# :param list input_items: List of Metabolomics Workbench input values for mwTab files. +# :param str base_url: Base url to Metabolomics Workbench REST API. +# :param bool return_exceptions: Whether to yield a tuple with url and exception or just the url. +# :param dict kwds: Keyword arguments of Metabolomics Workbench URL Path items. +# :return: Metabolomics Workbench REST URL string(s). +# :rtype: :py:class:`str` +# """ +# for input_item in input_items: +# try: +# params = dict(kwds) +# params["input_item"] = input_item +# yield fileio._return_correct_yield(GenericMWURL(params, base_url).url, +# exception=None, +# return_exceptions=return_exceptions) +# except Exception as e: +# yield fileio._return_correct_yield(None, +# exception=e, +# return_exceptions=return_exceptions) class GenericMWURL(object): @@ -143,7 +167,9 @@ class GenericMWURL(object): Metabolomics REST API requests are performed using URL requests in the form of https://www.metabolomicsworkbench.org/rest/context/input_specification/output_specification - + + .. code-block:: text + where: if context = "study" | "compound" | "refmet" | "gene" | "protein" input_specification = input_item/input_value @@ -162,7 +188,6 @@ class GenericMWURL(object): input_value1 = LIPID_abbreviation input_value2 = ion_type_value output_specification = None - """ context = { "study": { @@ -263,7 +288,7 @@ def _validate(self): :rtype: :py:obj:`None` """ if not self.rest_params["context"] in self.context.keys(): - raise KeyError("Error: Invalid/missing context") + raise ValueError("Error: Invalid/missing context") elif self.rest_params["context"] in {"study", "compound", "refmet", "gene", "protein"}: self._validate_generic() elif self.rest_params["context"] == "moverz": @@ -552,8 +577,8 @@ def _is_json(string): :param string: Input string. :type string: :py:class:`str` or :py:class:`bytes` - :return: Input string if in JSON format or False otherwise. - :rtype: :py:class:`str` or :py:obj:`False` + :return: string parsed into a JSON object or False if there was an error. + :rtype: :py:class:`str` or :py:class:`dict` or :py:class:`list` or :py:class:`None` or :py:obj:`False` """ try: if isinstance(string, bytes): diff --git a/src/mwtab/mwschema.py b/src/mwtab/mwschema.py new file mode 100644 index 0000000..2cbcddf --- /dev/null +++ b/src/mwtab/mwschema.py @@ -0,0 +1,671 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +mwtab.mwschema +~~~~~~~~~~~~~~ + +This module provides schema definitions for different sections of the +``mwTab`` Metabolomics Workbench format. +""" + +from copy import deepcopy + +from . import metadata_column_matching + + + +NA_VALUES = ['', '-', '−', '--', '---', + 'NA', 'na', 'n.a.', 'N.A.', 'n/a', 'N/A', '#N/A', 'NaN', 'nan', + 'null', 'Null', 'NULL', 'none', 'None', + 'unspecified', 'Unspecified'] +# 'NA' is a legitimate metabolite name that very rarely shows up. +METABOLITE_NA_VALUES = [value for value in NA_VALUES if value != 'NA'] + + +def create_units_regex(units: list[str], can_be_range: bool = False) -> str: + """Create a regular expression to match something like '5 V' or '5-6 V'. + + Regular expression will match something like '5 V'. The space is required, '5V' + will not match. If can_be_range is True, then the expression will also match + something like '5-7 V' (hyphen), '5−7 V' (em dash), and '5 to 7 V' as well. + The spacing must match. + + Args: + units: The list of unit strings that could follow the number. + can_be_range: If True, allow a pattern like '5-7 V' with a range. + + Returns: + A regular expression to match a number and units based on the arguments. + """ + if can_be_range: + regex = '^(' + metadata_column_matching.NUM_RANGE + '|' + metadata_column_matching.NUMS + ')' + f' ({"|".join(units)})$' + else: + regex = '^' + metadata_column_matching.NUMS + f' ({"|".join(units)})$' + return regex + +def create_unit_error_message(can_be_range: bool = False, + no_units: bool = False, + units: list[str]|None = None, + integer: bool = False) -> str: + """Generate the error message for mwTab subsections that fail the unit regex. + + Args: + can_be_range: If True, the regular expression used to validate could have matched a number range, so the message is modified to note that. + no_units: If True, the regular expression did not require units to be present, so the message is modified to note that. + units: If the regular expression required units, pass them in with this parameter so the message will indicate the allowed units. + integer: If True, only integers are allowed so the message will refer to intergers instead of numbers. + + Returns: + A completed string error message. + """ + if can_be_range: + range_string = ' or range (ex. "5-6") ' + else: + range_string = '' if no_units else ' ' + + if no_units: + unit_string = '.' + else: + unit_string = f'followed by a space with a unit (ex. "5 V") from the following list: {units}.' + + if integer: + number = 'integer' + else: + number = 'number' + + message = (f' should be a {"unitless " if no_units else ""}{number}{range_string}{unit_string} ' + 'Ignore this when more complicated descriptions are required.') + return message + +def _create_unit_regex_and_message(units: list[str], can_be_range: bool = False) -> str: + """Simple wrapper for DRY purposes. + + Creating the regular expression and validation error message in 1 function mixes too many + concerns into 1 function, so they are split into 2 and this function serves as a convenience + to pass them into a jsonschema easily. To this end the return is in dictionary form with the + intention for it to be unpacked into the jsonschema. For example, + {**_create_unit_regex_and_message(['V'], True)} + + Args: + units: A list of strings used to create the regex and error message. + can_be_range: If true, the regex will match a number range and the message will be slightly different. + + Returns: + A dicitonary {'pattern': regex, 'message_func': message_function}. + """ + regex = create_units_regex(units, can_be_range) + message = create_unit_error_message(can_be_range = can_be_range, no_units = False, units = units) + return {'pattern': regex, 'pattern_custom_message': message} + +def _create_num_regex_and_message(can_be_range: bool = False) -> str: + """Simple wrapper for DRY purposes. + + Creating the regular expression and validation error message in 1 function mixes too many + concerns into 1 function, so they are split into 2 and this function serves as a convenience + to pass them into a jsonschema easily. To this end the return is in dictionary form with the + intention for it to be unpacked into the jsonschema. For example, + {**_create_num_regex_and_message(True)} + + Args: + can_be_range: If true, the regex will match a number range and the message will be slightly different. + + Returns: + A dicitonary {'pattern': regex, 'message_func': message_function}. + """ + regex = r'^((\d+)|(\d*\.\d+))$' + message = create_unit_error_message(can_be_range = can_be_range, no_units = True, units = None) + return {'pattern': regex, 'pattern_custom_message': message} + +def _create_int_regex_and_message(can_be_range: bool = False) -> str: + """Simple wrapper for DRY purposes. + + Creating the regular expression and validation error message in 1 function mixes too many + concerns into 1 function, so they are split into 2 and this function serves as a convenience + to pass them into a jsonschema easily. To this end the return is in dictionary form with the + intention for it to be unpacked into the jsonschema. For example, + {**_create_num_regex_and_message(True)} + + Args: + can_be_range: If true, the regex will match an integer range and the message will be slightly different. + + Returns: + A dicitonary {'pattern': regex, 'message_func': message_function}. + """ + regex = r'^\d+$' + message = create_unit_error_message(can_be_range = can_be_range, no_units = True, units = None) + return {'pattern': regex, 'pattern_custom_message': message} + + + + +ID_error_template = ' must be the letters "{abbrev}" followed by 6 numbers. Ex. "{abbrev}001405".' +numeric_error_template = ' must be a positive integer. Ex. "1" or "2051".' +metabolomics_workbench_schema = \ +{'type': 'object', + 'properties': {'STUDY_ID': {'type': 'string', 'pattern': r'^ST\d{6}$', 'pattern_custom_message': ID_error_template.format(abbrev = 'ST')}, + 'ANALYSIS_ID': {'type': 'string', 'pattern': r'^AN\d{6}$', 'pattern_custom_message': ID_error_template.format(abbrev = 'AN')}, + 'VERSION': {'type': 'string', 'pattern': r'^\d+$', 'pattern_custom_message': numeric_error_template}, + 'CREATED_ON': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PROJECT_ID': {'type': 'string', 'pattern': r'^PR\d{6}$', 'pattern_custom_message': ID_error_template.format(abbrev = 'PR')}, + 'HEADER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'DATATRACK_ID': {'type': 'string', 'pattern': r'^\d+$', 'pattern_custom_message': numeric_error_template}, + 'filename': {'type': 'string'}}, + 'required': ['VERSION', 'CREATED_ON'], + 'additionalProperties': False} + +project_schema = \ +{'type': 'object', + 'properties': {'PROJECT_TITLE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PROJECT_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PROJECT_SUMMARY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'INSTITUTE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'DEPARTMENT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'LABORATORY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'LAST_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'FIRST_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ADDRESS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'EMAIL': {'type': 'string', 'format': 'email'}, + 'PHONE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'FUNDING_SOURCE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PROJECT_COMMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PUBLICATIONS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CONTRIBUTORS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'DOI': {'type': 'string', 'pattern': r'10\.\d{4,9}/[-._;()/:a-z0-9A-Z]+', + 'pattern_custom_message': ' does not appear to be a valid DOI.'}}, + 'required': ['PROJECT_TITLE', + 'PROJECT_SUMMARY', + 'INSTITUTE', + 'LAST_NAME', + 'FIRST_NAME', + 'ADDRESS', + 'EMAIL', + 'PHONE'], + 'additionalProperties': False} + +study_schema = \ +{'type': 'object', + 'properties': {'STUDY_TITLE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'STUDY_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'STUDY_SUMMARY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'INSTITUTE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'DEPARTMENT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'LABORATORY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'LAST_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'FIRST_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ADDRESS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'EMAIL': {'type': 'string', 'format': 'email'}, + 'PHONE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SUBMIT_DATE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NUM_GROUPS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TOTAL_SUBJECTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NUM_MALES': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NUM_FEMALES': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'STUDY_COMMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PUBLICATIONS': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + 'required': ['STUDY_TITLE', + 'STUDY_SUMMARY', + 'INSTITUTE', + 'LAST_NAME', + 'FIRST_NAME', + 'ADDRESS', + 'EMAIL', + 'PHONE'], + 'additionalProperties': False} + +subject_schema = \ +{'type': 'object', + 'properties': {'SUBJECT_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SUBJECT_SPECIES': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TAXONOMY_ID': {'type': 'string', + 'pattern': metadata_column_matching.make_list_regex(r'\d+', r'(,|;|\||/)', empty_string = True), + 'pattern_custom_message': ' must be a number or list of numbers.', + 'not':{'enum': NA_VALUES}}, + 'GENOTYPE_STRAIN': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'AGE_OR_AGE_RANGE': {'type': 'string', **_create_unit_regex_and_message(['weeks', 'days', 'months', 'years'], True)}, + 'WEIGHT_OR_WEIGHT_RANGE': {'type': 'string', **_create_unit_regex_and_message(['g', 'mg', 'kg', 'lbs'], True)}, + 'HEIGHT_OR_HEIGHT_RANGE': {'type': 'string', **_create_unit_regex_and_message(['cm', 'in'], True)}, + 'GENDER': {'type': 'string', 'pattern': r'^((?i:male)|(?i:female)|(?i:male, female)|(?i:hermaphrodite)|N/A)$', + 'pattern_custom_message': (' should be one of "Male", "Female", "Male, Female", "Hermaphrodite", or "N/A". ' + 'Ignore this when more complicated descriptions are required.')}, + 'HUMAN_RACE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_ETHNICITY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_TRIAL_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_LIFESTYLE_FACTORS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_MEDICATIONS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_PRESCRIPTION_OTC': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_SMOKING_STATUS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_ALCOHOL_DRUG_USE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_NUTRITION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_INCLUSION_CRITERIA': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_EXCLUSION_CRITERIA': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_ANIMAL_SUPPLIER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_HOUSING': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_LIGHT_CYCLE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_FEED': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_WATER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_INCLUSION_CRITERIA': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_BIOSOURCE_OR_SUPPLIER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_STRAIN_DETAILS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SUBJECT_COMMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_PRIMARY_IMMORTALIZED': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_PASSAGE_NUMBER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_COUNTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SPECIES_GROUP': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + 'required': ['SUBJECT_TYPE', 'SUBJECT_SPECIES'], + 'additionalProperties': False} + +subject_sample_factors_schema = \ +{'type': 'array', + 'items': {'type': 'object', + 'properties': {'Subject ID': {'type': 'string'}, + 'Sample ID': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'Factors': {'type': 'object', + 'additionalProperties': {'type': 'string', 'minLength':1, + 'minLength_prefix':'Warning: ', + 'minLength_custom_message': ' is missing a value.'}}, + 'Additional sample data': {'type': 'object', + 'properties': {'RAW_FILE_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + 'additionalProperties': {'type': 'string', 'minLength':1, + 'minLength_prefix':'Warning: ', + 'minLength_custom_message': ' is missing a value.'}}}, + 'required': ['Subject ID', 'Sample ID', 'Factors'], + 'additionalProperties': False}} + +collection_schema = \ +{'type': 'object', + 'properties': {'COLLECTION_SUMMARY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLECTION_PROTOCOL_ID': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLECTION_PROTOCOL_FILENAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLECTION_PROTOCOL_COMMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SAMPLE_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLECTION_METHOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLECTION_LOCATION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLECTION_FREQUENCY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLECTION_DURATION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLECTION_TIME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'VOLUMEORAMOUNT_COLLECTED': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'STORAGE_CONDITIONS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLECTION_VIALS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'STORAGE_VIALS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLECTION_TUBE_TEMP': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ADDITIVES': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'BLOOD_SERUM_OR_PLASMA': {'type': 'string', 'pattern': r'(?i)^(blood|plasma|serum)$', + 'pattern_custom_message': (' should be one of "Blood", "Plasma", or "Serum". ' + 'Ignore this when more complicated descriptions are required.')}, + 'TISSUE_CELL_IDENTIFICATION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TISSUE_CELL_QUANTITY_TAKEN': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + 'required': ['COLLECTION_SUMMARY'], + 'additionalProperties': False} + +treatment_schema = \ +{'type': 'object', + 'properties': {'TREATMENT_SUMMARY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TREATMENT_PROTOCOL_ID': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TREATMENT_PROTOCOL_FILENAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TREATMENT_PROTOCOL_COMMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TREATMENT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TREATMENT_COMPOUND': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TREATMENT_ROUTE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TREATMENT_DOSE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TREATMENT_DOSEVOLUME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TREATMENT_DOSEDURATION': {'type': 'string', **_create_unit_regex_and_message(['h', 'weeks', 'days'], True)}, + 'TREATMENT_VEHICLE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_VET_TREATMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_ANESTHESIA': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_ACCLIMATION_DURATION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_FASTING': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_ENDP_EUTHANASIA': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_ENDP_TISSUE_COLL_LIST': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_ENDP_TISSUE_PROC_METHOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANIMAL_ENDP_CLINICAL_SIGNS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_FASTING': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'HUMAN_ENDP_CLINICAL_SIGNS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_STORAGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_GROWTH_CONTAINER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_GROWTH_CONFIG': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_GROWTH_RATE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_INOC_PROC': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_MEDIA': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_ENVIR_COND': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_HARVESTING': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_GROWTH_SUPPORT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_GROWTH_LOCATION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_PLOT_DESIGN': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_LIGHT_PERIOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_HUMIDITY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_TEMP': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C'], True)}, + 'PLANT_WATERING_REGIME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_NUTRITIONAL_REGIME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_ESTAB_DATE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_HARVEST_DATE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_GROWTH_STAGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_METAB_QUENCH_METHOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_HARVEST_METHOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PLANT_STORAGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_PCT_CONFLUENCE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_MEDIA_LASTCHANGED': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + 'required': ['TREATMENT_SUMMARY'], + 'additionalProperties': False} + +sampleprep_schema = \ +{'type': 'object', + 'properties': {'SAMPLEPREP_SUMMARY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SAMPLEPREP_PROTOCOL_ID': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SAMPLEPREP_PROTOCOL_FILENAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SAMPLEPREP_PROTOCOL_COMMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PROCESSING_METHOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PROCESSING_STORAGE_CONDITIONS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'EXTRACTION_METHOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'EXTRACT_CONCENTRATION_DILUTION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'EXTRACT_ENRICHMENT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'EXTRACT_CLEANUP': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'EXTRACT_STORAGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SAMPLE_RESUSPENSION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SAMPLE_DERIVATIZATION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SAMPLE_SPIKING': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ORGAN': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ORGAN_SPECIFICATION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CELL_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SUBCELLULAR_LOCATION': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + 'required': ['SAMPLEPREP_SUMMARY'], + 'additionalProperties': False} + +chromatography_schema = \ +{'type': 'object', + 'properties': {'CHROMATOGRAPHY_SUMMARY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CHROMATOGRAPHY_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'INSTRUMENT_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLUMN_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'FLOW_GRADIENT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'FLOW_RATE': {'type': 'string', **_create_unit_regex_and_message(['mL/min', 'uL/min', 'μL/min'], True)}, + 'COLUMN_TEMPERATURE': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C'], True)}, + 'METHODS_FILENAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SAMPLE_INJECTION': {'type': 'string', **_create_unit_regex_and_message(['μL', 'uL'])}, + 'SOLVENT_A': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SOLVENT_B': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'METHODS_ID': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLUMN_PRESSURE': {'type': 'string', **_create_unit_regex_and_message(['psi', 'bar'], True)}, + 'INJECTION_TEMPERATURE': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C'], True)}, + 'INTERNAL_STANDARD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'INTERNAL_STANDARD_MT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'RETENTION_INDEX': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'RETENTION_TIME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SAMPLING_CONE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANALYTICAL_TIME': {'type': 'string', **_create_unit_regex_and_message(['min'], True)}, + 'CAPILLARY_VOLTAGE': {'type': 'string', **_create_unit_regex_and_message(['V', 'kV'])}, + 'MIGRATION_TIME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'OVEN_TEMPERATURE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PRECONDITIONING': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'RUNNING_BUFFER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'RUNNING_VOLTAGE': {'type': 'string', **_create_unit_regex_and_message(['V', 'kV'])}, + 'SHEATH_LIQUID': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TIME_PROGRAM': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TRANSFERLINE_TEMPERATURE': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C'])}, + 'WASHING_BUFFER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'WEAK_WASH_SOLVENT_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'WEAK_WASH_VOLUME': {'type': 'string', **_create_unit_regex_and_message(['μL', 'uL'])}, + 'STRONG_WASH_SOLVENT_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'STRONG_WASH_VOLUME': {'type': 'string', **_create_unit_regex_and_message(['μL', 'uL'])}, + 'TARGET_SAMPLE_TEMPERATURE': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C'])}, + 'SAMPLE_LOOP_SIZE': {'type': 'string', **_create_unit_regex_and_message(['μL', 'uL'])}, + 'SAMPLE_SYRINGE_SIZE': {'type': 'string', **_create_unit_regex_and_message(['μL', 'uL'])}, + 'RANDOMIZATION_ORDER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CHROMATOGRAPHY_COMMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + 'required': ['CHROMATOGRAPHY_TYPE', + 'INSTRUMENT_NAME', + 'COLUMN_NAME', + 'FLOW_GRADIENT', + 'FLOW_RATE', + 'COLUMN_TEMPERATURE', + 'SOLVENT_A', + 'SOLVENT_B'], + 'additionalProperties': False} + +# Add in special case for 'room temperature'. +inj_pattern = chromatography_schema['properties']['INJECTION_TEMPERATURE']['pattern'] +new_pattern = '^(' + inj_pattern[1:-1] + ')|(?i:room temperature)$' +chromatography_schema['properties']['INJECTION_TEMPERATURE']['pattern'] = new_pattern +new_message = (' should be a number or range (ex. "5-6") followed by a space with a unit ' + '(ex. "5 V") from the following list: [\'°C\', \'C\'] or "room temperature". ' + 'Ignore this when more complicated descriptions are required.') +chromatography_schema['properties']['INJECTION_TEMPERATURE']['pattern_custom_message'] = new_message + +analysis_schema = \ +{'type': 'object', + 'properties': {'ANALYSIS_TYPE': {'type': 'string', 'enum': ['MS', 'NMR']}, + 'LABORATORY_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ACQUISITION_DATE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SOFTWARE_VERSION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'OPERATOR_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'DETECTOR_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANALYSIS_PROTOCOL_FILE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ACQUISITION_PARAMETERS_FILE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PROCESSING_PARAMETERS_FILE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'DATA_FORMAT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ACQUISITION_ID': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ACQUISITION_TIME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANALYSIS_COMMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ANALYSIS_DISPLAY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'INSTRUMENT_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'INSTRUMENT_PARAMETERS_FILE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NUM_FACTORS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NUM_METABOLITES': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PROCESSED_FILE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'RANDOMIZATION_ORDER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'RAW_FILE': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + 'required': ['ANALYSIS_TYPE'], + 'additionalProperties': False} + +results_file_schema = \ +{'type': 'object', + 'properties': {'filename': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'UNITS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'Has m/z': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'Has RT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'RT units': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + 'required': ['filename', 'UNITS'], + 'additionalProperties': False} + +ms_schema = \ +{'type': 'object', + 'properties': {'INSTRUMENT_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'INSTRUMENT_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'MS_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ION_MODE': {'type': 'string', 'pattern': r'(?i)^(positive|negative|positive, negative|unspecified)$', + 'pattern_custom_message': (' should be one of "Positive", "Negative", "Positive, Negative", or "Unspecified". ' + 'Ignore this when more complicated descriptions are required.')}, + 'CAPILLARY_TEMPERATURE': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C'], True)}, + 'CAPILLARY_VOLTAGE': {'type': 'string', **_create_unit_regex_and_message(['V', 'kV'])}, + 'COLLISION_ENERGY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'COLLISION_GAS': {'type': 'string', 'pattern': r'(?i)^(nitrogen|argon)$', + 'pattern_custom_message': (' should be one of "Nitrogen" or "Argon". ' + 'Ignore this when more complicated descriptions are required.')}, + 'DRY_GAS_FLOW': {'type': 'string', **_create_unit_regex_and_message(['L/hr', 'L/min'])}, + 'DRY_GAS_TEMP': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C'])}, + 'FRAGMENT_VOLTAGE': {'type': 'string', **_create_unit_regex_and_message(['V'])}, + 'FRAGMENTATION_METHOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'GAS_PRESSURE': {'type': 'string', **_create_unit_regex_and_message(['psi', 'psig', 'bar', 'kPa'])}, + 'HELIUM_FLOW': {'type': 'string', **_create_unit_regex_and_message(['mL/min'])}, + 'ION_SOURCE_TEMPERATURE': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C'])}, + 'ION_SPRAY_VOLTAGE': {'type': 'string', **_create_unit_regex_and_message(['V', 'kV'])}, + 'IONIZATION': {'type': 'string', + 'not':{'oneOf':[{'enum': NA_VALUES}, + {'pattern': '(?i)^(pos|neg|positive|negative|postive|both)$', + 'pattern_custom_message': (' should not be "positive" or "negative". ' + '"ION_MODE" is where that should be indicated.')}]}}, + 'IONIZATION_ENERGY': {'type': 'string', **_create_unit_regex_and_message(['eV'])}, + 'IONIZATION_POTENTIAL': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'MASS_ACCURACY': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PRECURSOR_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'REAGENT_GAS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SOURCE_TEMPERATURE': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C'])}, + 'SPRAY_VOLTAGE': {'type': 'string', **_create_unit_regex_and_message(['kV'])}, + 'ACTIVATION_PARAMETER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ACTIVATION_TIME': {'type': 'string', **_create_unit_regex_and_message(['ms'])}, + 'ATOM_GUN_CURRENT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'AUTOMATIC_GAIN_CONTROL': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'BOMBARDMENT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CDL_SIDE_OCTOPOLES_BIAS_VOLTAGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CDL_TEMPERATURE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'DATAFORMAT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'DESOLVATION_GAS_FLOW': {'type': 'string', **_create_unit_regex_and_message(['L/hr', 'L/min'])}, + 'DESOLVATION_TEMPERATURE': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C'])}, + 'INTERFACE_VOLTAGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'IT_SIDE_OCTOPOLES_BIAS_VOLTAGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'LASER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'MATRIX': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NEBULIZER': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'OCTPOLE_VOLTAGE': {'type': 'string', **_create_unit_regex_and_message(['V'])}, + 'PROBE_TIP': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'RESOLUTION_SETTING': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SAMPLE_DRIPPING': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SCAN_RANGE_MOVERZ': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SCANNING': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SCANNING_CYCLE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SCANNING_RANGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SKIMMER_VOLTAGE': {'type': 'string', **_create_unit_regex_and_message(['V'])}, + 'TUBE_LENS_VOLTAGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'MS_COMMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'MS_RESULTS_FILE': results_file_schema}, + 'required': ['INSTRUMENT_NAME', 'INSTRUMENT_TYPE', 'MS_TYPE', 'ION_MODE'], + 'additionalProperties': False} + +nmr_schema = \ +{'type': 'object', + 'properties': {'INSTRUMENT_NAME': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'INSTRUMENT_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NMR_EXPERIMENT_TYPE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NMR_COMMENTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'FIELD_FREQUENCY_LOCK': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'STANDARD_CONCENTRATION': {'type': 'string', **_create_unit_regex_and_message(['mM'])}, + 'SPECTROMETER_FREQUENCY': {'type': 'string', **_create_unit_regex_and_message(['MHz'])}, + 'NMR_PROBE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NMR_SOLVENT': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NMR_TUBE_SIZE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'SHIMMING_METHOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PULSE_SEQUENCE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'WATER_SUPPRESSION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'PULSE_WIDTH': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'POWER_LEVEL': {'type': 'string', **_create_unit_regex_and_message(['W', 'dB'])}, + 'RECEIVER_GAIN': {'type': 'string', **_create_num_regex_and_message(False)}, + 'OFFSET_FREQUENCY': {'type': 'string', **_create_unit_regex_and_message(['ppm', 'Hz'])}, + 'PRESATURATION_POWER_LEVEL': {'type': 'string', **_create_unit_regex_and_message(['W', 'dB'])}, + 'CHEMICAL_SHIFT_REF_CPD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'TEMPERATURE': {'type': 'string', **_create_unit_regex_and_message(['°C', 'C', 'K'])}, + 'NUMBER_OF_SCANS': {'type': 'string', **_create_int_regex_and_message(False)}, + 'DUMMY_SCANS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'ACQUISITION_TIME': {'type': 'string', **_create_unit_regex_and_message(['s'])}, + 'RELAXATION_DELAY': {'type': 'string', **_create_unit_regex_and_message(['s', 'ms', 'us', 'μs'])}, + 'SPECTRAL_WIDTH': {'type': 'string', **_create_unit_regex_and_message(['ppm', 'Hz'])}, + 'NUM_DATA_POINTS_ACQUIRED': {'type': 'string', **_create_int_regex_and_message(False)}, + 'REAL_DATA_POINTS': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'LINE_BROADENING': {'type': 'string', **_create_unit_regex_and_message(['Hz'])}, + 'ZERO_FILLING': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'APODIZATION': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'BASELINE_CORRECTION_METHOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'CHEMICAL_SHIFT_REF_STD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'BINNED_INCREMENT': {'type': 'string', **_create_unit_regex_and_message(['ppm'])}, + 'BINNED_DATA_NORMALIZATION_METHOD': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'BINNED_DATA_PROTOCOL_FILE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'BINNED_DATA_CHEMICAL_SHIFT_RANGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'BINNED_DATA_EXCLUDED_RANGE': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'NMR_RESULTS_FILE': results_file_schema}, + 'required': ['INSTRUMENT_NAME', + 'INSTRUMENT_TYPE', + 'NMR_EXPERIMENT_TYPE', + 'SPECTROMETER_FREQUENCY'], + 'additionalProperties': False} + +# The 'properties' and 'required' keys are commented out because they are validated in separate +# functions. Doing it here causes many more spurious errors to be printed. +data_schema = \ +{'type': 'array', + 'items': {'type': 'object', + # 'properties': {'Metabolite': {'type': 'string', 'not':{'enum': METABOLITE_NA_VALUES}}}, + # 'required': ['Metabolite'], + 'additionalProperties': True}} + +extended_schema = \ +{'type': 'array', + 'items': {'type': 'object', + # 'properties': {'Metabolite': {'type': 'string', 'not':{'enum': METABOLITE_NA_VALUES}}, + # 'sample_id': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + # 'required': ['Metabolite', 'sample_id'], + 'additionalProperties': True}} + +binned_data_schema = \ +{'type': 'array', + 'items': {'type': 'object', + # 'properties': {'Bin range(ppm)': {'type': 'string', 'not':{'enum': NA_VALUES}}}, + # 'required': ['Bin range(ppm)'], + 'additionalProperties': True}} + +ms_metabolite_data_schema = \ +{'type': 'object', + 'properties': {'Units': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'Data': data_schema, + 'Metabolites': data_schema, + 'Extended': extended_schema}, + 'required': ['Units', 'Data'], + 'additionalProperties': False} + +nmr_binned_data_schema = \ +{'type': 'object', + 'properties': {'Units': {'type': 'string', 'not':{'enum': NA_VALUES}}, + 'Data': binned_data_schema}, + 'required': ['Units', 'Data'], + 'additionalProperties': False} + +base_required_schema = \ +{'properties': {'METABOLOMICS WORKBENCH': metabolomics_workbench_schema, + 'PROJECT': project_schema, + 'STUDY': study_schema, + 'SUBJECT': subject_schema, + 'SUBJECT_SAMPLE_FACTORS': subject_sample_factors_schema, + 'COLLECTION': collection_schema, + 'TREATMENT': treatment_schema, + 'SAMPLEPREP': sampleprep_schema, + 'ANALYSIS': analysis_schema}, + 'required': ['METABOLOMICS WORKBENCH', + 'PROJECT', + 'STUDY', + 'SUBJECT', + 'SUBJECT_SAMPLE_FACTORS', + 'COLLECTION', + 'TREATMENT', + 'SAMPLEPREP', + 'ANALYSIS'], + 'additionalProperties': False} + +ms_required_schema = deepcopy(base_required_schema) +ms_required_schema['properties']['MS'] = ms_schema +ms_required_schema['properties']['MS_METABOLITE_DATA'] = ms_metabolite_data_schema +ms_required_schema['properties']['CHROMATOGRAPHY'] = chromatography_schema +ms_required_schema['required'].extend(['MS']) +ms_required_schema['if'] = {'properties': {'MS_METABOLITE_DATA':{'not':{}}}} +ms_required_schema['then'] = {'properties': {'MS':{'required':['MS_RESULTS_FILE'], + 'required_message': ('Error: There must be either a "MS_METABOLITE_DATA" ' + 'section or a "MS_RESULTS_FILE" subsection in the ' + '"MS" section. Neither were found.')}}} + +nmr_required_schema = deepcopy(base_required_schema) +nmr_required_schema['properties']['NM'] = nmr_schema +nmr_required_schema['properties']['NMR_METABOLITE_DATA'] = ms_metabolite_data_schema +nmr_required_schema['properties']['NMR_BINNED_DATA'] = nmr_binned_data_schema +nmr_required_schema['required'].extend(['NM']) +nmr_required_schema['if'] = {'allOf':[{'properties': {'NMR_METABOLITE_DATA':{'not':{}}}}, {'properties': {'NMR_BINNED_DATA':{'not':{}}}}]} +nmr_required_schema['then'] = {'properties': {'NM':{'required':['NMR_RESULTS_FILE'], + 'required_message': ('Error: There must be either a "NMR_METABOLITE_DATA" ' + 'section, a "NMR_BINNED_DATA" section or a ' + '"NMR_RESULTS_FILE" subsection in the ' + '"NM" section. Neither were found.')}}} + +# oneOf prevents specific error messages from being printed, so instead +# determine whether MS or NMR should be validated and validate using the appropriate schema. +# compiled_schema = \ +# {'type': 'object', +# 'oneOf':[ms_required_schema, +# nmr_required_schema]} + + diff --git a/src/mwtab/mwtab.py b/src/mwtab/mwtab.py new file mode 100644 index 0000000..eafcbf1 --- /dev/null +++ b/src/mwtab/mwtab.py @@ -0,0 +1,1200 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +mwtab.mwtab +~~~~~~~~~~~ + +This module provides the :class:`~mwtab.mwtab.MWTabFile` class +that stores the data from a single ``mwTab`` formatted file in the +form of an :py:class:`dict`. Data can be accessed +directly from the :class:`~mwtab.mwtab.MWTabFile` instance using +bracket accessors. + +The data is divided into a series of "sections" which each contain a +number of "key-value"-like pairs. Also, the file contains a specially +formatted ``SUBJECT_SAMPLE_FACTOR`` block and blocks of data between +``*_START`` and ``*_END``. +""" + +from __future__ import print_function, division, unicode_literals +import io +import sys +import json +import re +import copy +from itertools import zip_longest + +import pandas + +from .tokenizer import tokenizer, _results_file_line_to_dict +from .validator import validate_file +from .mwschema import ms_required_schema, nmr_required_schema +from .duplicates_dict import DuplicatesDict, DUPLICATE_KEY_REGEX + +SORT_KEYS = False +INDENT = 4 + + +# From https://stackoverflow.com/questions/14902299/json-loads-allows-duplicate-keys-in-a-dictionary-overwriting-the-first-value +def _handle_duplicate_keys(ordered_pairs): + """Object pairs hook for the json package to be able to read in duplicate keys for dictionaries.""" + d = DuplicatesDict() + s = set() + for k, v in ordered_pairs: + d[k] = v + s.add(k) + + if len(s) == len(ordered_pairs): + return d.data + else: + return d + + +def _parse_header_input(input_str): + """Attempt to parse a header string into a dict.""" + match_re = re.match(r"#METABOLOMICS WORKBENCH( )?([^: ]+ )?([A-Z_]+:\w+ ?)*", input_str) + if match_re: + key_values = re.findall(r" (\w*:\w*)", input_str) + temp_dict = {} + for key_value in key_values: + key, new_value = key_value.split(":") + temp_dict[key] = new_value + + return temp_dict + else: + raise ValueError(r"Header cannot be set because it is not of the form \"#METABOLOMICS WORKBENCH( )?([^: ]+ )?([A-Z_]+:\w+ ?)*\"") + + + +# Descriptor to handle the convenience properties for MWTabFile. +# https://realpython.com/python-descriptors/ +class MWTabProperty: + def __set_name__(self, owner, name): + self._name = name + + def __get__(self, obj, type=None): + if self._name == "study_id" or self._name == "analysis_id": + try: + return obj["METABOLOMICS WORKBENCH"].get(self._name.upper()) + except Exception: + return None + + if self._name == "header": + try: + header_str = "#METABOLOMICS WORKBENCH" + pairs = " ".join([item[0] + ":" + item[1] for item in obj["METABOLOMICS WORKBENCH"].items() + if item[0] not in ["VERSION", "CREATED_ON"]]) + header_str += " " + pairs + return header_str + except Exception: + return None + + def __set__(self, obj, value): + if not isinstance(value, str): + raise TypeError("The value for " + self._name + " must be a string.") + + if "METABOLOMICS WORKBENCH" in obj: + if self._name == "study_id" or self._name == "analysis_id": + if isinstance(obj["METABOLOMICS WORKBENCH"], dict): + obj["METABOLOMICS WORKBENCH"][self._name.upper()] = value + else: + raise TypeError("The \"METABOLOMICS WORKBENCH\" key is not a dictionary, so " + self._name + " cannot be set.") + + if self._name == "header": + temp_dict = _parse_header_input(value) + obj["METABOLOMICS WORKBENCH"].update(temp_dict) + else: + if self._name == "study_id" or self._name == "analysis_id": + obj["METABOLOMICS WORKBENCH"] = {self._name.upper() : value} + + if self._name == "header": + temp_dict = _parse_header_input(value) + obj["METABOLOMICS WORKBENCH"] = temp_dict + + + +class MWTabFile(dict): + """MWTabFile class that stores data from a single ``mwTab`` formatted file in + the form of a dictionary. + + Parameters: + source: A string that should be the file path to the mwtab file that will be read in. + duplicate_keys: If True, use a special dictionary type that can handle duplicate keys. + If you are uisng this class to build an mwtab file by hand, don't set + this to True. This was added because some files already upload to the + Metabolomics Workbench erroneously have duplicate keys and the class + needed to be able to read write them back out correctly. + force: If True, replace non-dictionary values in METABOLITES_DATA, METABOLITES, and EXTENDED + tables with empty dicts on JSON read in. + + Attributes: + source: A string that should be the file path to the mwtab file that was read in. + study_id: A managed property. The study ID is stored in the METABOLOMICS WORKBENCH + key of this class and the JSON version of an mwTab file. This property + is provided as a convenience to access the study ID. + analysis_id: A managed property. The analysis ID is stored in the METABOLOMICS WORKBENCH + key of this class and the JSON version of an mwTab file. This property + is provided as a convenience to access the analysis ID. + header: A managed property. It is provided as a convenience to be able to view the + header line of the file. You can also set the METABOLOMICS WORKBENCH key by + setting this property. It will parse the string you assign into the dictionary + that belongs in the METABOLOMICS WORKBENCH key. Assuming the provided string is + a correctly generated header line. + data_section_key: A simple property that will give you the key to the data section. + Either one of "MS_METABOLITE_DATA", "NMR_METABOLITE_DATA", or "NMR_BINNED_DATA", + or None if none of those were found. + + Special Notes: + In general this class has the same structure as mwTab JSON, but there are a few exceptions to that. + One is that the _RESULTS_FILE subsection, whether in the MS or NM section, is a dictionary in this + class with keys for the elements found. In the mwTab JSON this is just a string. The possible + keys the dictionary could have are: "filename", "UNITS", "Has m/z", "Has RT", and "RT units". + If the _RESULTS_FILE line did not have these keys, then they won't be in the dictionary. + + The Metabolomics Workbench has deprecated mwTab files with NMR_BINNED_DATA sections, but for + the few that do exist if you read them in using this class, the dictionaries in the ['NMR_BINNED_DATA']['Data'] + list of dicts will have keys for both "Metabolite" and "Bin range(ppm)". This is because the + JSON version from the Metabolomics Workbench uses "Bin range(ppm)" and not "Metabolite", but + we wanted to present a seemless unified interface for this class regardless of the analysis type. + They print out with only the "Bin range(ppm)" keys to match what the Metabolomics Workbench provides, + but internally both keys will be there. If they somehow become different, you will see a message + about it when you try to write the file out. + """ + + prefixes = { + "METABOLOMICS WORKBENCH": "", + "PROJECT": "PR:", + "STUDY": "ST:", + "SUBJECT": "SU:", + "SUBJECT_SAMPLE_FACTORS": "", + "COLLECTION": "CO:", + "TREATMENT": "TR:", + "SAMPLEPREP": "SP:", + "CHROMATOGRAPHY": "CH:", + "ANALYSIS": "AN:", + "MS": "MS:", + "NMR": "NM:", + "NM": "NM:", + "MS_METABOLITE_DATA": "", + "NMR_METABOLITE_DATA": "", + "NMR_BINNED_DATA": "", + "METABOLITES": "", + } + + result_file_keys = ["UNITS", "Has m/z", "Has RT", "RT units"] + data_section_keys = {"MS_METABOLITE_DATA", "NMR_METABOLITE_DATA", "NMR_BINNED_DATA"} + table_names = ['Data', 'Extended', 'Metabolites'] + + study_id = MWTabProperty() + analysis_id = MWTabProperty() + header = MWTabProperty() + + def __init__(self, source, duplicate_keys=False, force=False, *args, **kwds): + """File initializer. + + :param str source: Source a `MWTabFile` instance was created from. + """ + super(MWTabFile, self).__init__(*args, **kwds) + self.source = source + self._force = force + self._factors = None + self._samples = None + self._raw_samples = None + self._metabolite_header = None + self._raw_metabolite_header = None + self._extended_metabolite_header = None + self._raw_extended_metabolite_header = None + self._binned_header = None + self._raw_binned_header = None + self._short_headers = set() + self._duplicate_sub_sections = {} + self._input_format = 'json' + self._duplicate_keys = duplicate_keys + if duplicate_keys: + self._default_dict_type = DuplicatesDict + else: + self._default_dict_type = dict + + @property + def data_section_key(self): + """Easily determine the data_section_key. + + The key will be one of "MS_METABOLITE_DATA", "NMR_METABOLITE_DATA", or "NMR_BINNED_DATA", + but will be None if none of those keys are found. + """ + data_section_key = list(set(self.keys()) & self.data_section_keys) + if data_section_key: + return data_section_key[0] + return None + + def set_table_from_pandas(self, df, table_name, clear_header=False): + """Return the given table_name as a pandas.DataFrame. + + table_name must be one of "Metabolites", "Extended", or "Data". + + :param pandas.DataFrame df: pandas.DataFrame that will be used to update the object. + :param str table_name: the name of the table to set from df. + :param bool clear_header: if True, sets the appropriate header to None, otherwise sets it to the df columns. + :return: None + :rtype: :py:obj:`None` + """ + data_section_key = self.data_section_key + self[data_section_key][table_name] = [self._default_dict_type(data_dict) for data_dict in df.fillna('').astype(str).to_dict(orient='records')] + if clear_header: + if table_name == 'Metabolites': + self._metabolite_header = None + elif table_name == 'Extended': + self._extended_metabolite_header = None + elif table_name == 'Data': + self._samples = None + if "BINNED" in data_section_key: + self._binned_header = self._samples + elif df.shape[1] > 0: + if table_name == 'Metabolites': + self._metabolite_header = [k for k in self[data_section_key][table_name][0].keys()][1:] + elif table_name == 'Extended': + self._extended_metabolite_header = [k for k in self[data_section_key][table_name][0].keys()][1:] + elif table_name == 'Data': + self._samples = [k for k in self[data_section_key][table_name][0].keys()][1:] + if "BINNED" in data_section_key: + self._binned_header = self._samples + + def set_metabolites_from_pandas(self, df, clear_header=False): + """Update MWTabFile based on provided pandas.DataFrame. + + Overwrite the current list of dicts in self[data_section_key]['Metabolites'] with the + values in df. Also overwrites self._metabolite_header with the columns in df, excluding the + first column. df is assumed to have the first column as the 'Metabolites' column. + + :param pandas.DataFrame df: pandas.DataFrame that will be used to update the object. + :param bool clear_header: if True, sets _metabolite_header to None, otherwise sets it to the df columns. + :return: None + :rtype: :py:obj:`None` + """ + self.set_table_from_pandas(df, 'Metabolites', clear_header) + + def set_extended_from_pandas(self, df, clear_header=False): + """Update MWTabFile based on provided pandas.DataFrame. + + Overwrite the current list of dicts in self[data_section_key]['Extended'] with the + values in df. Also overwrites self._extended_metabolite_header with the columns in df, excluding the + first column. df is assumed to have the first column as the 'Metabolites' column. + + :param pandas.DataFrame df: pandas.DataFrame that will be used to update the object. + :param bool clear_header: if True, sets _extended_metabolite_header to None, otherwise sets it to the df columns. + :return: None + :rtype: :py:obj:`None` + """ + self.set_table_from_pandas(df, 'Extended', clear_header) + + def set_metabolites_data_from_pandas(self, df, clear_header=False): + """Update MWTabFile based on provided pandas.DataFrame. + + Overwrite the current list of dicts in self[data_section_key]['Data'] with the + values in df. Also overwrites self._samples with the columns in df, excluding the + first column. df is assumed to have the first column as the 'Metabolites' column. + + :param pandas.DataFrame df: pandas.DataFrame that will be used to update the object. + :param bool clear_header: if True, sets _samples to None, otherwise sets it to the df columns. + :return: None + :rtype: :py:obj:`None` + """ + self.set_table_from_pandas(df, 'Data', clear_header) + + def get_table_as_pandas(self, table_name): + """Return the given table_name as a pandas.DataFrame. + + table_name must be one of "Metabolites", "Extended", or "Data". Note that + if there are duplicate column names, they will have a string appended to the + end of the name like {{{_\\d+_}}}. + + :param str table_name: the name of the table to return as a pandas.DataFrame. + :return: The list of dicts for the given table_name as a pandas.DataFrame. + :rtype: pandas.DataFrame + """ + data_section_key = self.data_section_key + if data_section_key and table_name in self[data_section_key]: + if self._duplicate_keys: + temp_list = [duplicates_dict.data for duplicates_dict in self[data_section_key][table_name]] + else: + temp_list = self[data_section_key][table_name] + df = pandas.DataFrame.from_records(temp_list) + return df + return pandas.DataFrame() + + def get_metabolites_as_pandas(self): + """Return the Metabolites table as a pandas.DataFrame. + + Note that if there are duplicate column names, they will have a string appended to the + end of the name like {{{_\\d+_}}}. + + :return: The list of dicts for the Metabolites table as a pandas.DataFrame. + :rtype: pandas.DataFrame + """ + return self.get_table_as_pandas('Metabolites') + + def get_extended_as_pandas(self): + """Return the Extended table as a pandas.DataFrame. + + Note that if there are duplicate column names, they will have a string appended to the + end of the name like {{{_\\d+_}}}. + + :return: The list of dicts for the Extended table as a pandas.DataFrame. + :rtype: pandas.DataFrame + """ + return self.get_table_as_pandas('Extended') + + def get_metabolites_data_as_pandas(self): + """Return the Data table as a pandas.DataFrame. + + Note that if there are duplicate column names, they will have a string appended to the + end of the name like {{{_\\d+_}}}. + + :return: The list of dicts for the Data table as a pandas.DataFrame. + :rtype: pandas.DataFrame + """ + return self.get_table_as_pandas('Data') + + def validate(self, ms_schema: dict = ms_required_schema, nmr_schema: dict = nmr_required_schema, verbose: bool = True) -> (str, list[dict]): + """Validate the instance. + + Args: + ms_schema: jsonschema to validate both the base parts of the file and the MS specific parts of the file. + nmr_schema: jsonschema to validate both the base parts of the file and the NMR specific parts of the file. + verbose: whether to be verbose or not. + + Returns: + Error messages as a single string and error messages in JSON form. If verbose is True, then the single string will be None. + """ + return validate_file( + mwtabfile=self, + ms_schema = ms_schema, + nmr_schema = nmr_schema, + verbose = verbose, + ) + + @classmethod + def from_dict(cls, input_dict): + """Create a new MWTabFile instance from input_dict. + + :param dict input_dict: Dictionary to create the new instance from. + :return: New instance of MWTabFile + :rtype: :class:`~mwtab.mwtab.MWTabFile` + """ + new_mwtabfile = cls("Internal dictionary. ID: " + str(id(input_dict))) + new_mwtabfile.update(input_dict) + return new_mwtabfile + + def read_from_str(self, input_str): + """Read input_str into a :class:`~mwtab.mwtab.MWTabFile` instance. + + :return: None + :rtype: :py:obj:`None` + """ + if not input_str: + raise ValueError("Blank input string retrieved from source.") + + mwtab_str = self._is_mwtab(input_str) + self._input_format = 'mwtab' if mwtab_str else 'json' + json_str = self._is_json(input_str, self._duplicate_keys, self._force) + + if json_str: + self.update(json_str) + + metabolite_header = [column if not column.endswith('}}}') else re.match(DUPLICATE_KEY_REGEX, column).group(1) + for column in self.get_metabolites_as_pandas().columns] + self._raw_metabolite_header = metabolite_header if metabolite_header else None + self._metabolite_header = metabolite_header[1:] if metabolite_header[1:] else None + + extended_metabolite_header = [column if not column.endswith('}}}') else re.match(DUPLICATE_KEY_REGEX, column).group(1) + for column in self.get_extended_as_pandas().columns] + self._raw_extended_metabolite_header = extended_metabolite_header if extended_metabolite_header else None + self._extended_metabolite_header = extended_metabolite_header[1:] if extended_metabolite_header[1:] else None + + samples = [column if not column.endswith('}}}') else re.match(DUPLICATE_KEY_REGEX, column).group(1) + for column in self.get_metabolites_data_as_pandas().columns] + self._raw_samples = samples if samples else None + self._samples = samples[1:] if samples[1:] else None + + if (data_section_key := self.data_section_key) and "BINNED" in data_section_key: + self._binned_header = self._samples + self._raw_binned_header = self._raw_samples + + for i in range(len(self['NMR_BINNED_DATA']['Data'])): + if 'Bin range(ppm)' in self['NMR_BINNED_DATA']['Data'][i]: + self['NMR_BINNED_DATA']['Data'][i]['Metabolite'] = self['NMR_BINNED_DATA']['Data'][i]['Bin range(ppm)'] + + elif mwtab_str: + self._build_mwtabfile(mwtab_str) + else: + raise TypeError("Unknown file format") + + def read(self, filehandle): + """Read data into a :class:`~mwtab.mwtab.MWTabFile` instance. + + :param filehandle: file-like object. + :type filehandle: :py:class:`io.TextIOWrapper`, :py:class:`gzip.GzipFile`, + :py:class:`bz2.BZ2File`, :py:class:`zipfile.ZipFile` + :return: None + :rtype: :py:obj:`None` + """ + input_str = filehandle.read() + self.read_from_str(input_str) + filehandle.close() + + def write(self, filehandle, file_format): + """Write :class:`~mwtab.mwtab.MWTabFile` data into file. + + :param filehandle: file-like object. + :type filehandle: :py:class:`io.TextIOWrapper` + :param str file_format: Format to use to write data: `mwtab` or `json`. + :return: None + :rtype: :py:obj:`None` + """ + try: + if file_format == "json": + json_str = self._to_json() + filehandle.write(json_str) + elif file_format == "mwtab": + mwtab_str = self._to_mwtab() + filehandle.write(mwtab_str) + else: + raise TypeError("Unknown file format.") + except IOError: + raise IOError('"filehandle" parameter must be writable.') + filehandle.close() + + def writestr(self, file_format): + """Write :class:`~mwtab.mwtab.MWTabFile` data into string. + + :param str file_format: Format to use to write data: `mwtab` or `json`. + :return: String representing the :class:`~mwtab.mwtab.MWTabFile` instance. + :rtype: :py:class:`str` + """ + if file_format == "json": + json_str = self._to_json() + return json_str + elif file_format == "mwtab": + mwtab_str = self._to_mwtab() + return mwtab_str + else: + raise TypeError("Unknown file format.") + + def _build_mwtabfile(self, mwtab_str): + """Build :class:`~mwtab.mwtab.MWTabFile` instance. + + :param mwtab_str: String in `mwtab` format. + :type mwtab_str: :py:class:`str` or :py:class:`bytes` + :return: instance of :class:`~mwtab.mwtab.MWTabFile`. + :rtype: :class:`~mwtab.mwtab.MWTabFile` + """ + mwtab_file = self + lexer = tokenizer(mwtab_str, self._default_dict_type) + token = next(lexer) + + while token.key != "!#ENDFILE": + if token.key.startswith("#"): + name = token.key[1:] + section = self._build_block(name, lexer) + # if section: + if name == "METABOLITES": + data_section = next((n for n in mwtab_file.keys() if "METABOLITE_DATA" in n or "BINNED_DATA" in n), None) + if data_section: + for key in section.keys(): + mwtab_file[data_section][key] = section[key] + elif name == "NMR": + mwtab_file["NM"] = section + elif name == "END": + pass + else: + mwtab_file[name] = section + token = next(lexer) + + # Sometimes the results file line is in the wrong spot, look for it in DATA and move it if so. + # Looked at changing the tokenizer to use the two letter code on the line, 'MS:RESULTS_FILE', + # so we could put it in the correct spot when building, but there are other examples of two + # letter codes that are in the right spot, but have the wrong 2 letter code (AN000012). + if self.data_section_key: + results_file_key = None + for key in self[self.data_section_key]: + if key.endswith('_RESULTS_FILE'): + results_file_key = key + if 'MS' in self: + section_key = 'MS' + elif 'NM' in self: + section_key = 'NM' + + temp = self[self.data_section_key][key] + self[section_key][key] = temp + if results_file_key: + del self[self.data_section_key][results_file_key] + + return mwtab_file + + def _build_block(self, name, lexer): + """Build individual text block of :class:`~mwtab.mwtab.MWTabFile` instance. + + :param name: name of the block, used for errors. + :type name: str + :param lexer: instance of the mwtab tokenizer. + :type lexer: :func:`~mwtab.tokenizer.tokenizer` + :return: Section dictionary. + :rtype: :py:class:`dict` + """ + section = {} + token = next(lexer) + + if 'SUBJECT_SAMPLE_FACTORS' in self: + ssf_samples = [value['Sample ID'] for value in self['SUBJECT_SAMPLE_FACTORS']] + + while not token.key.startswith("#ENDSECTION"): + + if token.key.startswith("SUBJECT_SAMPLE_FACTORS"): + if type(section) != list: + section = list() + section.append(token.value) + + elif token.key.endswith("_START"): + section_name = token.key[0:-6] + data = [] + + token = next(lexer) + header = list(token.value) + # Sometimes there can be extra tabs at the end of the line that results in + # an empty string as the token value, so remove it. + while not header[-1]: + header.pop() + metabolite_header = ["Metabolite"] + header[1:] + + loop_count = 0 + while not token.key.endswith("_END"): + # Sometimes there can be extra tabs at the end of the line that results in + # an empty string as the token value, so remove it. + token_value = list(token.value) + while not token_value[-1]: + token_value.pop() + + is_header = False + if "BINNED_DATA" in section_name and 'EXTENDED' not in section_name and loop_count < 2: + if loop_count < 1: + self._raw_binned_headers = token_value + self._raw_samples = self._raw_binned_headers + if token.key == "Bin range(ppm)": + # self._binned_header = token_value[1:] + # self._samples = self._binned_header + is_header = True + # Have seen Factors section in incorrect sections such as METABOLITES, + # and seen multiple Factors sections in a single METABOLITE_DATA section. + # So just grab the one near the top. + elif token.key == "Factors" and "METABOLITE_DATA" in section_name and loop_count < 2: + self._factors = {} + for i, factor_string in enumerate(token_value[1:]): + factor_pairs = factor_string.split("| ") + factor_dict = self._default_dict_type() + for pair in factor_pairs: + factor_key, factor_value = pair.split(":") + factor_dict[factor_key.strip()] = factor_value.strip() + self._factors[header[i+1]] = factor_dict + is_header = True + + elif "METABOLITE_DATA" in section_name and 'EXTENDED' not in section_name and loop_count < 2: + if loop_count < 1: + self._raw_samples = token_value + # The last check for len(token_value) == 1 is for ones like AN000788. + if (any(sample in ssf_samples for sample in token_value[1:]) or \ + (len(token_value) == 1 and token_value[0] in ['Samples', 'metabolite name', 'metabolite_name']) or \ + (len(ssf_samples) == 0 and token_value[0] in ['Samples', 'metabolite name', 'metabolite_name'])): + # self._samples = token_value[1:] + is_header = True + + elif "METABOLITES" in section_name and loop_count < 2: + if loop_count < 1: + self._raw_metabolite_header = token_value + if token.key.lower() == "metabolite_name": + # self._metabolite_header = token_value[1:] + is_header = True + + elif "EXTENDED" in section_name and loop_count < 2: + if loop_count < 1: + self._raw_extended_metabolite_header = token_value + if token.key.lower() == "metabolite_name": + # self._extended_metabolite_header = token_value[1:] + is_header = True + + + + # TODO remove. + # if token.key == "Bin range(ppm)" and "BINNED_DATA" in section_name and loop_count < 2: + # self._binned_header = token_value[1:] + # self._samples = self._binned_header + # # Have seen Factors section in incorrect sections such as METABOLITES, + # # and seen multiple Factors sections in a single METABOLITE_DATA section. + # # So just grab the one near the top. + # elif token.key == "Factors" and "METABOLITE_DATA" in section_name and loop_count < 2: + # self._factors = {} + # for i, factor_string in enumerate(token_value[1:]): + # factor_pairs = factor_string.split("| ") + # factor_dict = self._default_dict_type() + # for pair in factor_pairs: + # factor_key, factor_value = pair.split(":") + # factor_dict[factor_key.strip()] = factor_value.strip() + # self._factors[header[i+1]] = factor_dict + + # # The last check for len(token_value) == 1 is for ones like AN000788. + # elif "METABOLITE_DATA" in section_name and 'EXTENDED' not in section_name and self._samples is None and \ + # loop_count < 2 and (any(sample in ssf_samples for sample in token_value[1:]) or \ + # (len(token_value) == 1 and token_value[0] in ['Samples', 'metabolite name', 'metabolite_name'])): + # self._samples = token_value[1:] + + # elif token.key.lower() == "metabolite_name" and "METABOLITES" in section_name and loop_count < 2: + # self._metabolite_header = token_value[1:] + + # elif token.key.lower() == "metabolite_name" and "EXTENDED" in section_name and loop_count < 2: + # self._extended_metabolite_header = token_value[1:] + + if not is_header: + token_len = len(token_value) + temp_dict = self._default_dict_type() + for item in zip_longest(metabolite_header, token_value, fillvalue=''): + temp_dict[item[0]] = item[1] + data.append(temp_dict) + + if token_len > len(metabolite_header): + self._short_headers.add(section_name) + + token = next(lexer) + loop_count += 1 + + # This makes it so all dicitonaries have the same number of values. + # Let's say row 3 looks like {'Metabolite': 'asdf', 'col1': 'qwer', '': 2345} + # The rows above don't have the '' entry, this code makes it so they do. + if self._duplicate_keys: + data = [duplicates_dict.data for duplicates_dict in data] + data_df = pandas.DataFrame.from_records(data).fillna('').astype(str) + data = data_df.to_dict(orient='records') + if self._duplicate_keys: + data = [DuplicatesDict(data_dict) for data_dict in data] + min_header = [column if not column.endswith('}}}') else re.match(DUPLICATE_KEY_REGEX, column).group(1) + for column in data_df.columns] + min_header = min_header[1:] if min_header[1:] else None + + if token.key.startswith("METABOLITES"): + section["Metabolites"] = data + self._metabolite_header = min_header + elif token.key.startswith("EXTENDED_"): + section["Extended"] = data + self._extended_metabolite_header = min_header + else: + section["Data"] = data + self._samples = min_header + if "BINNED_DATA" in section_name: + self._binned_header = min_header + + elif token.key.endswith("_RESULTS_FILE"): + key, results_file_dict = token + section[key] = results_file_dict + + else: + key, value = token + if key in section: + if section[key] == value: + self._duplicate_sub_sections.setdefault(name, {}) + self._duplicate_sub_sections[name][key] = value + if name.endswith('WORKBENCH'): + section[key] = value + else: + section[key] += " {}".format(value) + else: + section[key] = value + + # load token(s) (from parsing of next line in file) + token = next(lexer) + + return section + + def print_file(self, f=sys.stdout, file_format="mwtab"): + """Print :class:`~mwtab.mwtab.MWTabFile` into a file or stdout. + + :param f: writable file-like stream. + :type f: :py:class:`io.StringIO` + :param str file_format: Format to use: `mwtab` or `json`. + :return: None + :rtype: :py:obj:`None` + """ + if file_format == "mwtab": + for key in self: + if key == "SUBJECT_SAMPLE_FACTORS": + print("#SUBJECT_SAMPLE_FACTORS: \tSUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data", file=f) + self.print_subject_sample_factors(key, f=f, file_format=file_format) + else: + if key == "METABOLOMICS WORKBENCH": + print(self.header, file=f) + elif key == "NM": + print("#NMR", file=f) + else: + print("#{}".format(key), file=f) + + if isinstance(self[key], dict): + self.print_block(key, f=f, file_format=file_format) + else: + raise TypeError(f'Key/section "{key}" is not a dictionary. It cannot be translated to the mwTab format.') + print("#END", file=f) + + elif file_format == "json": + print(self._to_json(), file=f) + + def print_subject_sample_factors(self, section_key, f=sys.stdout, file_format="mwtab"): + """Print `mwtab` `SUBJECT_SAMPLE_FACTORS` section into a file or stdout. + + :param str section_key: Section name. + :param f: writable file-like stream. + :type f: :py:class:`io.StringIO` + :param str file_format: Format to use: `mwtab` or `json`. + :return: None + :rtype: :py:obj:`None` + """ + if file_format == "mwtab": + for item in self[section_key]: + formatted_items = [] + for k in item.keys(): + if k in ["Subject ID", "Sample ID"]: + formatted_items.append(str(item[k])) + elif k == "Factors": + factors = [] + factor_dict = item[k] + for k2, value in factor_dict.items(): + factors.append("{}:{}".format(k2, value)) + formatted_items.append(" | ".join(factors)) + elif k == "Additional sample data": + additional_sample_data = [] + add_dict = item[k] + for k2, value in add_dict.items(): + additional_sample_data.append("{}={}".format(k2, value)) + formatted_items.append("; ".join(additional_sample_data)) + line = "{}{}\t{}".format(section_key, 11 * " ", "\t".join(formatted_items)) + # for file missing "Additional sample data" items + if len(formatted_items) < 4: + line += "\t" + print(line, file=f) + + def print_block(self, section_key, f=sys.stdout, file_format="mwtab"): + """Print `mwtab` section into a file or stdout. + + :param str section_key: Section name. + :param f: writable file-like stream. + :type f: :py:class:`io.StringIO` + :param str file_format: Format to use: `mwtab` or `json`. + :return: None + :rtype: :py:obj:`None` + """ + if file_format == "mwtab": + for key, value in self[section_key].items(): + if section_key == "METABOLOMICS WORKBENCH" and key not in ("VERSION", "CREATED_ON"): + continue + + if key in ("VERSION", "CREATED_ON"): + cw = 20 - len(key) + elif key == "Units": + cw = 33 - len(section_key+":UNITS") + else: + cw = 30 - len(key) + + if key.endswith("_RESULTS_FILE"): + + results_string = self.prefixes.get(section_key, "") + key + cw * " " + "\t" + results_string += self._create_result_file_string(section_key, key, "mwtab") + print(results_string, file=f) + + # prints #MS_METABOLITE_DATA, #NMR_METABOLITE_DATA, or #NMR_BINNED_DATA sections + elif key == "Units": + print("{}:UNITS{}\t{}".format(section_key, cw * " ", value), file=f) + elif key == "Data": + print("{}_START".format(section_key), file=f) + + if "METABOLITE" in section_key: + # prints "Samples" line at head of data section + sample_names = [] + if self._samples is not None: + sample_names = self._samples + elif self[section_key][key]: + sample_names = [k for k in self[section_key][key][0].keys()][1:] + print("\t".join(["Samples"] + sample_names), file=f) + if sample_names: + # prints "Factors" line at head of data section + if self._factors is not None: + factors_dict = {} + for sample_name, sample_factors in self._factors.items(): + factors_for_sample = [] + factor_dict = sample_factors + for factor_key, factor_value in factor_dict.items(): + factors_for_sample.append(factor_key + ":" + factor_value) + factors_dict[sample_name] = ' | '.join(factors_for_sample) + else: + factors_dict = {} + for i in self['SUBJECT_SAMPLE_FACTORS']: + sample = i['Sample ID'] + factors_for_sample = [] + factor_dict = i['Factors'] + for factor_key, factor_value in factor_dict.items(): + factors_for_sample.append(factor_key + ":" + factor_value) + factors_dict[sample] = ' | '.join(factors_for_sample) + + factors_list = [] + for k in sample_names: + # Need to make sure the factors always line up with samples. + if k not in factors_dict: + factors_list = [] + break + factors_list.append(factors_dict[k]) + if factors_list: + print("\t".join(["Factors"] + factors_list), file=f) + + for k, i in enumerate(self[section_key][key]): + print("\t".join(i.values()), file=f) + + else: # NMR_BINNED_DATA + # Only print if there is data to print. + if self._binned_header is not None: + binned_header = self._binned_header + elif self[section_key][key]: + binned_header = [k for k in self[section_key][key][0].keys()][1:] + + print("\t".join(["Bin range(ppm)"] + binned_header), file=f) + + for i in self[section_key][key]: + print("\t".join(i.values()), file=f) + + print("{}_END".format(section_key), file=f) + + # prints #METABOLITES section + elif key in ("Metabolites", "Extended"): + if key == "Metabolites": + print("#METABOLITES", file=f) + print("METABOLITES_START", file=f) + else: + print("EXTENDED_{}_START".format(section_key), file=f) + + if key == "Metabolites" and self._metabolite_header is not None: + metabolite_header = self._metabolite_header + elif key == "Extended" and self._extended_metabolite_header is not None: + metabolite_header = self._extended_metabolite_header + elif self[section_key][key]: + metabolite_header = [k for k in self[section_key][key][0].keys()][1:] + else: + metabolite_header = [] + print("\t".join(["metabolite_name"] + metabolite_header), file=f) + + for i in self[section_key][key]: + print("\t".join(i.values()), file=f) + + if key == "Metabolites": + print("METABOLITES_END", file=f) + else: + print("EXTENDED_{}_END".format(section_key), file=f) + + else: + # Filenames don't get split. + if len(str(value)) > 80 and not key.endswith("_FILENAME"): + words = str(value).split(" ") + length = 0 + line = list() + for word in words: + if length + len(word) + len(line) - 1 < 80: + line.append(word) + length += len(word) + else: + # Long filenames were printing 2 lines, with the top one being blank. + # I fixed this by adding a check to skip filenames, but just in case I also don't + # let empty lines be printed. + if line: + print("{}{}{}\t{}".format(self.prefixes.get(section_key, ""), key, cw * " ", " ".join(line)), file=f) + line = [word] + length = len(word) + print("{}{}{}\t{}".format(self.prefixes.get(section_key, ""), key, cw * " ", " ".join(line)), + file=f) + else: + print("{}{}{}\t{}".format(self.prefixes.get(section_key, ""), key, cw * " ", value), file=f) + + # Note that indent cannot be None or json will use a version of the + # encoder written in C and DuplicatesDict will not be printed correctly. + elif file_format == "json": + print(json.dumps(self[section_key], sort_keys=SORT_KEYS, indent=INDENT), file=f) + + def _to_json(self): + """Save :class:`~mwtab.mwtab.MWTabFile` into JSON string. + + :return: JSON string. + :rtype: :py:class:`str` + """ + self._set_key_order() + temp = copy.deepcopy(self) + # Result files ends up being a dictionary, but needs to printed as a string. + for section_key, section_value in temp.items(): + if isinstance(section_value, dict): + for key in section_value: + if key.endswith("_RESULTS_FILE"): + temp[section_key][key] = self._create_result_file_string(section_key, key, "json") + + if 'NMR_BINNED_DATA' in temp: + for i in range(len(temp['NMR_BINNED_DATA']['Data'])): + if 'Bin range(ppm)' in temp['NMR_BINNED_DATA']['Data'][i]: + if temp['NMR_BINNED_DATA']['Data'][i]['Metabolite'] != temp['NMR_BINNED_DATA']['Data'][i]['Bin range(ppm)']: + print("Warning: The \"Metabolite\" key and \"Bin range(ppm)\" " + f"key in ['NMR_BINNED_DATA']['Data'][{i}] are different " + "values. Only the value in \"Bin range(ppm)\" will be written out.") + del temp['NMR_BINNED_DATA']['Data'][i]['Metabolite'] + else: + new_dict = self._default_dict_type() + new_dict['Bin range(ppm)'] = temp['NMR_BINNED_DATA']['Data'][i]['Metabolite'] + del temp['NMR_BINNED_DATA']['Data'][i]['Metabolite'] + # Note that the update method cannot be used here because it can mess up DuplicatesDict. + for key, value in temp['NMR_BINNED_DATA']['Data'][i].items(): + new_dict[key] = value + temp['NMR_BINNED_DATA']['Data'][i] = new_dict + + # Note that indent cannot be None or json will use a version of the + # encoder written in C and DuplicatesDict will not be printed correctly. + return json.dumps(temp, sort_keys=SORT_KEYS, indent=INDENT) + + + def _to_mwtab(self): + """Save :class:`~mwtab.mwtab.MWTabFile` in `mwtab` formatted string. + + :return: NMR-STAR string. + :rtype: :py:class:`str` + """ + self._set_key_order() + mwtab_str = io.StringIO() + self.print_file(mwtab_str) + return mwtab_str.getvalue() + + @staticmethod + def _is_mwtab(string): + """Test if input string is in `mwtab` format. + + :param string: Input string. + :type string: :py:class:`str` or :py:class:`bytes` + :return: Input string if in mwTab format or False otherwise. + :rtype: :py:class:`str` or :py:obj:`False` + """ + if isinstance(string, str): + lines = string.replace("\r", "\n").split("\n") + elif isinstance(string, bytes): + lines = string.decode("utf-8").replace("\r", "\n").split("\n") + else: + raise TypeError("Expecting or , but {} was passed".format(type(string))) + + lines = [line for line in lines if line] + header = lines[0] + + if header.startswith("#METABOLOMICS WORKBENCH"): + return "\n".join(lines) + return False + + @staticmethod + def _is_json(string, duplicate_keys=False, force=False): + """Test if input string is in JSON format. + + :param string: Input string. + :type string: :py:class:`str` or :py:class:`bytes` + :param duplicate_keys: if true, replace some dictionaries with DuplicatesDict. + :type text: py:class:`bool` + :param force: if true, replace non-dictionary values in METABOLITES_DATA, METABOLITES, and EXTENDED with empty dicts. + :type text: py:class:`bool` + :return: Input string if in JSON format or False otherwise. + :rtype: :py:class:`str` or :py:obj:`False` + """ + try: + if isinstance(string, bytes): + if duplicate_keys: + json_str = json.loads(string.decode("utf-8"), object_pairs_hook=_handle_duplicate_keys) + else: + json_str = json.loads(string.decode("utf-8")) + elif isinstance(string, str): + if duplicate_keys: + json_str = json.loads(string, object_pairs_hook=_handle_duplicate_keys) + else: + json_str = json.loads(string) + else: + raise TypeError("Expecting or , but {} was passed".format(type(string))) + + if duplicate_keys and isinstance(json_str, DuplicatesDict): + raise KeyError('Given JSON contains duplicate keys at the highest level.') + + if duplicate_keys: + for i, ssf_dict in enumerate(json_str['SUBJECT_SAMPLE_FACTORS']): + if not isinstance(ssf_dict['Factors'], DuplicatesDict): + json_str['SUBJECT_SAMPLE_FACTORS'][i]['Factors'] = DuplicatesDict(ssf_dict['Factors']) + if 'Additional sample data' in ssf_dict and not isinstance(ssf_dict['Additional sample data'], DuplicatesDict): + json_str['SUBJECT_SAMPLE_FACTORS'][i]['Additional sample data'] = DuplicatesDict(ssf_dict['Additional sample data']) + + data_key = [n for n in json_str.keys() if "METABOLITE_DATA" in n or "BINNED_DATA" in n] + if data_key: + data_key = data_key[0] + for section_key, section_value in json_str[data_key].items(): + if section_key in ["Data", "Metabolites", "Extended"]: + indexes_to_delete = [] + for i, data_dict in enumerate(section_value): + if isinstance(data_dict, dict): + if duplicate_keys and not isinstance(data_dict, DuplicatesDict): + json_str[data_key][section_key][i] = DuplicatesDict(json_str[data_key][section_key][i]) + else: + if force: + indexes_to_delete.append(i) + else: + raise TypeError(f'Given JSON contains non-dictionary values in ["{data_key}"]["{section_key}"].') + for index in indexes_to_delete: + del json_str[data_key][section_key][index] + + # Have to tokenize the results file. + # "ST000071_AN000111_Results.txt UNITS:Peak area Has m/z:Yes Has RT:Yes RT units:Minutes" + for section_key, section_value in json_str.items(): + if isinstance(section_value, dict): + results_file_keys = [key for key in section_value if key.endswith('_RESULTS_FILE')] + if results_file_keys: + results_file_key = results_file_keys[0] + results_file_value = json_str[section_key][results_file_key] + results_file_dict = _results_file_line_to_dict(results_file_value) + + json_str[section_key][results_file_key] = results_file_dict + + return json_str + except ValueError: + return False + + def _create_result_file_string(self, section_key: str, key: str, to_format: str) -> str: + """Build result file string from its dictionary parts. + + Args: + section_key: The section where the result file dicitonary is. + key: The key where the result file is. + to_format: If 'mwtab', then formats the string for mwTab, otherwise formats for JSON. + + Returns: + A string to go in either the mwTab or JSON file. + """ + delimiter = "\t" if to_format == "mwtab" else " " + + if "filename" in self[section_key][key]: + new_value = self[section_key][key]["filename"] + else: + new_value = "" + + other_pair_strings = [] + for result_key, result_value in self[section_key][key].items(): + if result_key != "filename": + other_pair_strings.append(result_key + ":" + result_value) + + joined_pairs = delimiter.join(other_pair_strings) + + if new_value: + if joined_pairs: + new_value += delimiter + joined_pairs + elif joined_pairs: + new_value += joined_pairs + return new_value + + def _set_key_order(self): + """Set the key order to a specific order. + + Sets the key order to a certain order for better reproducibility and consistency. + """ + key_order = \ + {'METABOLOMICS WORKBENCH': {}, + 'PROJECT': {}, + 'STUDY': {}, + 'SUBJECT': {}, + 'SUBJECT_SAMPLE_FACTORS': {'Subject ID': [], + 'Sample ID': [], + 'Factors': [], + 'Additional sample data': []}, + 'COLLECTION': {}, + 'TREATMENT': {}, + 'SAMPLEPREP': {}, + 'CHROMATOGRAPHY': {}, + 'ANALYSIS': {}, + 'MS': {}, + 'NM': {}, + 'MS_METABOLITE_DATA': {'Units': [], + 'Data': ['Metabolite', 'Bin range(ppm)'], + 'Metabolites': ['Metabolite', 'Bin range(ppm)'], + 'Extended': ['Metabolite']}, + 'NMR_METABOLITE_DATA': {'Units': [], + 'Data': ['Metabolite', 'Bin range(ppm)'], + 'Metabolites': ['Metabolite', 'Bin range(ppm)'], + 'Extended': ['Metabolite']}, + 'NMR_BINNED_DATA': {'Units': [], 'Data': ['Metabolite', 'Bin range(ppm)']}} + + for key, sub_keys in key_order.items(): + if key in self: + # SUBJECT_SAMPLE_FACTORS is the only list as of now. + if isinstance(self[key], list): + temp_list = [] + for i, element in enumerate(self[key]): + temp_list.append({}) + for sub_key in sub_keys: + if sub_key in element: + temp_list[i][sub_key] = element[sub_key] + del self[key] + self[key] = temp_list + else: + temp_dict = {} + for sub_key, sub_sub_keys in sub_keys.items(): + if sub_key in self[key]: + # For the Data, Metabolites, and Extended sections. + if isinstance(self[key][sub_key], list): + temp_list = [] + for i, element in enumerate(self[key][sub_key]): + temp_list.append({}) + for sub_sub_key in sub_sub_keys: + if sub_sub_key in element: + temp_list[i][sub_sub_key] = element[sub_sub_key] + # Add the elements that aren't in the key_order. + for unordered_key in element: + if unordered_key not in temp_list[i]: + temp_list[i][unordered_key] = element[unordered_key] + if self._duplicate_keys: + temp_list[i] = DuplicatesDict(temp_list[i]) + temp_dict[sub_key] = temp_list + else: + temp_dict[sub_key] = self[key][sub_key] + # Add any unknown sub_keys that aren't in key_order. + for unordered_sub_key in self[key]: + if unordered_sub_key not in temp_dict: + temp_dict[unordered_sub_key] = self[key][unordered_sub_key] + + del self[key] + self[key] = temp_dict + + # Handle unrecognized keys, put them at the end. + keys_to_move = [] + for key in self: + if key not in key_order: + keys_to_move.append(key) + + for key in keys_to_move: + temp = self[key] + del self[key] + self[key] = temp + + def __deepcopy__(self, memo): + new_tabfile = MWTabFile(self.source, self._duplicate_keys) + memo[id(new_tabfile)] = new_tabfile + for key, value in self.items(): + new_tabfile[key] = copy.deepcopy(value, memo) + for key, value in self.__dict__.items(): + new_tabfile.__dict__[key] = copy.deepcopy(value, memo) + return new_tabfile + + def __copy__(self): + new_tabfile = MWTabFile(self.source, self._duplicate_keys) + for key, value in self.items(): + new_tabfile[key] = copy.copy(value) + for key, value in self.__dict__.items(): + new_tabfile.__dict__[key] = copy.copy(value) + return new_tabfile + + + + diff --git a/src/mwtab/tokenizer.py b/src/mwtab/tokenizer.py new file mode 100644 index 0000000..fbeea10 --- /dev/null +++ b/src/mwtab/tokenizer.py @@ -0,0 +1,203 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +mwtab.tokenizer +~~~~~~~~~~~~~~~ + +This module provides the :func:`~mwtab.tokenizer.tokenizer` lexical analyzer for +`mwTab` format syntax. It is implemented as Python generator-based state +machine which generates (yields) tokens one at a time when :py:func:`next()` +is invoked on :func:`~mwtab.tokenizer.tokenizer` instance. + +Each token is a tuple of "key-value"-like pairs, tuple of +``SUBJECT_SAMPLE_FACTORS`` or tuple of data deposited between +``*_START`` and ``*_END`` blocks. +""" + +from __future__ import print_function, division, unicode_literals +from collections import deque, namedtuple +import re +import traceback +import sys + + +KeyValue = namedtuple("KeyValue", ["key", "value"]) + +def _results_file_line_to_dict(line: str): + """Parse a RESULTS_FILE line into a dictionary. + + Args: + line: The line to parse. Expected to just be the value without "RESULTS_FILE" in it. + + Returns: + A dictionary of the values found in the line, won't always have every key. + """ + filename_regex = r'(\s*)([^\s]+?\s*?)((UNITS|Has m/z|Has RT|RT units)|$)' + units_regex = r'(.*)UNITS:(.*?)((\s(Has m/z|Has RT|RT units))|$)' + has_mz_regex = r'(.*)Has m/z:(.*?)((\s(UNITS|Has RT|RT units))|$)' + has_rt_regex = r'(.*)Has RT:(.*?)((\s(Has m/z|UNITS|RT units))|$)' + rt_units_regex = r'(.*)RT units:(.*?)((\s(Has m/z|Has RT|UNITS))|$)' + + filename_value = None if not (match := re.match(filename_regex, line)) else match.group(2).rstrip() + units_value = None if not (match := re.match(units_regex, line)) else match.group(2).strip() + has_mz_value = None if not (match := re.match(has_mz_regex, line)) else match.group(2).strip() + has_rt_value = None if not (match := re.match(has_rt_regex, line)) else match.group(2).strip() + rt_units_value = None if not (match := re.match(rt_units_regex, line)) else match.group(2).strip() + + results_file_dict = {} + if filename_value: + results_file_dict['filename'] = filename_value + if units_value: + results_file_dict['UNITS'] = units_value + if has_mz_value: + results_file_dict['Has m/z'] = has_mz_value + if has_rt_value: + results_file_dict['Has RT'] = has_rt_value + if rt_units_value: + results_file_dict['RT units'] = rt_units_value + + return results_file_dict + + +def tokenizer(text, dict_type = None): + """A lexical analyzer for the `mwtab` formatted files. + + :param text: `mwTab` formatted text. + :type text: :py:class:`str` + :param dict_type: the type of dictionary to use, default is dict. + :return: Tuples of data. + :rtype: :py:class:`~collections.namedtuple` + """ + if dict_type is None: + dict_type = dict + + stream = deque(text.split("\n")) + + while len(stream) > 0: + line = stream.popleft() + try: + + # header + if line.startswith("#METABOLOMICS WORKBENCH"): + yield KeyValue("#METABOLOMICS WORKBENCH", "\n") + for i, identifier in enumerate(re.split(r' |\t', line)): + if ":" in identifier: + key, value = identifier.split(":") + yield KeyValue(key, value) + + # SUBJECT_SAMPLE_FACTORS header (reached new section) + elif line.startswith("#SUBJECT_SAMPLE_FACTORS:"): + yield KeyValue("#ENDSECTION", "\n") + yield KeyValue("#SUBJECT_SAMPLE_FACTORS", "\n") + + # section header (reached new section) + elif line.startswith("#"): + yield KeyValue("#ENDSECTION", "\n") + yield KeyValue(line.strip(), "\n") + + # SUBJECT_SAMPLE_FACTORS line + elif line.startswith("SUBJECT_SAMPLE_FACTORS"): + line_items = line.split("\t") + + factor_dict = dict_type() + colon_split = line_items[3].split(":") + factor_items = [item for factor_item in colon_split for item in factor_item.rsplit('| ', 1)] + element_indexes_without_bar = [i+1 for i, value in enumerate(colon_split[1:-1]) if '| ' not in value] + if element_indexes_without_bar: + index = element_indexes_without_bar[0] + factor_item_start = colon_split[index-1] + if '| ' in factor_item_start: + factor_item_start = factor_item_start.split('| ')[1] + factor_item_end = colon_split[index+1] + if '| ' in factor_item_end: + factor_item_end = factor_item_end.split('| ')[0] + factor_item = ':'.join([factor_item_start, + colon_split[index], + factor_item_end]) + message = ("Either a bar ('| ') separating 2 items is missing or there is an extra colon (':') " + "in the factor key value pair, '" + factor_item + "'") + raise ValueError(message) + + for key, value in zip(factor_items[0::2], factor_items[1::2]): + factor_dict[key.strip()] = value.strip() + + subject_sample_factors_dict = { + "Subject ID": line_items[1], + "Sample ID": line_items[2], + "Factors": factor_dict + } + + if len(line_items) > 4 and line_items[4]: + additional_data = dict_type() + equal_split = line_items[4].split("=") + add_items = [item for add_item in equal_split for item in add_item.rsplit('; ', 1)] + element_indexes_without_semicolon = [i+1 for i, value in enumerate(equal_split[1:-1]) if '; ' not in value] + if element_indexes_without_semicolon: + index = element_indexes_without_semicolon[0] + add_item_start = equal_split[index-1] + if '; ' in add_item_start: + add_item_start = add_item_start.split('; ')[1] + add_item_end = equal_split[index+1] + if '; ' in add_item_end: + add_item_end = add_item_end.split('; ')[0] + add_item = '='.join([add_item_start, + equal_split[index], + add_item_end]) + message = ("Either a semicolon ('; ') separating 2 items is missing or there is an extra equal sign ('=') " + "in the additional data key value pair, '" + add_item + "'") + raise ValueError(message) + + for key, value in zip(add_items[0::2], add_items[1::2]): + additional_data[key.strip()] = value.strip() + + subject_sample_factors_dict["Additional sample data"] = additional_data + yield KeyValue(line_items[0].strip(), subject_sample_factors_dict) + + # data start header + elif line.endswith("_START"): + yield KeyValue(line, "\n") + + # tokenize lines in data section till line ending with "_END" is reached + while not line.endswith("_END"): + line = stream.popleft() + if line.endswith("_END"): + yield KeyValue(line.strip(), "\n") + else: + data = line.split("\t") + data = [token.strip('" ') for token in data] + yield KeyValue(data[0], tuple(data)) + + # item line in item section (e.g. PROJECT, SUBJECT, etc..) + elif line: + if "_RESULTS_FILE" in line: + line_items = line.split("\t") + # Sometimes RESULTS_FILE is found in DATA sections like UNITS is. So key needs to be parsed out accordingly. + yield KeyValue(line_items[0].split(':')[1].strip(), _results_file_line_to_dict('\t'.join(line_items[1:]))) + else: + try: + key, value = line.split("\t", 1) + except ValueError as e: + raise ValueError("Expected a tab in the line.") from e + if ":" in key: + if ":UNITS" in key: + yield KeyValue("Units", value) + else: + yield KeyValue(key.strip()[3:], value) + else: + yield KeyValue(key.strip(), value.strip()) + + except IndexError as e: + message = traceback.format_exc() + "LINE WITH ERROR:\n\t" + repr(line) + raise IndexError(message) from e + # print(traceback.format_exc(), file=sys.stderr) + # raise IndexError("LINE WITH ERROR:\n\t" + repr(line)) from e + except ValueError as e: + message = traceback.format_exc() + "LINE WITH ERROR:\n\t" + repr(line) + raise ValueError(message) from e + # print(traceback.format_exc(), file=sys.stderr) + # raise ValueError("LINE WITH ERROR:\n\t" + repr(line)) from e + + # end of file + yield KeyValue("#ENDSECTION", "\n") + yield KeyValue("!#ENDFILE", "\n") # This is to ensure that tokenizer terminates when #END is missing. diff --git a/src/mwtab/validator.py b/src/mwtab/validator.py new file mode 100644 index 0000000..2dc1b69 --- /dev/null +++ b/src/mwtab/validator.py @@ -0,0 +1,1125 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +mwtab.validator +~~~~~~~~~~~~~~~ + +This module contains routines to validate consistency of the ``mwTab`` +formatted files, e.g. make sure that ``Samples`` and ``Factors`` +identifiers are consistent across the file, and make sure that all +required key-value pairs are present. +""" + +from datetime import datetime +from re import match +import io +import sys +import traceback +from collections.abc import Iterable + +import jsonschema +import pandas + +from .mwschema import ms_required_schema, nmr_required_schema, METABOLITE_NA_VALUES +from .mwschema import NA_VALUES as SCHEMA_NA_VALUES + +import mwtab +from mwtab import metadata_column_matching + +column_finders = metadata_column_matching.column_finders +NA_VALUES = metadata_column_matching.NA_VALUES + +VERBOSE = False +LOG = None + +VALIDATION_LOG_HEADER = \ +"""Validation Log +{} +mwtab Python Library Version: {} +Source: {} +Study ID: {} +Analysis ID: {} +File format: {}""" + + +SUFFIXES = {1: 'st', 2: 'nd', 3: 'rd'} +def ordinal_suffix(num: int) -> str: + """Return the ordinal suffix for the given integer. + """ + if 10 <= num % 100 <= 20: + suffix = 'th' + else: + suffix = SUFFIXES.get(num % 10, 'th') + return str(num) + suffix + +def format_column_name(column_name: str, position: int) -> str: + """Build better message for error printing. + + When a column name has duplicates it is more complicated to identify in an + error message, so we build a more complex description. Duplicate names should + end in a pattern like {{{_\\d_}}}. We can pull out the number and identify + which duplicate column (enumerating from the left) is the one needing a + description. + + Args: + column_name: The name of the column to build a message for. + position: A number indicating the position of the column when enumerating from the left most column. + Should start from 1. + + Returns: + A string message describing where the column is. + """ + if column_name.endswith('}}}'): + new_name = match(mwtab.duplicates_dict.DUPLICATE_KEY_REGEX, column_name).group(1) + duplicate_num = match(r'.*\{\{\{_(\d+)_\}\}\}', column_name).group(1) + duplicate_suffix = ordinal_suffix(int(duplicate_num)+1) + message = f'The {duplicate_suffix} "{new_name}" column at position {position}' + else: + message = f'The "{column_name}" column at position {position}' + + return message + + + +def validate_sub_section_uniqueness(mwtabfile): + """Validate that the sub-sections don't repeat. + + :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. + :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` + """ + errors = [] + if mwtabfile._duplicate_sub_sections: + for section_name, sub_section in mwtabfile._duplicate_sub_sections.items(): + for sub_section_name in sub_section: + errors.append({'message': "Error: The section, " + section_name + ", has a sub-section, " + + sub_section_name + ", that is duplicated.", + 'tags': ['consistency'], 'section': section_name, 'sub-section': sub_section_name, + 'ID': '1', 'name': 'Duplicate Sub-section'}) + return errors + + +def validate_header_lengths(mwtabfile): + """Validate that the headers are the correct size. + + :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. + :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` + """ + errors = [] + if mwtabfile._short_headers: + for section in mwtabfile._short_headers: + errors.append({'message': "Error: The section, " + section + ", has a mismatch between the " + "number of headers and the number of elements in each " + "line. Either a line(s) has more values than headers or " + "there are too few headers.", + 'tags': ['consistency'], 'section': section, 'sub-section': None, + 'ID': '2', 'name': 'Bad Headers'}) + return errors + + +def validate_factors(mwtabfile): + """Validate that the subject sample factors and factors in the metabolites data match. + + :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. + :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` + """ + errors = [] + if mwtabfile._factors: + factors_dict_1 = {sample: factors for sample, factors in mwtabfile._factors.items()} + factors_dict_2 = {i["Sample ID"]: i["Factors"] for i in mwtabfile["SUBJECT_SAMPLE_FACTORS"] if i["Sample ID"] in factors_dict_1} + if factors_dict_1 != factors_dict_2: + errors.append({'message': "Error: The factors in the METABOLITE_DATA section " + "and SUBJECT_SAMPLE_FACTORS section do not match.", + 'tags': ['consistency'], 'section': 'SUBJECT_SAMPLE_FACTORS', 'sub-section': None, + 'ID': '3', 'name': 'Factor Mismatch'}) + return errors + + +def validate_subject_samples_factors(mwtabfile): + """Validate ``SUBJECT_SAMPLE_FACTORS`` section. + + :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. + :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` + """ + if mwtabfile._input_format == 'mwtab': + location = 'SUBJECT_SAMPLE_FACTORS entry #{}' + index_corrector = lambda x: x+1 + else: + location = 'The SSF at ["SUBJECT_SAMPLE_FACTORS"][{}]' + index_corrector = lambda x: x + + subject_samples_factors_errors = list() + seen_samples = set() + for index, subject_sample_factor in enumerate(mwtabfile["SUBJECT_SAMPLE_FACTORS"]): + if subject_sample_factor["Sample ID"]: + if subject_sample_factor["Sample ID"] in seen_samples: + subject_samples_factors_errors.append( + {'message': f"Warning: {location.format(index_corrector(index))} has a duplicate Sample ID.", + 'tags': ['value'], 'section': 'SUBJECT_SAMPLE_FACTORS', 'sub-section': None, 'ID': '4', 'name': 'Duplicate Sample ID in SSF'} + ) + seen_samples.add(subject_sample_factor["Sample ID"]) + if subject_sample_factor.get("Factors"): + duplicate_keys = [re_match.group(1) for key in subject_sample_factor["Factors"] + if (re_match := match(mwtab.duplicates_dict.DUPLICATE_KEY_REGEX, key))] + + if duplicate_keys: + subject_samples_factors_errors.append( + {'message': f"Warning: {location.format(index_corrector(index))} has the " + "following duplicate keys in its Factors:\n\t" + + "\n\t".join(f'"{value}"' for value in duplicate_keys), + 'tags': ['value'], 'section': 'SUBJECT_SAMPLE_FACTORS', 'sub-section': None, 'ID': '5', 'name': 'Duplicate Factors in SSF'}) + + if subject_sample_factor.get("Additional sample data"): + duplicate_keys = [re_match.group(1) for key in subject_sample_factor["Additional sample data"] + if (re_match := match(mwtab.duplicates_dict.DUPLICATE_KEY_REGEX, key))] + + if duplicate_keys: + subject_samples_factors_errors.append( + {'message': f"Warning: {location.format(index_corrector(index))} has the " + "following duplicate keys in its Additional sample data:\n\t" + + "\n\t".join(f'"{value}"' for value in duplicate_keys), + 'tags': ['value'], 'section': 'SUBJECT_SAMPLE_FACTORS', 'sub-section': None, 'ID': '6', 'name': 'Duplicate Additional Data'}) + return subject_samples_factors_errors + + +def validate_data(mwtabfile, data_section_key, mwtabfile_tables): + """Validates ``MS_METABOLITE_DATA``, ``NMR_METABOLITE_DATA``, and ``NMR_BINNED_DATA`` sections. + + :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. + :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` + :param data_section_key: Section key (either MS_METABOLITE_DATA, NMR_METABOLITE_DATA, or NMR_BINNED_DATA) + :type data_section_key: :py:class:`str` + :param mwtabfile_tables: Dictionary where the keys are table names and the values are the tables as pandas DataFrames. + :type mwtabfile_tables: :py:class:`dict` + """ + data_errors = list() + + subject_sample_factors_sample_id_set = {subject_sample_factor["Sample ID"] for subject_sample_factor in mwtabfile["SUBJECT_SAMPLE_FACTORS"]} + if mwtabfile._samples: + data_sample_id_set = set(mwtabfile._samples) + elif mwtabfile[data_section_key].get("Data"): + data_keys = list(mwtabfile[data_section_key]["Data"][0].keys()) + data_sample_id_set = set(data_keys[1:]) + else: + data_sample_id_set = set() + + if mwtabfile._input_format == 'mwtab': + location = data_section_key + else: + location = f'["{data_section_key}"]["Data"]' + + if data_sample_id_set - subject_sample_factors_sample_id_set: + data_errors.append({'message': 'Error: SUBJECT_SAMPLE_FACTORS section missing sample ID(s). ' + 'The following IDs were found in the {} section but not in the SUBJECT_SAMPLE_FACTORS:\n\t{}'.format( + location, + "\n\t".join(f'"{value}"' for value in sorted(list(data_sample_id_set - subject_sample_factors_sample_id_set))), + ), + 'tags': ['consistency'], 'section': 'SUBJECT_SAMPLE_FACTORS', 'sub-section': None, 'ID': '7', 'name': 'Missing Sample ID(s) in SSF'}) + + # Check if there are duplicate sample names. + if mwtabfile._samples and (len(mwtabfile._samples) > len(set(mwtabfile._samples))): + data_errors.append({'message': "Warning: There are duplicate samples in the " + f"{location} section.", + 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Data', + 'ID': '8', 'name': 'Duplicate Samples in DATA'}) + + # Check whether mwtabolites in Data are in the Metabolites section. + if mwtabfile._input_format == 'mwtab': + metabolites_location = 'METABOLITES' + data_location = data_section_key + else: + metabolites_location = f'["{data_section_key}"]["Metabolites"]' + data_location = f'["{data_section_key}"]["Data"]' + + # If either table does not have a 'Metabolite' column, then a message will be + # printed about not being able to find any metabolite. There is a separate + # message about not having the column doen elsewhere, so don't run the + # 'Metabolite in DATA not METABOLITES' check unless both tables have then column. + both_have_metabolites_column = True + metabolites_in_data_section = pandas.Series() + if mwtabfile_tables['Data'] is not None and not mwtabfile_tables['Data'].empty: + if 'Metabolite' in mwtabfile_tables['Data'].columns: + metabolites_in_data_section = mwtabfile_tables['Data'].loc[:, 'Metabolite'].str.strip() + else: + both_have_metabolites_column = False + + metabolites_in_metabolites = pandas.Series() + if mwtabfile_tables['Metabolites'] is not None and not mwtabfile_tables['Metabolites'].empty: + if 'Metabolite' in mwtabfile_tables['Metabolites'].columns: + metabolites_in_metabolites = mwtabfile_tables['Metabolites'].loc[:, 'Metabolite'].str.strip() + else: + both_have_metabolites_column = False + + if both_have_metabolites_column: + data_not_in_met_mask = ~metabolites_in_data_section.isin(metabolites_in_metabolites) + data_not_in_met = metabolites_in_data_section[data_not_in_met_mask] + if len(data_not_in_met) > 0 and 'BINNED' not in data_section_key: + message = (f'Error: The following metabolites in the, {data_location} table ' + f'were not found in the {metabolites_location} table:\n\t') + message = message + '\n\t'.join(f'"{value}"' for value in data_not_in_met.values) + data_errors.append({'message': message, 'tags': ['consistency'], 'section': data_section_key, 'sub-section': 'Data', + 'ID': '9', 'name': 'Metabolite(s) in DATA not METABOLITES'}) + + if metabolites_in_data_section.isna().any() or metabolites_in_data_section.isin(METABOLITE_NA_VALUES).any(): + message = f'Error: A metabolite without a name was found in the {data_location} table.' + data_errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Data', + 'ID': '10', 'name': 'Blank Metabolite(s) in DATA'}) + + duplicate_metabolites_mask = metabolites_in_data_section.duplicated() + if duplicate_metabolites_mask.any(): + duplicate_metabolites = metabolites_in_data_section[duplicate_metabolites_mask] + message = f'Warning: The following metabolites in the {data_location} table appear more than once in the table:\n\t' + message = message + '\n\t'.join(f'"{value}"' for value in duplicate_metabolites.values) + data_errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Data', + 'ID': '11', 'name': 'Duplicate Metabolite(s) in DATA'}) + + return data_errors + + +def validate_metabolites(mwtabfile, data_section_key, mwtabfile_tables): + """Validate ``METABOLITES`` section. + + :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. + :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` + :param data_section_key: Section key (either MS_METABOLITE_DATA, NMR_METABOLITE_DATA, or NMR_BINNED_DATA) + :type data_section_key: :py:class:`str` + :param mwtabfile_tables: Dictionary where the keys are table names and the values are the tables as pandas DataFrames. + :type mwtabfile_tables: :py:class:`dict` + """ + implied_pairs = metadata_column_matching.implied_pairs + + metabolites_errors = list() + + if mwtabfile._input_format == 'mwtab': + metabolites_location = 'METABOLITES' + data_location = data_section_key + else: + metabolites_location = f'["{data_section_key}"]["Metabolites"]' + data_location = f'["{data_section_key}"]["Data"]' + + # If either table does not have a 'Metabolite' column, then a message will be + # printed about not being able to find any metabolite. There is a separate + # message about not having the column doen elsewhere, so don't run the + # 'Metabolite in METABOLITES not DATA' check unless both tables have then column. + both_have_metabolites_column = True + metabolites_in_data_section = pandas.Series() + if mwtabfile_tables['Data'] is not None and not mwtabfile_tables['Data'].empty: + if 'Metabolite' in mwtabfile_tables['Data'].columns: + metabolites_in_data_section = mwtabfile_tables['Data'].loc[:, 'Metabolite'].str.strip() + else: + both_have_metabolites_column = False + + metabolites_in_metabolites = pandas.Series() + if mwtabfile_tables['Metabolites'] is not None and not mwtabfile_tables['Metabolites'].empty: + if 'Metabolite' in mwtabfile_tables['Metabolites'].columns: + metabolites_in_metabolites = mwtabfile_tables['Metabolites'].loc[:, 'Metabolite'].str.strip() + else: + both_have_metabolites_column = False + + + if both_have_metabolites_column: + met_not_in_data_mask = ~metabolites_in_metabolites.isin(metabolites_in_data_section) + met_not_in_data = metabolites_in_metabolites[met_not_in_data_mask] + if len(met_not_in_data) > 0: + message = (f'Error: The following metabolites in the {metabolites_location} table ' + f'were not found in the {data_location} table:\n\t') + message = message + '\n\t'.join(f'"{value}"' for value in met_not_in_data.values) + metabolites_errors.append({'message': message, 'tags': ['consistency'], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '12', 'name': 'Metabolite(s) in METABOLITES not DATA'}) + + if metabolites_in_metabolites.isna().any() or metabolites_in_metabolites.isin(METABOLITE_NA_VALUES).any(): + message = f'Error: A metabolite without a name was found in the {metabolites_location} table.' + metabolites_errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '13', 'name': 'Blank Metabolite(s) in METABOLITES'}) + + duplicate_metabolites_mask = metabolites_in_metabolites.duplicated() + if duplicate_metabolites_mask.any(): + duplicate_metabolites = metabolites_in_metabolites[duplicate_metabolites_mask] + message = f'Warning: The following metabolites in the {metabolites_location} table appear more than once in the table:\n\t' + message = message + '\n\t'.join(f'"{value}"' for value in duplicate_metabolites.values) + metabolites_errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '14', 'name': 'Duplicate Metabolite(s) in METABOLITES'}) + + # Check if fields/columns are recognized variations and report the standardized name to the user. + df = mwtabfile_tables['Metabolites'] + columns = {column:column.lower().strip() for column in df.columns} + found_columns = {} + columns_to_standard_columns = {} + for name, finder in column_finders.items(): + if column_matches := finder.name_dict_match(columns): + found_columns[name] = column_matches + if name not in df.columns: + for column_name in column_matches: + # We ignore if the column name is only off due to capitalization because lowering is common and easy. + if column_name.lower() != name: + message = (f'Warning: {format_column_name(column_name, df.columns.get_loc(column_name)+1)} ' + f'in the {metabolites_location} table, ' + f'matches a standard column name, "{name}". ' + 'If this match was not in error, the column should be renamed to ' + 'the standard name or a name that doesn\'t resemble the standard name.') + metabolites_errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '15', 'name': 'Standard Column Name Match'}) + for column_name in column_matches: + if column_name in columns_to_standard_columns: + columns_to_standard_columns[column_name].append(name) + else: + columns_to_standard_columns[column_name] = [name] + + value_mask = finder.values_series_match(df.loc[:, column_name].astype('string[pyarrow]'), na_values = NA_VALUES) + if not value_mask.all(): + message = (f'Warning: {format_column_name(column_name, df.columns.get_loc(column_name)+1)} ' + f'in the {metabolites_location} table, ' + f'matches a standard column name, "{name}", ' + 'and some of the values in the column do not match the expected type or format for that column. ' + 'The non-matching values are:\n') + message += df.loc[~value_mask, column_name].to_string() + metabolites_errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '16', 'name': 'METABOLITES Bad Standard Values'}) + + # When certain columns are found in METABOLITES, look for the implied pair and warn if it isn't there. + # For example, other_id and other_id_type and retention_index and retention_index_type. + # Also check that they both have data in the same rows. + for name, matches in found_columns.items(): + if name in implied_pairs: + implied_pairs_not_found = [column for column in implied_pairs[name] if column not in found_columns] + for column in implied_pairs_not_found: + message = (f'Warning: The column "{matches[0]}" was found in the {metabolites_location} table, ' + f'but this column implies that another column, "{column}", ' + 'should also exist, and that column was not found.') + metabolites_errors.append({'message': message, 'tags': [], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '17', 'name': 'Missing Implied Column'}) + + implied_pairs_found = [column for column in implied_pairs[name] if column in found_columns] + for column in implied_pairs_found: + parent_mask = (df.loc[:, matches[0]].isna() | df.loc[:, matches[0]].isin(NA_VALUES)) + child_mask = (df.loc[:, found_columns[column][0]].isna() | df.loc[:, found_columns[column][0]].isin(NA_VALUES)) + if (parent_mask != child_mask).any(): + message = (f'Warning: The column pair, "{matches[0]}" and "{found_columns[column][0]}", ' + 'in the METABOLITES table should have data in the ' + 'same rows, but at least one row has data in one ' + 'column and nothing in the other.') + metabolites_errors.append({'message': message, 'tags': [], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '18', 'name': 'Paired Columns Value Mismatch'}) + + # If the other_id column is found, print message about making individual database ID columns. + # I thought about checking to see if there were database IDs in the column first, but I'm not sure if it's worth the effort. + if 'other_id' in found_columns: + message = ('Warning: The standard column, "other_id", was ' + f'found in the METABOLITES table as "{found_columns["other_id"][0]}". ' + 'If this column contains database IDs for standard databases such ' + 'as KEGG, PubChem, HMDB, etc., it is recommended to make individual ' + 'columns for these and not lump them together into a less descriptive ' + '"other_id" column.') + metabolites_errors.append({'message': message, 'tags': [], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '19', 'name': '"other_id" Column'}) + + # If a column in df matches multiple standard names, print a warning to prefer separating them. + for column_name, standard_names in columns_to_standard_columns.items(): + if len(standard_names) > 1: + message = (f'Warning: The column, "{column_name}", in the {metabolites_location} table ' + f'was matched to multiple standard names, {standard_names}. This is a good indication ' + 'that the values in that column should be split into the appropriate ' + 'individual columns.') + metabolites_errors.append({'message': message, 'tags': [], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '20', 'name': 'Multiple Standard Name Match'}) + + return metabolites_errors + + +def validate_extended(mwtabfile, data_section_key, mwtabfile_tables): + """Validate ``EXTENDED_MS_METABOLITE_DATA``, ``EXTENDED_NMR_METABOLITE_DATA``, and ``EXTENDED_NMR_BINNED_DATA`` sections. + + :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. + :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` + :param data_section_key: Section key (either MS_METABOLITE_DATA, NMR_METABOLITE_DATA, or NMR_BINNED_DATA) + :type data_section_key: :py:class:`str` + :param mwtabfile_tables: Dictionary where the keys are table names and the values are the tables as pandas DataFrames. + :type mwtabfile_tables: :py:class:`dict` + """ + extended_errors = list() + + sample_id_set = {subject_sample_factor["Sample ID"] for subject_sample_factor in + mwtabfile["SUBJECT_SAMPLE_FACTORS"]} + + if mwtabfile._input_format == 'mwtab': + extended_location = 'EXTENDED_METABOLITE_DATA' + ssf_string = 'in the SUBJECT_SAMPLE_FACTORS section' + + else: + extended_location = f' in ["{data_section_key}"]["Extended"]' + ssf_string = 'in ["SUBJECT_SAMPLE_FACTORS"]' + + df = mwtabfile_tables['Extended'] + if "sample_id" not in df.columns: + message = f"Error: The {extended_location} table does not have a column for \"sample_id\"." + extended_errors.append({'message': message, 'tags': ['format'], 'section': data_section_key, 'sub-section': 'Extended', + 'ID': '21', 'name': 'Missing "sample_id" in EXTENDED'}) + else: + extended_id_set = set(df.loc[:, 'sample_id']) + not_in_ssf = extended_id_set - sample_id_set + if not_in_ssf: + message = (f"Error: The {extended_location} table has Sample IDs that were not found " + f"{ssf_string}. Those IDs are:\n\t" + '\n\t'.join(f'"{value}"' for value in not_in_ssf)) + extended_errors.append({'message': message, 'tags': ['consistency'], 'section': data_section_key, 'sub-section': 'Extended', + 'ID': '22', 'name': 'Missing Sample ID(s) in EXTENDED'}) + + if df.loc[:, 'sample_id'].isna().any() or df.loc[:, 'sample_id'].isin(SCHEMA_NA_VALUES).any(): + message = f'Error: A Sample ID without a name was found in the {extended_location} table.' + extended_errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Extended', + 'ID': '35', 'name': 'Blank Sample ID(s) in EXTENDED'}) + + if 'Metabolite' in df.columns and (df.loc[:, 'Metabolite'].isna().any() or df.loc[:, 'Metabolite'].isin(METABOLITE_NA_VALUES).any()): + message = f'Error: A metabolite without a name was found in the {extended_location} table.' + extended_errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Extended', + 'ID': '36', 'name': 'Blank Metabolite(s) in EXTENDED'}) + + return extended_errors + + +def validate_metabolite_names(mwtabfile, data_section_key): + """Validate that a header didn't get identified as a metabolite. + + :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. + :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` + :param data_section_key: Section key (either MS_METABOLITE_DATA, NMR_METABOLITE_DATA, or NMR_BINNED_DATA) + :type data_section_key: :py:class:`str` + """ + # Lowered versions of headers to look for, and common mispellings. + list_headers = ["samples", "factors", "bin range(ppm)", "metabolite_name", "metabolite name"] + + if "Metabolites" in mwtabfile[data_section_key]: + metabolites_section_bad_names = [data_dict["Metabolite"] for data_dict in mwtabfile[data_section_key]["Metabolites"] + if 'Metabolite' in data_dict and data_dict["Metabolite"].lower() in list_headers] + else: + metabolites_section_bad_names = [] + + if "Data" in mwtabfile[data_section_key]: + data_section_bad_names = [data_dict["Metabolite"] for data_dict in mwtabfile[data_section_key]["Data"] + if 'Metabolite' in data_dict and data_dict["Metabolite"].lower() in list_headers] + else: + data_section_bad_names = [] + + if "Extended" in mwtabfile[data_section_key]: + extended_section_bad_names = [data_dict["Metabolite"] for data_dict in mwtabfile[data_section_key]["Extended"] + if 'Metabolite' in data_dict and data_dict["Metabolite"].lower() in list_headers] + else: + extended_section_bad_names = [] + + if mwtabfile._input_format == 'mwtab': + desc_string = 'in the {} table' + metabolite_string = 'METABOLITES' + data_string = 'METABOLITE_DATA' if not 'BINNED' in data_section_key else 'BINNED_DATA' + extended_string = 'EXTENDED_METABOLITE_DATA' + else: + desc_string = f'in the table at ["{data_section_key}"]{{}}' + metabolite_string = '[\"Metabolites\"]' + data_string = '[\"Data\"]' + extended_string = '[\"Extended\"]' + errors = [] + message = ("Warning: There is a metabolite name, " + f"\"{{}}\", {desc_string} that is probably wrong. " + "It is close to a header name and is likely due to a badly constructed Tab file.") + for name in metabolites_section_bad_names: + errors.append({'message': message.format(name, metabolite_string), + 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '23', 'name': 'Bad Metabolite Name'}) + for name in data_section_bad_names: + errors.append({'message': message.format(name, data_string), + 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Data', + 'ID': '23', 'name': 'Bad Metabolite Name'}) + for name in extended_section_bad_names: + errors.append({'message': message.format(name, extended_string), + 'tags': ['value'], 'section': data_section_key, 'sub-section': 'Extended', + 'ID': '23', 'name': 'Bad Metabolite Name'}) + + return errors + + + +def _format_section_name(section, subsection): + section_name = section + if 'METABOLITE_DATA' in section: + if subsection == 'Extended': + section_name = 'EXTENDED_' + section + elif subsection == 'Metabolites': + section_name = 'METABOLITES' + return section_name + + +def _gen_jsonschema_format_string(error: jsonschema.ValidationError, + error_path_len: int, + schema: dict, + mwtabfile: 'mwtab.mwtab.MWTabFile') -> [str, bool|None, str|None, str|None]: + """Generates a few variables that create_better_error_messages will use in creating better error messages. + + Args: + error: The error to create messages for. + error_path_len: The len of error.relative_path. + schema: The jsonschema given to create_better_error_messages. + mwtabfile: The mwtabfile that was validated against. + + Returns: + A tuple where the first element is a format string used in messages, + the second is a boolean that is True if the key the error is about is required, + the third element is the section of the mwTab file the error came from + (None when this does not make sense), and + the fourth element is the subsection of the mwTab file the error came from + (None when this does not make sense). + The boolean will be None when key requirement does not make any sense. + """ + format_string = '' + key_is_required = None + section = None + subsection = None + # This should be an additional properties error. + if error_path_len == 0: + pass + # 4 levels deep should be an issue with a metabolite in MS_METABOLITE_DATA or something in SUBJECT_SAMPLE_FACTORS. + elif error_path_len == 4: + section = error.relative_path[0] + subsection = error.relative_path[1] + element = error.relative_path[2] + result_key = error.relative_path[3] + + if isinstance(subsection, int): + if mwtabfile._input_format == 'mwtab': + format_string = f'for the "{result_key}" in "{element}" in entry {subsection+1} of the "{_format_section_name(section, subsection)}" section' + else: + format_string = f'in ["{section}"][{subsection}]["{element}"]["{result_key}"]' + + # This is likely a key in the Factors or Additional data dicts of SUBJECT_SAMPLE_FACTORS, so no keys are required. + required = [] + else: + if mwtabfile._input_format == 'mwtab': + format_string = f'in the "{_format_section_name(section, subsection)}" section, in row number {element+1}, for the "{result_key}" column' + else: + format_string = f'in ["{section}"]["{subsection}"][{element}]["{result_key}"]' + + required = schema['properties'][section]['properties'][subsection]['items'].get('required') + key_is_required = required and result_key in required + + # MS_RESULTS_FILE or NMR_RESULTS_FILE or SUBJECT_SAMPLE_FACTORS. + elif error_path_len == 3: + section = error.relative_path[0] + subsection = error.relative_path[1] + result_key = error.relative_path[2] + + if isinstance(subsection, int): + if mwtabfile._input_format == 'mwtab': + format_string = f'for the "{result_key}" in entry {subsection+1} of the "{_format_section_name(section, subsection)}" section' + else: + format_string = f'in ["{section}"][{subsection}]["{result_key}"]' + + required = schema['properties'][section]['items'].get('required') + else: + if mwtabfile._input_format == 'mwtab': + format_string = f'for the subsection, "{subsection}", in the "{_format_section_name(section, subsection)}" section, for the "{result_key}" attribute' + else: + format_string = f'in ["{section}"]["{subsection}"]["{result_key}"]' + + required = schema['properties'][section]['properties'][subsection].get('required') + key_is_required = required and result_key in required + + elif error_path_len == 1: + section = error.relative_path[0] + + if mwtabfile._input_format == 'mwtab': + format_string = f'in the "{section}" section ' + else: + format_string = f'in ["{section}"]' + else: + section = error.relative_path[0] + subsection = error.relative_path[1] + + if mwtabfile._input_format == 'mwtab': + if section == 'METABOLOMICS WORKBENCH': + format_string = f'for "{subsection}" in the file header' + else: + format_string = f'for the subsection, "{subsection}", in the "{_format_section_name(section, subsection)}" section' + else: + format_string = f'in ["{section}"]["{subsection}"]' + + required = schema['properties'][section].get('required') + key_is_required = required and subsection in required + + return format_string, key_is_required, section, subsection + +VALIDATOR_TO_TAG = { + 'additionalProperties': ['format'], + 'minProperties': ['value'], + 'required': ['format'], + 'minLength': ['value'], + 'maxLength': ['value'], + 'minItems': ['value'], + 'type': ['format'], + 'enum': ['value'], + 'format': ['value'], + 'pattern': ['value'], + 'minimum': ['value'], + 'maximum': ['value'], + 'uniqueItems': ['value'], + 'not': ['value'] + } +def create_better_error_messages(errors_generator: Iterable[jsonschema.exceptions.ValidationError], + mwtabfile: 'mwtab.mwtab.MWTabFile', + schema: dict) -> list[str]: + """Create better error messages for jsonschema validation errors. + + Args: + errors_generator: The generator returned from validator.iter_errors(). + mwtabfile: The mwtabfile that was validated against. + schema: The schema used to create errors_generator. Used to get more information and craft better messages. + + Returns: + A list of the errors encountered. + """ + + # try: + # jsonschema.validate(instance=instance, schema=schema) + # except jsonschema.ValidationError as e: + # ## code to easily see the contents of the error for building a better message. + # for key, value in e._contents().items(): + # print(key, value) + # print() + + # for key, value in error._contents().items(): + # print(key, value) + # print() + + errors = [] + for error in errors_generator: + validator = error.validator + validator_value = error.validator_value + if prefix := error.schema.get(f'{validator}_prefix'): + message = prefix + else: + message = "Error: " + custom_message = "" + + + error_path_len = len(error.relative_path) + format_string, key_is_required, section, subsection = _gen_jsonschema_format_string(error, error_path_len, schema, mwtabfile) + + # There is one special case where 'not' is not only being used for {'not': {'enum':NA_VALUES}}. + # This is for IONIZATION in the MS section. This code is a more generalized approach to determine + # which schema within a 'oneOf' is the one that actually triggered the validation error. It + # is probably overkill for this special case, but might be relevant in the future. + if validator == "not" and 'oneOf' in error.schema['not']: + real_schema_found = False + i = 0 + while not real_schema_found and i < len(error.schema['not']['oneOf']): + try: + jsonschema.validate(error.instance, {'not':error.schema['not']['oneOf'][i]}) + except jsonschema.ValidationError as error2: + real_schema_found = True + custom_message_keys = [key for key in error2.schema['not'] if 'custom_message' in key] + error.schema = error2.schema['not'] + if custom_message_keys: + validator = match(r'(.*)_custom_message', custom_message_keys[0]).group(1) + else: + # Assuming that the schema is only a single keyword. + validator = list(error2.schema['not'].keys())[0] + i += 1 + + + if custom_message_attr := error.schema.get(f'{validator}_custom_message'): + custom_message = custom_message_attr + elif message_attr := error.schema.get(f'{validator}_message'): + message = message_attr + elif validator == "additionalProperties": + if 'were' in error.message: + bad_keys = match(r'.* \((.*) were unexpected\).*', error.message).group(1) + bad_keys = bad_keys.replace("'", '"') + if error_path_len == 0: + message = message + f'Unknown or invalid sections, {bad_keys}.' + else: + message = message + f'Unknown or invalid subsections, {bad_keys}, {format_string}.' + else: + bad_key = match(r'.* \(\'(.*)\' was unexpected\).*', error.message).group(1) + if error_path_len == 0: + message = message + f'Unknown or invalid section, "{bad_key}".' + else: + message = message + f'Unknown or invalid subsection, "{bad_key}", {format_string}.' + elif validator == "minProperties": + custom_message = " cannot be empty." + elif validator == "required": + required_property = match(r"\'(.*)\'", error.message).group(1) + message += f'The required property, "{required_property}", {format_string} is missing.' + # Commenting these out for now because they aren't used in mwschema and I don't want to test them. + # elif validator == "dependencies": + # message += "The entry " + "[%s]" % "][".join(repr(index) for index in error.relative_path) + " is missing a dependent property.\n" + # message += error.message + # elif validator == "dependentRequired": + # message += "The entry " + "[%s]" % "][".join(repr(index) for index in error.relative_path) + " is missing a dependent property.\n" + # message += error.message + elif validator == "minLength": + if validator_value == 1 and isinstance(error.instance, str): + custom_message = " cannot be an empty string." + else: + custom_message = " is too short." + elif validator == "maxLength": + custom_message = " is too long." + elif validator == "minItems": + if validator_value == 1: + custom_message = " cannot be empty." + else: + custom_message = " must have at least " + str(validator_value) + " items." + elif validator == "type": + if type(validator_value) == list: + custom_message = " is not any of the allowed types: [" + for allowed_type in validator_value: + custom_message += "\'" + allowed_type + "\', " + custom_message = custom_message[:-2] + custom_message += "]." + else: + custom_message = " is not of type \"" + validator_value + "\"." + elif validator == "enum": + custom_message = " is not one of [" + "%s" % ", ".join(repr(index) for index in validator_value) + "]." + elif validator == "format": + custom_message = " is not a valid " + validator_value + "." + elif validator == "pattern": + custom_message = " does not match the regular expression pattern " + str(validator_value) + elif validator == "minimum": + custom_message = " must be greater than or equal to " + str(validator_value) + "." + elif validator == "maximum": + custom_message = " must be less than or equal to " + str(validator_value) + "." + elif validator == "uniqueItems": + custom_message = " has non-unique elements." + # For now 'not' is assumed to only be for {'not': {'enum':NA_VALUES}}. + # If a new use case for 'not' gets added, see if adding 'not_custom_message' + # like what is done for Factors in SUBJECT_SAMPLE_FACTORS will do what is needed. + elif validator == 'not': + message = message + f'An empty value or a null value was detected {format_string}.' + + if mwtabfile._input_format == 'mwtab': + container_noun = 'subsection' + else: + container_noun = 'key' + + if key_is_required: + message = message + f' A legitimate value should be provided for this required {container_noun}.' + else: + message = message + f' Either a legitimate value should be provided for this {container_noun}, or it should be removed altogether.' + else: + message += error.message + + + if custom_message: + str_instance = str(error.instance) + if len(str_instance) < 50: + message = message + f"The value, \"{str_instance}\", {format_string}" + custom_message + else: + message = message + f"The value {format_string}" + custom_message + errors.append({'message': message, 'tags': VALIDATOR_TO_TAG.get(validator, []), + 'section': section, 'sub-section': subsection, + 'ID': '24', 'name': 'JSON Schema Error: ' + validator}) + return errors + + +def validate_schema(mwtabfile, schema): + """Validate section of ``mwTab`` formatted file. + + :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. + :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` + :param schema: JSON Schema schema. + :type schema: :py:class:`dict` + :return: JSON Schema errors. + :rtype: :py:class:`list` + """ + validator = jsonschema.validators.validator_for(schema) + format_checker = jsonschema.FormatChecker() + validator = validator(schema=schema, format_checker=format_checker) + # errors_generator = validator.iter_errors(mwtabfile) + return create_better_error_messages(validator.iter_errors(mwtabfile), mwtabfile, schema) + + + +def validate_table_values(mwtabfile, data_section_key, mwtabfile_tables, na_values = NA_VALUES): + """Validate the values of all table sections. + + :param mwtabfile: Instance of :class:`~mwtab.mwtab.MWTabFile`. + :type mwtabfile: :class:`~mwtab.mwtab.MWTabFile` + :param data_section_key: Section key (either MS_METABOLITE_DATA, NMR_METABOLITE_DATA, or NMR_BINNED_DATA) + :type data_section_key: :py:class:`str` + :param mwtabfile_tables: Dictionary where the keys are table names and the values are the tables as pandas DataFrames. + :type mwtabfile_tables: :py:class:`dict` + :param na_values: List of values to consider null values. + :type na_values: :py:class:`list` + """ + if mwtabfile._input_format == 'mwtab': + data_location = data_section_key + met_location = 'METABOLITES' + extended_location = 'EXTENDED_METABOLITE_DATA' + + header_names = { + 'Data': 'Bin range(ppm)' if 'BINNED' in data_section_key else 'Samples', + 'Metabolites': 'metabolite_name', + 'Extended': 'metabolite_name' + } + else: + data_location = f'["{data_section_key}"]["Data"]' + met_location = f'["{data_section_key}"]["Metabolites"]' + extended_location = f'["{data_section_key}"]["Extended"]' + + header_names = { + 'Data': 'Bin range(ppm)' if 'BINNED' in data_section_key else 'Metabolite', + 'Metabolites': 'Metabolite', + 'Extended': 'Metabolite' + } + + message_strings = { + 'Data': data_location, + 'Metabolites': met_location, + 'Extended': extended_location + } + headers = { + 'Data': mwtabfile._raw_binned_header if 'BINNED' in data_section_key else mwtabfile._raw_samples, + 'Metabolites': mwtabfile._raw_metabolite_header, + 'Extended': mwtabfile._raw_extended_metabolite_header + } + errors = [] + for table_name in mwtabfile.table_names: + if table_name in mwtabfile[data_section_key]: + if headers[table_name] and header_names[table_name] not in headers[table_name]: + message = (f"Error: The {message_strings[table_name]} table does not have a column for \"{header_names[table_name]}\". " + "It is likely misspelled or using a common incorrect substitute.") + errors.append({'message': message, 'tags': ['format'], 'section': data_section_key, 'sub-section': table_name, + 'ID': '34', 'name': 'Missing Header'}) + + records = mwtabfile[data_section_key][table_name] + if len(records) > 0: + columns = list(records[0].keys()) + if not all([list(data_dict.keys()) == columns for data_dict in records]): + message = (f'Error: The {message_strings[table_name]} table ' + 'does not have the same columns for every row.') + errors.append({'message': message, 'tags': ['consistency'], 'section': data_section_key, 'sub-section': table_name, + 'ID': '25', 'name': 'Inconsistent Columns'}) + + data_df = mwtabfile_tables[table_name] + + # Look for empty column names. + if any(name == '' for name in data_df.columns): + message = (f'Error: Column(s) with no name were found in the {message_strings[table_name]} table.') + errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': table_name, + 'ID': '26', 'name': 'Column With No Name'}) + + # Look for NA values that aren't the empty string. + # Hunter did not like this in validation since normalizing NA values is not too difficult as a cleaning step. + # na_values_sans_empty_string = [value for value in na_values if value != ''] + # values_mask = data_df.isin(na_values_sans_empty_string) + # if values_mask.any().any(): + # na_values_in_df = [f'"{value}"' for value in pandas.unique(data_df[values_mask].values.ravel()) if not pandas.isna(value)] + # message = (f'Warning: Nonstandard NA values were found in the {message_strings[table_name]} table. ' + # 'NA values should be expressed using the empty string unless ' + # 'the empty string means something else, such as "not found", ' + # 'in which case there should be both NA and NF values and no empty string. ' + # 'The following values were the nonstandard values found:\n\t') + # message = message + '\n\t'.join(na_values_in_df) + # errors.append(message) + + # Look for completely null columns. + null_columns = data_df.isna().all() | data_df.isin(na_values).all() + null_columns = null_columns[null_columns] + if len(null_columns) > 0: + for column in null_columns.index: + message = (f'Warning: {format_column_name(column, data_df.columns.get_loc(column)+1)} ' + f'in the {message_strings[table_name]} table has all null values.') + errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': table_name, + 'ID': '27', 'name': 'Null Column'}) + + # Look for overbalanced values, so if 90% of a column is dominated by a single value print a warning. + for i, column in enumerate([column for column in data_df.columns if column != 'Metabolite']): + temp_column = data_df.loc[:, column].astype(str) + value_counts = temp_column.value_counts(dropna=False) + value_counts = value_counts / value_counts.sum() + if any(value_counts > .9) and len(value_counts) > 1: + message = (f'Warning: {format_column_name(column, data_df.columns.get_loc(column)+1)} ' + f'in the {message_strings[table_name]} table may have incorrect values. ' + '90% or more of the values are the same, but 10% or less are different.') + errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': table_name, + 'ID': '28', 'name': 'Possible Bad Column Values'}) + + # Look for duplicate rows. + if data_df.duplicated().any(): + message = f"Warning: There are duplicate rows in the {message_strings[table_name]} table." + errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': table_name, + 'ID': '29', 'name': 'Duplicate Rows'}) + + # Look for duplicate column names. We skip Data because there is a separate check to look for duplicate samples. + if not table_name == 'Data': + columns = [column if not column.endswith('}}}') else match(mwtab.duplicates_dict.DUPLICATE_KEY_REGEX, column).group(1) + for column in data_df.columns] + if len(columns) > len(set(columns)): + message = f"Warning: There are duplicate column names in the {message_strings[table_name]} table." + errors.append({'message': message, 'tags': ['value'], 'section': data_section_key, 'sub-section': table_name, + 'ID': '30', 'name': 'Duplicate Column Names'}) + return errors + + +def validate_polarity(mwtabfile, data_section_key, mwtabfile_tables): + """Validate that polarity columns are mono valued. + + :param mwtabfile_tables: Dictionary where the keys are table names and the values are the tables as pandas DataFrames. + :type mwtabfile_tables: :py:class:`dict` + :param data_section_key: Section key (either MS_METABOLITE_DATA, NMR_METABOLITE_DATA, or NMR_BINNED_DATA) + :type data_section_key: :py:class:`str` + :param mwtabfile_tables: Dictionary where the keys are table names and the values are the tables as pandas DataFrames. + :type mwtabfile_tables: :py:class:`dict` + """ + if mwtabfile._input_format == 'mwtab': + location = 'METABOLITES' + else: + location = f'["{data_section_key}"]["Metabolites"]' + + errors = [] + df = mwtabfile_tables['Metabolites'] + column_finder = column_finders['polarity'] + columns = {column:column.lower().strip() for column in df.columns} + if column_matches := column_finder.name_dict_match(columns): + for column_match in column_matches: + pos_values = df.loc[:, column_match].str.lower().isin(['pos', 'positive', '+']) + neg_values = df.loc[:, column_match].str.lower().isin(['neg', 'negative', '+']) + if pos_values.any() and neg_values.any(): + message = (f'Error: The "{column_match}" column in the {location} table ' + 'indicates multiple polarities in a single analysis, and ' + 'this should not be. A single mwTab file is supposed to be ' + 'restricted to a single analysis. This means multiple MS ' + 'runs under different settings should each be in their own file.') + errors.append({'message': message, 'tags': ['format'], 'section': data_section_key, 'sub-section': 'Metabolites', + 'ID': '31', 'name': 'Multiple Polarities'}) + break + return errors + + +def validate_file(mwtabfile: 'mwtab.mwtab.MWTabFile', + ms_schema: dict = ms_required_schema, + nmr_schema: dict = nmr_required_schema, + verbose: bool = False) -> (str, list[dict]): + """Validate ``mwTab`` formatted file. + + Note that some of the validations are pretty strict to account for the majority of cases, + but if warranted could be ignored. For example, COLUMN_PRESSURE in the CHROMATOGRAPHY + section will print a warning if the value is not a single number or range of numbers + followed by a unit, but there might be some situations where the method is complex + and thus the column pressure is not static. So something like + "60 bar at starting conditions. 180 bar at %A" would be required to accurately + describe the COLUMN_PRESSURE, and would be valid. So in these kinds of situations + the warning printed can safely be ignored. + + Args: + mwtabfile: The file to be validated. + ms_schema: jsonschema to validate both the base parts of the file and the MS specific parts of the file. + nmr_schema: jsonschema to validate both the base parts of the file and the NMR specific parts of the file. + verbose: whether to be verbose or not. + + Returns: + Error messages as a single string and error messages in JSON form. If verbose is True, then the single string will be None. + """ + # setup + if not verbose: + error_stout = io.StringIO() + else: + error_stout = sys.stdout + + # generate validation log header(s) + file_format = mwtabfile.source.split("/")[-1] if "https://www.metabolomicsworkbench.org/" in mwtabfile.source else \ + mwtabfile.source.split(".")[1] + print(VALIDATION_LOG_HEADER.format( + str(datetime.now()), + mwtab.__version__, + mwtabfile.source, + mwtabfile.study_id, + mwtabfile.analysis_id, + file_format + ), file=error_stout) + + # create list to collect validation errors + errors = list() + + # Get tables as dataframes. + mwtabfile_tables = {} + for table_name in mwtabfile.table_names: + mwtabfile_tables[table_name] = mwtabfile.get_table_as_pandas(table_name) + + if 'NM' in mwtabfile: + errors.extend(validate_schema(mwtabfile, nmr_schema)) + else: + if 'MS' not in mwtabfile: + message = ('Error: No "MS" or "NM" section was found, ' + 'so analysis type could not be determined. ' + 'Mass spec will be assumed.') + errors.append({'message': message, 'tags': ['format'], 'section': None, + 'sub-section': None, 'ID': '32', 'name': 'No MS or NM Section'}) + errors.extend(validate_schema(mwtabfile, ms_schema)) + + # validate SUBJECT_SAMPLE_FACTORS + errors.extend(validate_subject_samples_factors(mwtabfile)) + errors.extend(validate_factors(mwtabfile)) + + # validate ..._DATA sections + data_section_key = mwtabfile.data_section_key + if data_section_key: + errors.extend(validate_data(mwtabfile, data_section_key, mwtabfile_tables)) + + if data_section_key in ("MS_METABOLITE_DATA", "NMR_METABOLITE_DATA"): + if "Metabolites" in mwtabfile[data_section_key].keys(): + errors.extend(validate_metabolites(mwtabfile, data_section_key, mwtabfile_tables)) + else: + if mwtabfile._input_format == 'mwtab': + location = 'METABOLITES' + else: + location = f'["{data_section_key}"]["Metabolites"]' + message = f"Warning: Missing {location} section." + errors.append({'message': message, 'tags': ['format'], 'section': None, + 'sub-section': None, 'ID': '33', 'name': 'Missing METABOLITES Section'}) + + if "Extended" in mwtabfile[data_section_key].keys(): + errors.extend(validate_extended(mwtabfile, data_section_key, mwtabfile_tables)) + + errors.extend(validate_metabolite_names(mwtabfile, data_section_key)) + errors.extend(validate_table_values(mwtabfile, data_section_key, mwtabfile_tables)) + errors.extend(validate_polarity(mwtabfile, data_section_key, mwtabfile_tables)) + + errors.extend(validate_header_lengths(mwtabfile)) + errors.extend(validate_sub_section_uniqueness(mwtabfile)) + + + # finish writing validation/error log + if errors: + print("Status: Contains Validation Issues", file=error_stout) + print("Number of Issues: {}\n".format(len(errors)), file=error_stout) + tags = [] + warning_count = 0 + error_messages = [] + for error in errors: + if error['message'].startswith('Warning'): + warning_count += 1 + tags += error['tags'] + error_messages.append(error['message']) + tag_counts = pandas.Series(tags).value_counts() + print("Number of Warnings: {}\n".format(warning_count), file=error_stout) + print("Number of Value Errors: {}\n".format(tag_counts.get('value', 0)), file=error_stout) + print("Number of Consistency Errors: {}\n".format(tag_counts.get('consistency', 0)), file=error_stout) + print("Number of Format Errors: {}\n".format(tag_counts.get('format', 0)), file=error_stout) + try: + print("Issue Log:\n" + "\n".join(error_messages), file=error_stout) + # I have only seen this exception when trying to run from Windows + # Command Prompt while also redirecting the output to a file. + # I have no idea how to get this to trigger through a test in pytest. + except UnicodeEncodeError: + try: + print("An error occurred when trying to print the issue log with unicode. Trying UTF-8.") + print(("Issue Log:\n" + "\n".join(error_messages)).encode('utf-8'), file=error_stout) + except Exception as e: + print("An error occurred when trying to print the issue log, so it could not be printed.") + print("Issue Log Exception:\n") + traceback.print_exception(e, file=error_stout) + else: + print("Status: Passing", file=error_stout) + + if verbose: + return None, errors + else: + return error_stout.getvalue(), errors + + diff --git a/tests/example_data/files_to_test_error_recovery/1_no_error.json b/tests/example_data/files_to_test_error_recovery/1_no_error.json new file mode 100644 index 0000000..e69de29 diff --git a/tests/example_data/files_to_test_error_recovery/2_error.json b/tests/example_data/files_to_test_error_recovery/2_error.json new file mode 100644 index 0000000..e69de29 diff --git a/tests/example_data/files_to_test_error_recovery/3_no_error.txt b/tests/example_data/files_to_test_error_recovery/3_no_error.txt new file mode 100644 index 0000000..e69de29 diff --git a/tests/example_data/other_mwtab_files/ST000022_AN000041.txt b/tests/example_data/other_mwtab_files/ST000022_AN000041.txt new file mode 100644 index 0000000..af2210f --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000022_AN000041.txt @@ -0,0 +1,407 @@ +#METABOLOMICS WORKBENCH wpathmasiri_20140228_9328071_mwtab.txt DATATRACK_ID:28 TEXT OUTPUT STUDY_ID:ST000022 ANALYSIS_ID:AN000041 PROJECT_ID:PR000021 +VERSION 1 +CREATED_ON 10-02-2015 +#PROJECT +PR:PROJECT_TITLE Johnston County Osteoarthritis Project +PR:PROJECT_TYPE Longitudinal study +PR:PROJECT_SUMMARY The Johnston County Osteoarthritis Project (JoCo) is a 20+ year ongoing, +PR:PROJECT_SUMMARY population-based, prospective cohort study of knee and hip OA designed to be +PR:PROJECT_SUMMARY representative of the civilian, non-institutionalized, African American or +PR:PROJECT_SUMMARY Caucasian population aged 45 years and older, who were residents of one of 6 +PR:PROJECT_SUMMARY townships in Johnston County NC for at least 1 year, and who were physically and +PR:PROJECT_SUMMARY mentally capable of completing the studys protocol. All participants had an +PR:PROJECT_SUMMARY initial home interview, a clinic examination with radiographs, and a subsequent +PR:PROJECT_SUMMARY second home interview. Approximately 3,200 individuals were recruited into the +PR:PROJECT_SUMMARY baseline evaluation between 1991 and 1997 (T0); the first follow up visit (T1) +PR:PROJECT_SUMMARY occurred from 1999-2004, and the second follow up visit (T2) from 2006-2010. +PR:PROJECT_SUMMARY Cohort enrichment (T1*) occurred from 2003-4, allowing new participants to be +PR:PROJECT_SUMMARY enrolled; the first follow up for these individuals was at T2. At T0, the sample +PR:PROJECT_SUMMARY was approximately 38% men and one-third African American. Between T0 and T1, +PR:PROJECT_SUMMARY radiographic knee OA, defined as Kellgren-Lawrence grade 2-4, developed in 12% +PR:PROJECT_SUMMARY of knees without knee OA at T0. +PR:INSTITUTE University of North Carolina at Chapel Hill +PR:DEPARTMENT Thurston Arthritis Research Center, Division of Rheumatology, Allergy, and +PR:DEPARTMENT Immunology +PR:LABORATORY Jordan Laboratory +PR:LAST_NAME Jordan +PR:FIRST_NAME Joanne +PR:ADDRESS 3300 Thurston Bldg., CB# 7280, Chapel Hill, NC 27599-7280 +PR:EMAIL wpathmasiri@rti.org +PR:PHONE 1-866-827-2762 +PR:FUNDING_SOURCE Center for Disease Control (CDC) +#STUDY +ST:STUDY_TITLE Biomarker Discovery in Knee Osteoarthritis (II) +ST:STUDY_TYPE Biomarker Discovery in Knee Osteoarthritis +ST:STUDY_SUMMARY This metabolomics pilot and feasibility (P & F) study was conducted to provide +ST:STUDY_SUMMARY data to be used to gain a better understanding of metabolic alterations in +ST:STUDY_SUMMARY people with knee osteoarthritis (OA) and to discover novel biomarkers of the +ST:STUDY_SUMMARY disease. The goal of the metabolomics study was to determine if metabolic +ST:STUDY_SUMMARY differences, detected by a comprehensive metabolomics analysis, can be used to +ST:STUDY_SUMMARY distinguish people who will develop symptomatic knee OA from those who will not. +ST:STUDY_SUMMARY For this metabolomics study, individuals participating in T1 or T1* with 5-year +ST:STUDY_SUMMARY follow-up at T2 were selected. At T2 subjects were on average 68.1(9.12) years +ST:STUDY_SUMMARY old with an average BMI of 31.4(7.01) with 32% men and one-third African +ST:STUDY_SUMMARY American. All had weight-bearing posterior-anterior knee films obtained with the +ST:STUDY_SUMMARY Synaflexer positioning device at both time points and read paired for +ST:STUDY_SUMMARY Kellgren-Lawrence grade and minimum joint space. Urine samples (second morning +ST:STUDY_SUMMARY void) collected from 36 overweight or obese participants in the JoCo at T1 or +ST:STUDY_SUMMARY T1* were selected from two subgroups (a group that developed radiographic +ST:STUDY_SUMMARY osteoarthritis (n=16) and an age, race, sex, and BMI matched group that did not +ST:STUDY_SUMMARY develop osteoarthritis (n=20). Radiographic knee OA was defined as +ST:STUDY_SUMMARY Kellgren-Lawrence grade 2-4 at T2 in a person with Kellgren-Lawrence grade 0 or +ST:STUDY_SUMMARY 1 at T1 or T1*. +ST:INSTITUTE RTI International +ST:DEPARTMENT Systems and Translational Sciences (STS) +ST:LABORATORY NIH Eastern Regional Comprehensive Metabolomics Resource Core (RTI RCMRC) +ST:LAST_NAME Sumner +ST:FIRST_NAME Susan +ST:ADDRESS 3040, East Cornwallis Road, Research Triangle Park, NC 27709 +ST:EMAIL ssumner@rti.org +ST:PHONE 919-541-7479 +ST:NUM_GROUPS 2 +ST:TOTAL_SUBJECTS 36 +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +SU:AGE_OR_AGE_RANGE Average age 68.1(9.12) +SU:WEIGHT_OR_WEIGHT_RANGE Average BMI of 31.4(7.01) +SU:GENDER Male, Female +SU:SPECIES_GROUP Human +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS - C0559 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0629 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0640 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0835 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0613 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0762 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1113 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1158 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D2090 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0004 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0195 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0225 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0309 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0487 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0036 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0108 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - A0233 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A0490 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A2003 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C0586 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C2177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0729 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0909 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0945 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D1174 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2054 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2062 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2079 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2111 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2404 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2532 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0546 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0607 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E2036 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - F0035 Disease Status:Control +#COLLECTION +CO:COLLECTION_SUMMARY Urine samples (second morning void) collected from 36 overweight or obese +CO:COLLECTION_SUMMARY participants in the JoCo at T1 or T1* were selected from two subgroups (a group +CO:COLLECTION_SUMMARY that developed radiographic osteoarthritis (n=16) and an age, race, sex, and BMI +CO:COLLECTION_SUMMARY matched group that did not develop osteoarthritis (n=20). Radiographic knee OA +CO:COLLECTION_SUMMARY was defined as Kellgren-Lawrence grade 2-4 at T2 in a person with +CO:COLLECTION_SUMMARY Kellgren-Lawrence grade 0 or 1 at T1 or T1*. +CO:SAMPLE_TYPE Urine +CO:STORAGE_CONDITIONS -80C +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Chenomx Internal Standard solution (70 ul) and 230 ul D20 was added to each of +SP:SAMPLEPREP_SUMMARY the 36 urine sample (400 ul), vortexed for 30s, and centrifuged at 12000 rcf for +SP:SAMPLEPREP_SUMMARY 5min. Chenomx ISTD (Chenomx, Edmonton, Alberta, Canada) contains 5mM +SP:SAMPLEPREP_SUMMARY 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS, Chemical Shift Indicator), 100 +SP:SAMPLEPREP_SUMMARY mM Imidazole (pH indicator), and 0.2% NaN3 (to inhibit bacterial growth) in D2O. +SP:SAMPLEPREP_SUMMARY 600 l aliquot of the supernatant was transferred into 5mm NMR tubes +SP:SAMPLEPREP_SUMMARY (Bruker-Biospin, Germany). Phenotypic pooled urine samples were made by +SP:SAMPLEPREP_SUMMARY combining 150 l aliquots from each of the study samples belonging to the same +SP:SAMPLEPREP_SUMMARY phenotype (cases and control). In addition, a combined phenotypic pooled sample +SP:SAMPLEPREP_SUMMARY was prepared by using 1000 l aliquot from each of the phenotypic pooled sample. +SP:SAMPLEPREP_SUMMARY Pooled NMR samples were prepared as described above and used as quality check +SP:SAMPLEPREP_SUMMARY (QC) samples. +SP:SAMPLEPREP_PROTOCOL_FILENAME Johnston_County_Procedure_March_4_2014.docx +SP:PROCESSING_METHOD Dilution using a mixture of D2O and Chenomx ISTD +SP:PROCESSING_STORAGE_CONDITIONS On ice +SP:EXTRACTION_METHOD None +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY None +CH:CHROMATOGRAPHY_TYPE - +CH:INSTRUMENT_NAME - +CH:COLUMN_NAME - +#ANALYSIS +AN:LABORATORY_NAME RTI, DHMRI +AN:ANALYSIS_TYPE NMR +AN:ACQUISITION_DATE 41527 +AN:SOFTWARE_VERSION TopSpin 3.0 +AN:OPERATOR_NAME Wimal Pathmasiri, Kevin Knagge +AN:DETECTOR_TYPE NMR +AN:DATA_FORMAT Bruker +#NMR +NM:INSTRUMENT_TYPE Bruker Avance III +NM:NMR_EXPERIMENT_TYPE 1D 1H +NM:FIELD_FREQUENCY_LOCK Deuterium +NM:STANDARD_CONCENTRATION 0.5mm +NM:SPECTROMETER_FREQUENCY 950 MHz +NM:NMR_PROBE 5mm, Inverse ATMA +NM:NMR_SOLVENT D2O +NM:NMR_TUBE_SIZE 5mm, 7 inch +NM:SHIMMING_METHOD Topshim +NM:PULSE_SEQUENCE noesypr1d +NM:WATER_SUPPRESSION presat +NM:PULSE_WIDTH 13.07 us +NM:POWER_LEVEL 12.589w +NM:RECEIVER_GAIN 4 +NM:OFFSET_FREQUENCY 4468.31 +NM:CHEMICAL_SHIFT_REF_CPD DSS +NM:TEMPERATURE 298 K +NM:NUMBER_OF_SCANS 64 +NM:DUMMY_SCANS 4 +NM:ACQUISITION_TIME 1.7301503 s +NM:RELAXATION_DELAY 2 s +NM:SPECTRAL_WIDTH 18939.395 Hz, 19.93 ppm +NM:NUM_DATA_POINTS_ACQUIRED 65536 +NM:REAL_DATA_POINTS 65536 +NM:LINE_BROADENING 0.5 +NM:ZERO_FILLING Yes +NM:APODIZATION Lorentzian +NM:BASELINE_CORRECTION_METHOD Polynomial +NM:CHEMICAL_SHIFT_REF_STD DSS +NM:INSTRUMENT_NAME Bruker Avance III +#NMR_BINNED_DATA +NMR_BINNED_DATA:UNITS Peak area +NMR_BINNED_DATA_START +Bin range(ppm) C0559 C0629 C0640 C0835 D0613 D0762 D1113 D1158 D2090 E0004 E0195 E0225 E0309 E0487 F0036 F0108 A0233 A0490 A2003 C0586 C2177 D0177 D0729 D0909 D0945 D1174 D2054 D2062 D2079 D2111 D2404 D2532 E0546 E0607 E2036 F0035 +0.4...0.46 0.000076 0 0.000344 0.000641 0.00663 0 0.00314 0 0 0.00172 0 0.00125 0.00411 0.00172 0.0166 0 0 0 0 0 0 0.00921 0 0 0 0 0.00155 0.0000239 0.0273 0 0 0 0.00145 0.00378 0 0.00427 +0.46...0.52 0 0.0178 0 0 0 0 0.00242 0.00371 0 0 0.00169 0 0 0 0 0.00231 0.0186 0 0 0.0169 0 0 0.00188 0.00108 0.000479 0.000933 0 0 0 0.0063 0.0108 0 0 0 0 0 +0.52...0.54 0.0732 0 0.00183 0.00644 0 0.0179 0.0802 0.0235 0.00245 0.0685 0.0557 0.0044 0.0223 0 0 0.0063 0 0.00299 0.0345 0 0.0302 0.0169 0.0558 0 0.007 0 0 0.0604 0.00181 0 0.00015 0.0172 0.00639 0.0103 0.0227 0.0218 +0.54...0.57 0.0848 0.0218 0.000186 0 0.0106 0.0198 0.104 0.0483 0.000998 0.0305 0.0629 0.0169 0.00754 0.01 0.0206 0.0111 0.0182 0.036 0.022 0.00664 0.0359 0.0147 0.0787 0.00569 0 0.00343 0.0122 0.0299 0.0117 0.00324 0.00141 0.0166 0.0147 0.0302 0 0 +0.57...0.6 0.032 0 0 0.00396 0 0 0.00532 0 0 0 0 0 0.00308 0 0 0 0 0 0 0 0 0 0.0292 0 0.00419 0.013 0 0 0 0 0 0 0 0 0 0.00275 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+9.7...9.74 0.00136 0.0023 0 0.00441 0.0073 0 0.00769 0.00201 0 0.00213 0 0.00511 0 0 0.00723 0.00726 0.000864 0.0105 0 0 0.0102 0.00231 0 0.00253 0.016 0.0047 0.259 0.00254 0.0443 0 0.0199 0.000539 0.00146 0.0191 0 0 +9.74...9.76 0.00237 0 0.00266 0.00325 0.00202 0.0291 0 0 0.000089 0 0.00157 0.0273 0.00637 0.000711 0.0343 0.0108 0.00302 0 0.00156 0.0493 0 0 0.0111 0 0 0 0 0 0.00493 0.00754 0 0 0.000636 0 0.003 0.00991 +9.76...9.81 0 0.00787 0 0.00048 0.00332 0 0 0 0.00702 0.00331 0 0 0 0 0 0 0 0.00327 0 0 0 0.0015 0.000969 0 0.000737 0.00486 0.0073 0 0 0.0197 0.00641 0 0 0.00238 0 0 +9.81...9.85 0 0 0.00923 0 0 0.000537 0.0000414 0 0 0 0 0.0142 0 0.00765 0 0 0 0.00308 0.00378 0.0276 0.00401 0.000268 0 0.0108 0 0 0 0.0185 0 0 0 0 0 0 0.00241 0.0306 +9.85...9.89 0 0 0.00417 0 0 0.0196 0.00268 0 0 0 0 0 0.00174 0 0.0151 0 0 0 0 0 0 0.00221 0 0 0.0023 0.00238 0 0 0.0817 0 0.00621 0 0 0.011 0.0136 0 +9.89...9.92 0.00156 0.00191 0 0.00344 0.0121 0 0.00142 0.0126 0.0066 0.00119 0.00206 0.00492 0 0.00159 0 0.00996 0.0201 0 0 0.0417 0 0 0 0 0.00447 0 0.00133 0.00568 0 0 0.00724 0.00214 0.0019 0 0 0.00326 +9.92...9.97 0 0.0154 0.00305 0 0 0 0 0 0 0.00224 0 0.00122 0.00421 0 0.0437 0.00288 0.00875 0.0078 0.0000109 0 0.00635 0 0.00486 0.0011 0 0.0192 0 0 0.0196 0.00243 0 0 0 0 0.000675 0.0297 +9.97...10 0.00048 0 0 0.000271 0.00875 0.0166 0.00753 0.0101 0.00357 0 0.000415 0 0 0.00333 0 0.00922 0.0166 0 0.00322 0 0 0 0 0.00245 0.0102 0 0.00438 0 0.0275 0 0.00939 0 0 0.00728 0.00445 0 +NMR_BINNED_DATA_END +#END + + + + + + diff --git a/tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_SSF_line.txt b/tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_SSF_line.txt new file mode 100644 index 0000000..94adc2c --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_SSF_line.txt @@ -0,0 +1,407 @@ +#METABOLOMICS WORKBENCH wpathmasiri_20140228_9328071_mwtab.txt DATATRACK_ID:28 TEXT OUTPUT STUDY_ID:ST000022 ANALYSIS_ID:AN000041 PROJECT_ID:PR000021 +VERSION 1 +CREATED_ON 10-02-2015 +#PROJECT +PR:PROJECT_TITLE Johnston County Osteoarthritis Project +PR:PROJECT_TYPE Longitudinal study +PR:PROJECT_SUMMARY The Johnston County Osteoarthritis Project (JoCo) is a 20+ year ongoing, +PR:PROJECT_SUMMARY population-based, prospective cohort study of knee and hip OA designed to be +PR:PROJECT_SUMMARY representative of the civilian, non-institutionalized, African American or +PR:PROJECT_SUMMARY Caucasian population aged 45 years and older, who were residents of one of 6 +PR:PROJECT_SUMMARY townships in Johnston County NC for at least 1 year, and who were physically and +PR:PROJECT_SUMMARY mentally capable of completing the studys protocol. All participants had an +PR:PROJECT_SUMMARY initial home interview, a clinic examination with radiographs, and a subsequent +PR:PROJECT_SUMMARY second home interview. Approximately 3,200 individuals were recruited into the +PR:PROJECT_SUMMARY baseline evaluation between 1991 and 1997 (T0); the first follow up visit (T1) +PR:PROJECT_SUMMARY occurred from 1999-2004, and the second follow up visit (T2) from 2006-2010. +PR:PROJECT_SUMMARY Cohort enrichment (T1*) occurred from 2003-4, allowing new participants to be +PR:PROJECT_SUMMARY enrolled; the first follow up for these individuals was at T2. At T0, the sample +PR:PROJECT_SUMMARY was approximately 38% men and one-third African American. Between T0 and T1, +PR:PROJECT_SUMMARY radiographic knee OA, defined as Kellgren-Lawrence grade 2-4, developed in 12% +PR:PROJECT_SUMMARY of knees without knee OA at T0. +PR:INSTITUTE University of North Carolina at Chapel Hill +PR:DEPARTMENT Thurston Arthritis Research Center, Division of Rheumatology, Allergy, and +PR:DEPARTMENT Immunology +PR:LABORATORY Jordan Laboratory +PR:LAST_NAME Jordan +PR:FIRST_NAME Joanne +PR:ADDRESS 3300 Thurston Bldg., CB# 7280, Chapel Hill, NC 27599-7280 +PR:EMAIL wpathmasiri@rti.org +PR:PHONE 1-866-827-2762 +PR:FUNDING_SOURCE Center for Disease Control (CDC) +#STUDY +ST:STUDY_TITLE Biomarker Discovery in Knee Osteoarthritis (II) +ST:STUDY_TYPE Biomarker Discovery in Knee Osteoarthritis +ST:STUDY_SUMMARY This metabolomics pilot and feasibility (P & F) study was conducted to provide +ST:STUDY_SUMMARY data to be used to gain a better understanding of metabolic alterations in +ST:STUDY_SUMMARY people with knee osteoarthritis (OA) and to discover novel biomarkers of the +ST:STUDY_SUMMARY disease. The goal of the metabolomics study was to determine if metabolic +ST:STUDY_SUMMARY differences, detected by a comprehensive metabolomics analysis, can be used to +ST:STUDY_SUMMARY distinguish people who will develop symptomatic knee OA from those who will not. +ST:STUDY_SUMMARY For this metabolomics study, individuals participating in T1 or T1* with 5-year +ST:STUDY_SUMMARY follow-up at T2 were selected. At T2 subjects were on average 68.1(9.12) years +ST:STUDY_SUMMARY old with an average BMI of 31.4(7.01) with 32% men and one-third African +ST:STUDY_SUMMARY American. All had weight-bearing posterior-anterior knee films obtained with the +ST:STUDY_SUMMARY Synaflexer positioning device at both time points and read paired for +ST:STUDY_SUMMARY Kellgren-Lawrence grade and minimum joint space. Urine samples (second morning +ST:STUDY_SUMMARY void) collected from 36 overweight or obese participants in the JoCo at T1 or +ST:STUDY_SUMMARY T1* were selected from two subgroups (a group that developed radiographic +ST:STUDY_SUMMARY osteoarthritis (n=16) and an age, race, sex, and BMI matched group that did not +ST:STUDY_SUMMARY develop osteoarthritis (n=20). Radiographic knee OA was defined as +ST:STUDY_SUMMARY Kellgren-Lawrence grade 2-4 at T2 in a person with Kellgren-Lawrence grade 0 or +ST:STUDY_SUMMARY 1 at T1 or T1*. +ST:INSTITUTE RTI International +ST:DEPARTMENT Systems and Translational Sciences (STS) +ST:LABORATORY NIH Eastern Regional Comprehensive Metabolomics Resource Core (RTI RCMRC) +ST:LAST_NAME Sumner +ST:FIRST_NAME Susan +ST:ADDRESS 3040, East Cornwallis Road, Research Triangle Park, NC 27709 +ST:EMAIL ssumner@rti.org +ST:PHONE 919-541-7479 +ST:NUM_GROUPS 2 +ST:TOTAL_SUBJECTS 36 +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +SU:AGE_OR_AGE_RANGE Average age 68.1(9.12) +SU:WEIGHT_OR_WEIGHT_RANGE Average BMI of 31.4(7.01) +SU:GENDER Male, Female +SU:SPECIES_GROUP Human +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS -C0559Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0629 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0640 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0835 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0613 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0762 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1113 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1158 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D2090 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0004 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0195 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0225 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0309 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0487 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0036 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0108 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - A0233 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A0490 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A2003 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C0586 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C2177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0729 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0909 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0945 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D1174 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2054 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2062 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2079 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2111 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2404 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2532 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0546 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0607 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E2036 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - F0035 Disease Status:Control +#COLLECTION +CO:COLLECTION_SUMMARY Urine samples (second morning void) collected from 36 overweight or obese +CO:COLLECTION_SUMMARY participants in the JoCo at T1 or T1* were selected from two subgroups (a group +CO:COLLECTION_SUMMARY that developed radiographic osteoarthritis (n=16) and an age, race, sex, and BMI +CO:COLLECTION_SUMMARY matched group that did not develop osteoarthritis (n=20). Radiographic knee OA +CO:COLLECTION_SUMMARY was defined as Kellgren-Lawrence grade 2-4 at T2 in a person with +CO:COLLECTION_SUMMARY Kellgren-Lawrence grade 0 or 1 at T1 or T1*. +CO:SAMPLE_TYPE Urine +CO:STORAGE_CONDITIONS -80C +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Chenomx Internal Standard solution (70 ul) and 230 ul D20 was added to each of +SP:SAMPLEPREP_SUMMARY the 36 urine sample (400 ul), vortexed for 30s, and centrifuged at 12000 rcf for +SP:SAMPLEPREP_SUMMARY 5min. Chenomx ISTD (Chenomx, Edmonton, Alberta, Canada) contains 5mM +SP:SAMPLEPREP_SUMMARY 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS, Chemical Shift Indicator), 100 +SP:SAMPLEPREP_SUMMARY mM Imidazole (pH indicator), and 0.2% NaN3 (to inhibit bacterial growth) in D2O. +SP:SAMPLEPREP_SUMMARY 600 l aliquot of the supernatant was transferred into 5mm NMR tubes +SP:SAMPLEPREP_SUMMARY (Bruker-Biospin, Germany). Phenotypic pooled urine samples were made by +SP:SAMPLEPREP_SUMMARY combining 150 l aliquots from each of the study samples belonging to the same +SP:SAMPLEPREP_SUMMARY phenotype (cases and control). In addition, a combined phenotypic pooled sample +SP:SAMPLEPREP_SUMMARY was prepared by using 1000 l aliquot from each of the phenotypic pooled sample. +SP:SAMPLEPREP_SUMMARY Pooled NMR samples were prepared as described above and used as quality check +SP:SAMPLEPREP_SUMMARY (QC) samples. +SP:SAMPLEPREP_PROTOCOL_FILENAME Johnston_County_Procedure_March_4_2014.docx +SP:PROCESSING_METHOD Dilution using a mixture of D2O and Chenomx ISTD +SP:PROCESSING_STORAGE_CONDITIONS On ice +SP:EXTRACTION_METHOD None +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY None +CH:CHROMATOGRAPHY_TYPE - +CH:INSTRUMENT_NAME - +CH:COLUMN_NAME - +#ANALYSIS +AN:LABORATORY_NAME RTI, DHMRI +AN:ANALYSIS_TYPE NMR +AN:ACQUISITION_DATE 41527 +AN:SOFTWARE_VERSION TopSpin 3.0 +AN:OPERATOR_NAME Wimal Pathmasiri, Kevin Knagge +AN:DETECTOR_TYPE NMR +AN:DATA_FORMAT Bruker +#NMR +NM:INSTRUMENT_TYPE Bruker Avance III +NM:NMR_EXPERIMENT_TYPE 1D 1H +NM:FIELD_FREQUENCY_LOCK Deuterium +NM:STANDARD_CONCENTRATION 0.5mm +NM:SPECTROMETER_FREQUENCY 950 MHz +NM:NMR_PROBE 5mm, Inverse ATMA +NM:NMR_SOLVENT D2O +NM:NMR_TUBE_SIZE 5mm, 7 inch +NM:SHIMMING_METHOD Topshim +NM:PULSE_SEQUENCE noesypr1d +NM:WATER_SUPPRESSION presat +NM:PULSE_WIDTH 13.07 us +NM:POWER_LEVEL 12.589w +NM:RECEIVER_GAIN 4 +NM:OFFSET_FREQUENCY 4468.31 +NM:CHEMICAL_SHIFT_REF_CPD DSS +NM:TEMPERATURE 298 K +NM:NUMBER_OF_SCANS 64 +NM:DUMMY_SCANS 4 +NM:ACQUISITION_TIME 1.7301503 s +NM:RELAXATION_DELAY 2 s +NM:SPECTRAL_WIDTH 18939.395 Hz, 19.93 ppm +NM:NUM_DATA_POINTS_ACQUIRED 65536 +NM:REAL_DATA_POINTS 65536 +NM:LINE_BROADENING 0.5 +NM:ZERO_FILLING Yes +NM:APODIZATION Lorentzian +NM:BASELINE_CORRECTION_METHOD Polynomial +NM:CHEMICAL_SHIFT_REF_STD DSS +NM:INSTRUMENT_NAME Bruker Avance III +#NMR_BINNED_DATA +NMR_BINNED_DATA:UNITS Peak area +NMR_BINNED_DATA_START +Bin range(ppm) C0559 C0629 C0640 C0835 D0613 D0762 D1113 D1158 D2090 E0004 E0195 E0225 E0309 E0487 F0036 F0108 A0233 A0490 A2003 C0586 C2177 D0177 D0729 D0909 D0945 D1174 D2054 D2062 D2079 D2111 D2404 D2532 E0546 E0607 E2036 F0035 +0.4...0.46 0.000076 0 0.000344 0.000641 0.00663 0 0.00314 0 0 0.00172 0 0.00125 0.00411 0.00172 0.0166 0 0 0 0 0 0 0.00921 0 0 0 0 0.00155 0.0000239 0.0273 0 0 0 0.00145 0.00378 0 0.00427 +0.46...0.52 0 0.0178 0 0 0 0 0.00242 0.00371 0 0 0.00169 0 0 0 0 0.00231 0.0186 0 0 0.0169 0 0 0.00188 0.00108 0.000479 0.000933 0 0 0 0.0063 0.0108 0 0 0 0 0 +0.52...0.54 0.0732 0 0.00183 0.00644 0 0.0179 0.0802 0.0235 0.00245 0.0685 0.0557 0.0044 0.0223 0 0 0.0063 0 0.00299 0.0345 0 0.0302 0.0169 0.0558 0 0.007 0 0 0.0604 0.00181 0 0.00015 0.0172 0.00639 0.0103 0.0227 0.0218 +0.54...0.57 0.0848 0.0218 0.000186 0 0.0106 0.0198 0.104 0.0483 0.000998 0.0305 0.0629 0.0169 0.00754 0.01 0.0206 0.0111 0.0182 0.036 0.022 0.00664 0.0359 0.0147 0.0787 0.00569 0 0.00343 0.0122 0.0299 0.0117 0.00324 0.00141 0.0166 0.0147 0.0302 0 0 +0.57...0.6 0.032 0 0 0.00396 0 0 0.00532 0 0 0 0 0 0.00308 0 0 0 0 0 0 0 0 0 0.0292 0 0.00419 0.013 0 0 0 0 0 0 0 0 0 0.00275 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0.0254 0.016 0.194 0.0239 0.022 0.0729 0.071 0.000182 0.144 0.027 0.0345 0.0878 0.142 0.102 0 0.0167 0.00664 0.0204 0.121 0.0228 0 3.39 0.0275 0 0.0463 0.00648 0.0214 0.0771 0.0353 0.0225 0.0141 +9.28...9.33 0 0.00429 0.00574 0 0.00844 0 0 0 0 0 0.00564 0 0.00267 0 0 0 0.0205 0 0 0 0 0 0 0.000105 0 0.0047 0.000424 0 0 0 0 0 0 0 0 0 +9.33...9.36 0.00678 0.0221 0.000631 0.00392 0.00672 0.001 0.00353 0.00347 0.00678 0.0138 0 0.0144 0 0.00351 0.00348 0 0.00889 0.0044 0.00242 0.00908 0.000109 0.00298 0.00298 0.00067 0 0 0.00397 0 0.0441 0.00111 0.00955 0 0.0144 0 0.00279 0.017 +9.36...9.41 0 0 0 0 0 0.000173 0.000326 0 0.0063 0 0.000993 0.00122 0 0 0.0165 0.0112 0.0087 0.00696 0 0 0 0 0.00591 0 0 0.003 0 0.00574 0.0223 0 0.00465 0.00387 0 0 0 0 +9.41...9.46 0 0.0141 0 0.00265 0 0 0 0 0 0.00243 0.000198 0 0 0.00369 0 0 0 0 0.00111 0.0855 0 0.000979 0 0 0.00845 0 0.0000509 0 0.0109 0.0154 0 0 0 0.0082 0.0102 0.00947 +9.46...9.52 0.00215 0 0 0.00083 0 0.0135 0 0.00541 0 0 0.000605 0 0.00886 0 0 0 0 0.00163 0 0 0 0 0.00114 0.000367 0 0 0 0.0122 0 0 0.0086 0 0 0.00485 0.000589 0 +9.52...9.55 0.00222 0.0277 0 0.00759 0 0 0 0 0 0.00277 0 0.00221 0 0 0.00146 0.0161 0.0395 0.00135 0 0.103 0.00278 0.00592 0.00196 0.00846 0 0.03 0.00544 0.00753 0.0906 0.0445 0.0024 0 0.00269 0.00224 0 0.019 +9.55...9.58 0.00137 0 0.00278 0 0.0153 0.000841 0 0 0.00254 0 0 0.00767 0 0 0.0444 0.00127 0 0 0.00419 0 0.00322 0 0 0 0.00765 0 0 0 0 0 0 0.00385 0 0.0112 0.0000583 0.00498 +9.58...9.61 0 0 0.0000985 0 0 0 0 0 0.00132 0 0.00145 0 0.00756 0.0104 0 0.00374 0 0.00408 0 0 0 0.00218 0 0 0.000181 0.00728 0 0.00188 0.0266 0.00949 0 0 0 0 0 0 +9.61...9.66 0 0.0049 0.00223 0.00246 0.00543 0.00422 0.005 0.00427 0 0.000493 0 0 0 0 0.0188 0 0.0199 0.00497 0.00197 0.0482 0.00636 0 0 0.00864 0 0 0.0965 0 0.023 0 0.00847 0.00334 0 0.0204 0.00329 0 +9.66...9.7 0.000695 0 0 0 0.00412 0 0 0 0.0192 0.105 0.00237 0 0 0.0299 0 0 0 0 0.000187 0 0 0 0.00638 0 0 0 0.373 0.074 0 0.00743 0 0.00318 0 0 0 0.0000535 +9.7...9.74 0.00136 0.0023 0 0.00441 0.0073 0 0.00769 0.00201 0 0.00213 0 0.00511 0 0 0.00723 0.00726 0.000864 0.0105 0 0 0.0102 0.00231 0 0.00253 0.016 0.0047 0.259 0.00254 0.0443 0 0.0199 0.000539 0.00146 0.0191 0 0 +9.74...9.76 0.00237 0 0.00266 0.00325 0.00202 0.0291 0 0 0.000089 0 0.00157 0.0273 0.00637 0.000711 0.0343 0.0108 0.00302 0 0.00156 0.0493 0 0 0.0111 0 0 0 0 0 0.00493 0.00754 0 0 0.000636 0 0.003 0.00991 +9.76...9.81 0 0.00787 0 0.00048 0.00332 0 0 0 0.00702 0.00331 0 0 0 0 0 0 0 0.00327 0 0 0 0.0015 0.000969 0 0.000737 0.00486 0.0073 0 0 0.0197 0.00641 0 0 0.00238 0 0 +9.81...9.85 0 0 0.00923 0 0 0.000537 0.0000414 0 0 0 0 0.0142 0 0.00765 0 0 0 0.00308 0.00378 0.0276 0.00401 0.000268 0 0.0108 0 0 0 0.0185 0 0 0 0 0 0 0.00241 0.0306 +9.85...9.89 0 0 0.00417 0 0 0.0196 0.00268 0 0 0 0 0 0.00174 0 0.0151 0 0 0 0 0 0 0.00221 0 0 0.0023 0.00238 0 0 0.0817 0 0.00621 0 0 0.011 0.0136 0 +9.89...9.92 0.00156 0.00191 0 0.00344 0.0121 0 0.00142 0.0126 0.0066 0.00119 0.00206 0.00492 0 0.00159 0 0.00996 0.0201 0 0 0.0417 0 0 0 0 0.00447 0 0.00133 0.00568 0 0 0.00724 0.00214 0.0019 0 0 0.00326 +9.92...9.97 0 0.0154 0.00305 0 0 0 0 0 0 0.00224 0 0.00122 0.00421 0 0.0437 0.00288 0.00875 0.0078 0.0000109 0 0.00635 0 0.00486 0.0011 0 0.0192 0 0 0.0196 0.00243 0 0 0 0 0.000675 0.0297 +9.97...10 0.00048 0 0 0.000271 0.00875 0.0166 0.00753 0.0101 0.00357 0 0.000415 0 0 0.00333 0 0.00922 0.0166 0 0.00322 0 0 0 0 0.00245 0.0102 0 0.00438 0 0.0275 0 0.00939 0 0 0.00728 0.00445 0 +NMR_BINNED_DATA_END +#END + + + + + + diff --git a/tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_additional_data.txt b/tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_additional_data.txt new file mode 100644 index 0000000..c269877 --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_additional_data.txt @@ -0,0 +1,407 @@ +#METABOLOMICS WORKBENCH wpathmasiri_20140228_9328071_mwtab.txt DATATRACK_ID:28 TEXT OUTPUT STUDY_ID:ST000022 ANALYSIS_ID:AN000041 PROJECT_ID:PR000021 +VERSION 1 +CREATED_ON 10-02-2015 +#PROJECT +PR:PROJECT_TITLE Johnston County Osteoarthritis Project +PR:PROJECT_TYPE Longitudinal study +PR:PROJECT_SUMMARY The Johnston County Osteoarthritis Project (JoCo) is a 20+ year ongoing, +PR:PROJECT_SUMMARY population-based, prospective cohort study of knee and hip OA designed to be +PR:PROJECT_SUMMARY representative of the civilian, non-institutionalized, African American or +PR:PROJECT_SUMMARY Caucasian population aged 45 years and older, who were residents of one of 6 +PR:PROJECT_SUMMARY townships in Johnston County NC for at least 1 year, and who were physically and +PR:PROJECT_SUMMARY mentally capable of completing the studys protocol. All participants had an +PR:PROJECT_SUMMARY initial home interview, a clinic examination with radiographs, and a subsequent +PR:PROJECT_SUMMARY second home interview. Approximately 3,200 individuals were recruited into the +PR:PROJECT_SUMMARY baseline evaluation between 1991 and 1997 (T0); the first follow up visit (T1) +PR:PROJECT_SUMMARY occurred from 1999-2004, and the second follow up visit (T2) from 2006-2010. +PR:PROJECT_SUMMARY Cohort enrichment (T1*) occurred from 2003-4, allowing new participants to be +PR:PROJECT_SUMMARY enrolled; the first follow up for these individuals was at T2. At T0, the sample +PR:PROJECT_SUMMARY was approximately 38% men and one-third African American. Between T0 and T1, +PR:PROJECT_SUMMARY radiographic knee OA, defined as Kellgren-Lawrence grade 2-4, developed in 12% +PR:PROJECT_SUMMARY of knees without knee OA at T0. +PR:INSTITUTE University of North Carolina at Chapel Hill +PR:DEPARTMENT Thurston Arthritis Research Center, Division of Rheumatology, Allergy, and +PR:DEPARTMENT Immunology +PR:LABORATORY Jordan Laboratory +PR:LAST_NAME Jordan +PR:FIRST_NAME Joanne +PR:ADDRESS 3300 Thurston Bldg., CB# 7280, Chapel Hill, NC 27599-7280 +PR:EMAIL wpathmasiri@rti.org +PR:PHONE 1-866-827-2762 +PR:FUNDING_SOURCE Center for Disease Control (CDC) +#STUDY +ST:STUDY_TITLE Biomarker Discovery in Knee Osteoarthritis (II) +ST:STUDY_TYPE Biomarker Discovery in Knee Osteoarthritis +ST:STUDY_SUMMARY This metabolomics pilot and feasibility (P & F) study was conducted to provide +ST:STUDY_SUMMARY data to be used to gain a better understanding of metabolic alterations in +ST:STUDY_SUMMARY people with knee osteoarthritis (OA) and to discover novel biomarkers of the +ST:STUDY_SUMMARY disease. The goal of the metabolomics study was to determine if metabolic +ST:STUDY_SUMMARY differences, detected by a comprehensive metabolomics analysis, can be used to +ST:STUDY_SUMMARY distinguish people who will develop symptomatic knee OA from those who will not. +ST:STUDY_SUMMARY For this metabolomics study, individuals participating in T1 or T1* with 5-year +ST:STUDY_SUMMARY follow-up at T2 were selected. At T2 subjects were on average 68.1(9.12) years +ST:STUDY_SUMMARY old with an average BMI of 31.4(7.01) with 32% men and one-third African +ST:STUDY_SUMMARY American. All had weight-bearing posterior-anterior knee films obtained with the +ST:STUDY_SUMMARY Synaflexer positioning device at both time points and read paired for +ST:STUDY_SUMMARY Kellgren-Lawrence grade and minimum joint space. Urine samples (second morning +ST:STUDY_SUMMARY void) collected from 36 overweight or obese participants in the JoCo at T1 or +ST:STUDY_SUMMARY T1* were selected from two subgroups (a group that developed radiographic +ST:STUDY_SUMMARY osteoarthritis (n=16) and an age, race, sex, and BMI matched group that did not +ST:STUDY_SUMMARY develop osteoarthritis (n=20). Radiographic knee OA was defined as +ST:STUDY_SUMMARY Kellgren-Lawrence grade 2-4 at T2 in a person with Kellgren-Lawrence grade 0 or +ST:STUDY_SUMMARY 1 at T1 or T1*. +ST:INSTITUTE RTI International +ST:DEPARTMENT Systems and Translational Sciences (STS) +ST:LABORATORY NIH Eastern Regional Comprehensive Metabolomics Resource Core (RTI RCMRC) +ST:LAST_NAME Sumner +ST:FIRST_NAME Susan +ST:ADDRESS 3040, East Cornwallis Road, Research Triangle Park, NC 27709 +ST:EMAIL ssumner@rti.org +ST:PHONE 919-541-7479 +ST:NUM_GROUPS 2 +ST:TOTAL_SUBJECTS 36 +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +SU:AGE_OR_AGE_RANGE Average age 68.1(9.12) +SU:WEIGHT_OR_WEIGHT_RANGE Average BMI of 31.4(7.01) +SU:GENDER Male, Female +SU:SPECIES_GROUP Human +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS - C0559 Disease Status:Cases key1==value1 +SUBJECT_SAMPLE_FACTORS - C0629 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0640 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0835 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0613 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0762 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1113 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1158 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D2090 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0004 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0195 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0225 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0309 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0487 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0036 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0108 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - A0233 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A0490 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A2003 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C0586 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C2177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0729 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0909 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0945 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D1174 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2054 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2062 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2079 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2111 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2404 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2532 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0546 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0607 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E2036 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - F0035 Disease Status:Control +#COLLECTION +CO:COLLECTION_SUMMARY Urine samples (second morning void) collected from 36 overweight or obese +CO:COLLECTION_SUMMARY participants in the JoCo at T1 or T1* were selected from two subgroups (a group +CO:COLLECTION_SUMMARY that developed radiographic osteoarthritis (n=16) and an age, race, sex, and BMI +CO:COLLECTION_SUMMARY matched group that did not develop osteoarthritis (n=20). Radiographic knee OA +CO:COLLECTION_SUMMARY was defined as Kellgren-Lawrence grade 2-4 at T2 in a person with +CO:COLLECTION_SUMMARY Kellgren-Lawrence grade 0 or 1 at T1 or T1*. +CO:SAMPLE_TYPE Urine +CO:STORAGE_CONDITIONS -80C +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Chenomx Internal Standard solution (70 ul) and 230 ul D20 was added to each of +SP:SAMPLEPREP_SUMMARY the 36 urine sample (400 ul), vortexed for 30s, and centrifuged at 12000 rcf for +SP:SAMPLEPREP_SUMMARY 5min. Chenomx ISTD (Chenomx, Edmonton, Alberta, Canada) contains 5mM +SP:SAMPLEPREP_SUMMARY 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS, Chemical Shift Indicator), 100 +SP:SAMPLEPREP_SUMMARY mM Imidazole (pH indicator), and 0.2% NaN3 (to inhibit bacterial growth) in D2O. +SP:SAMPLEPREP_SUMMARY 600 l aliquot of the supernatant was transferred into 5mm NMR tubes +SP:SAMPLEPREP_SUMMARY (Bruker-Biospin, Germany). Phenotypic pooled urine samples were made by +SP:SAMPLEPREP_SUMMARY combining 150 l aliquots from each of the study samples belonging to the same +SP:SAMPLEPREP_SUMMARY phenotype (cases and control). In addition, a combined phenotypic pooled sample +SP:SAMPLEPREP_SUMMARY was prepared by using 1000 l aliquot from each of the phenotypic pooled sample. +SP:SAMPLEPREP_SUMMARY Pooled NMR samples were prepared as described above and used as quality check +SP:SAMPLEPREP_SUMMARY (QC) samples. +SP:SAMPLEPREP_PROTOCOL_FILENAME Johnston_County_Procedure_March_4_2014.docx +SP:PROCESSING_METHOD Dilution using a mixture of D2O and Chenomx ISTD +SP:PROCESSING_STORAGE_CONDITIONS On ice +SP:EXTRACTION_METHOD None +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY None +CH:CHROMATOGRAPHY_TYPE - +CH:INSTRUMENT_NAME - +CH:COLUMN_NAME - +#ANALYSIS +AN:LABORATORY_NAME RTI, DHMRI +AN:ANALYSIS_TYPE NMR +AN:ACQUISITION_DATE 41527 +AN:SOFTWARE_VERSION TopSpin 3.0 +AN:OPERATOR_NAME Wimal Pathmasiri, Kevin Knagge +AN:DETECTOR_TYPE NMR +AN:DATA_FORMAT Bruker +#NMR +NM:INSTRUMENT_TYPE Bruker Avance III +NM:NMR_EXPERIMENT_TYPE 1D 1H +NM:FIELD_FREQUENCY_LOCK Deuterium +NM:STANDARD_CONCENTRATION 0.5mm +NM:SPECTROMETER_FREQUENCY 950 MHz +NM:NMR_PROBE 5mm, Inverse ATMA +NM:NMR_SOLVENT D2O +NM:NMR_TUBE_SIZE 5mm, 7 inch +NM:SHIMMING_METHOD Topshim +NM:PULSE_SEQUENCE noesypr1d +NM:WATER_SUPPRESSION presat +NM:PULSE_WIDTH 13.07 us +NM:POWER_LEVEL 12.589w +NM:RECEIVER_GAIN 4 +NM:OFFSET_FREQUENCY 4468.31 +NM:CHEMICAL_SHIFT_REF_CPD DSS +NM:TEMPERATURE 298 K +NM:NUMBER_OF_SCANS 64 +NM:DUMMY_SCANS 4 +NM:ACQUISITION_TIME 1.7301503 s +NM:RELAXATION_DELAY 2 s +NM:SPECTRAL_WIDTH 18939.395 Hz, 19.93 ppm +NM:NUM_DATA_POINTS_ACQUIRED 65536 +NM:REAL_DATA_POINTS 65536 +NM:LINE_BROADENING 0.5 +NM:ZERO_FILLING Yes +NM:APODIZATION Lorentzian +NM:BASELINE_CORRECTION_METHOD Polynomial +NM:CHEMICAL_SHIFT_REF_STD DSS +NM:INSTRUMENT_NAME Bruker Avance III +#NMR_BINNED_DATA +NMR_BINNED_DATA:UNITS Peak area +NMR_BINNED_DATA_START +Bin range(ppm) C0559 C0629 C0640 C0835 D0613 D0762 D1113 D1158 D2090 E0004 E0195 E0225 E0309 E0487 F0036 F0108 A0233 A0490 A2003 C0586 C2177 D0177 D0729 D0909 D0945 D1174 D2054 D2062 D2079 D2111 D2404 D2532 E0546 E0607 E2036 F0035 +0.4...0.46 0.000076 0 0.000344 0.000641 0.00663 0 0.00314 0 0 0.00172 0 0.00125 0.00411 0.00172 0.0166 0 0 0 0 0 0 0.00921 0 0 0 0 0.00155 0.0000239 0.0273 0 0 0 0.00145 0.00378 0 0.00427 +0.46...0.52 0 0.0178 0 0 0 0 0.00242 0.00371 0 0 0.00169 0 0 0 0 0.00231 0.0186 0 0 0.0169 0 0 0.00188 0.00108 0.000479 0.000933 0 0 0 0.0063 0.0108 0 0 0 0 0 +0.52...0.54 0.0732 0 0.00183 0.00644 0 0.0179 0.0802 0.0235 0.00245 0.0685 0.0557 0.0044 0.0223 0 0 0.0063 0 0.00299 0.0345 0 0.0302 0.0169 0.0558 0 0.007 0 0 0.0604 0.00181 0 0.00015 0.0172 0.00639 0.0103 0.0227 0.0218 +0.54...0.57 0.0848 0.0218 0.000186 0 0.0106 0.0198 0.104 0.0483 0.000998 0.0305 0.0629 0.0169 0.00754 0.01 0.0206 0.0111 0.0182 0.036 0.022 0.00664 0.0359 0.0147 0.0787 0.00569 0 0.00343 0.0122 0.0299 0.0117 0.00324 0.00141 0.0166 0.0147 0.0302 0 0 +0.57...0.6 0.032 0 0 0.00396 0 0 0.00532 0 0 0 0 0 0.00308 0 0 0 0 0 0 0 0 0 0.0292 0 0.00419 0.013 0 0 0 0 0 0 0 0 0 0.00275 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0.0254 0.016 0.194 0.0239 0.022 0.0729 0.071 0.000182 0.144 0.027 0.0345 0.0878 0.142 0.102 0 0.0167 0.00664 0.0204 0.121 0.0228 0 3.39 0.0275 0 0.0463 0.00648 0.0214 0.0771 0.0353 0.0225 0.0141 +9.28...9.33 0 0.00429 0.00574 0 0.00844 0 0 0 0 0 0.00564 0 0.00267 0 0 0 0.0205 0 0 0 0 0 0 0.000105 0 0.0047 0.000424 0 0 0 0 0 0 0 0 0 +9.33...9.36 0.00678 0.0221 0.000631 0.00392 0.00672 0.001 0.00353 0.00347 0.00678 0.0138 0 0.0144 0 0.00351 0.00348 0 0.00889 0.0044 0.00242 0.00908 0.000109 0.00298 0.00298 0.00067 0 0 0.00397 0 0.0441 0.00111 0.00955 0 0.0144 0 0.00279 0.017 +9.36...9.41 0 0 0 0 0 0.000173 0.000326 0 0.0063 0 0.000993 0.00122 0 0 0.0165 0.0112 0.0087 0.00696 0 0 0 0 0.00591 0 0 0.003 0 0.00574 0.0223 0 0.00465 0.00387 0 0 0 0 +9.41...9.46 0 0.0141 0 0.00265 0 0 0 0 0 0.00243 0.000198 0 0 0.00369 0 0 0 0 0.00111 0.0855 0 0.000979 0 0 0.00845 0 0.0000509 0 0.0109 0.0154 0 0 0 0.0082 0.0102 0.00947 +9.46...9.52 0.00215 0 0 0.00083 0 0.0135 0 0.00541 0 0 0.000605 0 0.00886 0 0 0 0 0.00163 0 0 0 0 0.00114 0.000367 0 0 0 0.0122 0 0 0.0086 0 0 0.00485 0.000589 0 +9.52...9.55 0.00222 0.0277 0 0.00759 0 0 0 0 0 0.00277 0 0.00221 0 0 0.00146 0.0161 0.0395 0.00135 0 0.103 0.00278 0.00592 0.00196 0.00846 0 0.03 0.00544 0.00753 0.0906 0.0445 0.0024 0 0.00269 0.00224 0 0.019 +9.55...9.58 0.00137 0 0.00278 0 0.0153 0.000841 0 0 0.00254 0 0 0.00767 0 0 0.0444 0.00127 0 0 0.00419 0 0.00322 0 0 0 0.00765 0 0 0 0 0 0 0.00385 0 0.0112 0.0000583 0.00498 +9.58...9.61 0 0 0.0000985 0 0 0 0 0 0.00132 0 0.00145 0 0.00756 0.0104 0 0.00374 0 0.00408 0 0 0 0.00218 0 0 0.000181 0.00728 0 0.00188 0.0266 0.00949 0 0 0 0 0 0 +9.61...9.66 0 0.0049 0.00223 0.00246 0.00543 0.00422 0.005 0.00427 0 0.000493 0 0 0 0 0.0188 0 0.0199 0.00497 0.00197 0.0482 0.00636 0 0 0.00864 0 0 0.0965 0 0.023 0 0.00847 0.00334 0 0.0204 0.00329 0 +9.66...9.7 0.000695 0 0 0 0.00412 0 0 0 0.0192 0.105 0.00237 0 0 0.0299 0 0 0 0 0.000187 0 0 0 0.00638 0 0 0 0.373 0.074 0 0.00743 0 0.00318 0 0 0 0.0000535 +9.7...9.74 0.00136 0.0023 0 0.00441 0.0073 0 0.00769 0.00201 0 0.00213 0 0.00511 0 0 0.00723 0.00726 0.000864 0.0105 0 0 0.0102 0.00231 0 0.00253 0.016 0.0047 0.259 0.00254 0.0443 0 0.0199 0.000539 0.00146 0.0191 0 0 +9.74...9.76 0.00237 0 0.00266 0.00325 0.00202 0.0291 0 0 0.000089 0 0.00157 0.0273 0.00637 0.000711 0.0343 0.0108 0.00302 0 0.00156 0.0493 0 0 0.0111 0 0 0 0 0 0.00493 0.00754 0 0 0.000636 0 0.003 0.00991 +9.76...9.81 0 0.00787 0 0.00048 0.00332 0 0 0 0.00702 0.00331 0 0 0 0 0 0 0 0.00327 0 0 0 0.0015 0.000969 0 0.000737 0.00486 0.0073 0 0 0.0197 0.00641 0 0 0.00238 0 0 +9.81...9.85 0 0 0.00923 0 0 0.000537 0.0000414 0 0 0 0 0.0142 0 0.00765 0 0 0 0.00308 0.00378 0.0276 0.00401 0.000268 0 0.0108 0 0 0 0.0185 0 0 0 0 0 0 0.00241 0.0306 +9.85...9.89 0 0 0.00417 0 0 0.0196 0.00268 0 0 0 0 0 0.00174 0 0.0151 0 0 0 0 0 0 0.00221 0 0 0.0023 0.00238 0 0 0.0817 0 0.00621 0 0 0.011 0.0136 0 +9.89...9.92 0.00156 0.00191 0 0.00344 0.0121 0 0.00142 0.0126 0.0066 0.00119 0.00206 0.00492 0 0.00159 0 0.00996 0.0201 0 0 0.0417 0 0 0 0 0.00447 0 0.00133 0.00568 0 0 0.00724 0.00214 0.0019 0 0 0.00326 +9.92...9.97 0 0.0154 0.00305 0 0 0 0 0 0 0.00224 0 0.00122 0.00421 0 0.0437 0.00288 0.00875 0.0078 0.0000109 0 0.00635 0 0.00486 0.0011 0 0.0192 0 0 0.0196 0.00243 0 0 0 0 0.000675 0.0297 +9.97...10 0.00048 0 0 0.000271 0.00875 0.0166 0.00753 0.0101 0.00357 0 0.000415 0 0 0.00333 0 0.00922 0.0166 0 0.00322 0 0 0 0 0.00245 0.0102 0 0.00438 0 0.0275 0 0.00939 0 0 0.00728 0.00445 0 +NMR_BINNED_DATA_END +#END + + + + + + diff --git a/tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_factor.txt b/tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_factor.txt new file mode 100644 index 0000000..15cb5d0 --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_factor.txt @@ -0,0 +1,407 @@ +#METABOLOMICS WORKBENCH wpathmasiri_20140228_9328071_mwtab.txt DATATRACK_ID:28 TEXT OUTPUT STUDY_ID:ST000022 ANALYSIS_ID:AN000041 PROJECT_ID:PR000021 +VERSION 1 +CREATED_ON 10-02-2015 +#PROJECT +PR:PROJECT_TITLE Johnston County Osteoarthritis Project +PR:PROJECT_TYPE Longitudinal study +PR:PROJECT_SUMMARY The Johnston County Osteoarthritis Project (JoCo) is a 20+ year ongoing, +PR:PROJECT_SUMMARY population-based, prospective cohort study of knee and hip OA designed to be +PR:PROJECT_SUMMARY representative of the civilian, non-institutionalized, African American or +PR:PROJECT_SUMMARY Caucasian population aged 45 years and older, who were residents of one of 6 +PR:PROJECT_SUMMARY townships in Johnston County NC for at least 1 year, and who were physically and +PR:PROJECT_SUMMARY mentally capable of completing the studys protocol. All participants had an +PR:PROJECT_SUMMARY initial home interview, a clinic examination with radiographs, and a subsequent +PR:PROJECT_SUMMARY second home interview. Approximately 3,200 individuals were recruited into the +PR:PROJECT_SUMMARY baseline evaluation between 1991 and 1997 (T0); the first follow up visit (T1) +PR:PROJECT_SUMMARY occurred from 1999-2004, and the second follow up visit (T2) from 2006-2010. +PR:PROJECT_SUMMARY Cohort enrichment (T1*) occurred from 2003-4, allowing new participants to be +PR:PROJECT_SUMMARY enrolled; the first follow up for these individuals was at T2. At T0, the sample +PR:PROJECT_SUMMARY was approximately 38% men and one-third African American. Between T0 and T1, +PR:PROJECT_SUMMARY radiographic knee OA, defined as Kellgren-Lawrence grade 2-4, developed in 12% +PR:PROJECT_SUMMARY of knees without knee OA at T0. +PR:INSTITUTE University of North Carolina at Chapel Hill +PR:DEPARTMENT Thurston Arthritis Research Center, Division of Rheumatology, Allergy, and +PR:DEPARTMENT Immunology +PR:LABORATORY Jordan Laboratory +PR:LAST_NAME Jordan +PR:FIRST_NAME Joanne +PR:ADDRESS 3300 Thurston Bldg., CB# 7280, Chapel Hill, NC 27599-7280 +PR:EMAIL wpathmasiri@rti.org +PR:PHONE 1-866-827-2762 +PR:FUNDING_SOURCE Center for Disease Control (CDC) +#STUDY +ST:STUDY_TITLE Biomarker Discovery in Knee Osteoarthritis (II) +ST:STUDY_TYPE Biomarker Discovery in Knee Osteoarthritis +ST:STUDY_SUMMARY This metabolomics pilot and feasibility (P & F) study was conducted to provide +ST:STUDY_SUMMARY data to be used to gain a better understanding of metabolic alterations in +ST:STUDY_SUMMARY people with knee osteoarthritis (OA) and to discover novel biomarkers of the +ST:STUDY_SUMMARY disease. The goal of the metabolomics study was to determine if metabolic +ST:STUDY_SUMMARY differences, detected by a comprehensive metabolomics analysis, can be used to +ST:STUDY_SUMMARY distinguish people who will develop symptomatic knee OA from those who will not. +ST:STUDY_SUMMARY For this metabolomics study, individuals participating in T1 or T1* with 5-year +ST:STUDY_SUMMARY follow-up at T2 were selected. At T2 subjects were on average 68.1(9.12) years +ST:STUDY_SUMMARY old with an average BMI of 31.4(7.01) with 32% men and one-third African +ST:STUDY_SUMMARY American. All had weight-bearing posterior-anterior knee films obtained with the +ST:STUDY_SUMMARY Synaflexer positioning device at both time points and read paired for +ST:STUDY_SUMMARY Kellgren-Lawrence grade and minimum joint space. Urine samples (second morning +ST:STUDY_SUMMARY void) collected from 36 overweight or obese participants in the JoCo at T1 or +ST:STUDY_SUMMARY T1* were selected from two subgroups (a group that developed radiographic +ST:STUDY_SUMMARY osteoarthritis (n=16) and an age, race, sex, and BMI matched group that did not +ST:STUDY_SUMMARY develop osteoarthritis (n=20). Radiographic knee OA was defined as +ST:STUDY_SUMMARY Kellgren-Lawrence grade 2-4 at T2 in a person with Kellgren-Lawrence grade 0 or +ST:STUDY_SUMMARY 1 at T1 or T1*. +ST:INSTITUTE RTI International +ST:DEPARTMENT Systems and Translational Sciences (STS) +ST:LABORATORY NIH Eastern Regional Comprehensive Metabolomics Resource Core (RTI RCMRC) +ST:LAST_NAME Sumner +ST:FIRST_NAME Susan +ST:ADDRESS 3040, East Cornwallis Road, Research Triangle Park, NC 27709 +ST:EMAIL ssumner@rti.org +ST:PHONE 919-541-7479 +ST:NUM_GROUPS 2 +ST:TOTAL_SUBJECTS 36 +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +SU:AGE_OR_AGE_RANGE Average age 68.1(9.12) +SU:WEIGHT_OR_WEIGHT_RANGE Average BMI of 31.4(7.01) +SU:GENDER Male, Female +SU:SPECIES_GROUP Human +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS - C0559 Disease Status::Cases +SUBJECT_SAMPLE_FACTORS - C0629 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0640 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0835 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0613 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0762 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1113 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1158 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D2090 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0004 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0195 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0225 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0309 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0487 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0036 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0108 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - A0233 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A0490 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A2003 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C0586 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C2177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0729 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0909 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0945 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D1174 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2054 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2062 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2079 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2111 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2404 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2532 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0546 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0607 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E2036 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - F0035 Disease Status:Control +#COLLECTION +CO:COLLECTION_SUMMARY Urine samples (second morning void) collected from 36 overweight or obese +CO:COLLECTION_SUMMARY participants in the JoCo at T1 or T1* were selected from two subgroups (a group +CO:COLLECTION_SUMMARY that developed radiographic osteoarthritis (n=16) and an age, race, sex, and BMI +CO:COLLECTION_SUMMARY matched group that did not develop osteoarthritis (n=20). Radiographic knee OA +CO:COLLECTION_SUMMARY was defined as Kellgren-Lawrence grade 2-4 at T2 in a person with +CO:COLLECTION_SUMMARY Kellgren-Lawrence grade 0 or 1 at T1 or T1*. +CO:SAMPLE_TYPE Urine +CO:STORAGE_CONDITIONS -80C +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Chenomx Internal Standard solution (70 ul) and 230 ul D20 was added to each of +SP:SAMPLEPREP_SUMMARY the 36 urine sample (400 ul), vortexed for 30s, and centrifuged at 12000 rcf for +SP:SAMPLEPREP_SUMMARY 5min. Chenomx ISTD (Chenomx, Edmonton, Alberta, Canada) contains 5mM +SP:SAMPLEPREP_SUMMARY 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS, Chemical Shift Indicator), 100 +SP:SAMPLEPREP_SUMMARY mM Imidazole (pH indicator), and 0.2% NaN3 (to inhibit bacterial growth) in D2O. +SP:SAMPLEPREP_SUMMARY 600 l aliquot of the supernatant was transferred into 5mm NMR tubes +SP:SAMPLEPREP_SUMMARY (Bruker-Biospin, Germany). Phenotypic pooled urine samples were made by +SP:SAMPLEPREP_SUMMARY combining 150 l aliquots from each of the study samples belonging to the same +SP:SAMPLEPREP_SUMMARY phenotype (cases and control). In addition, a combined phenotypic pooled sample +SP:SAMPLEPREP_SUMMARY was prepared by using 1000 l aliquot from each of the phenotypic pooled sample. +SP:SAMPLEPREP_SUMMARY Pooled NMR samples were prepared as described above and used as quality check +SP:SAMPLEPREP_SUMMARY (QC) samples. +SP:SAMPLEPREP_PROTOCOL_FILENAME Johnston_County_Procedure_March_4_2014.docx +SP:PROCESSING_METHOD Dilution using a mixture of D2O and Chenomx ISTD +SP:PROCESSING_STORAGE_CONDITIONS On ice +SP:EXTRACTION_METHOD None +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY None +CH:CHROMATOGRAPHY_TYPE - +CH:INSTRUMENT_NAME - +CH:COLUMN_NAME - +#ANALYSIS +AN:LABORATORY_NAME RTI, DHMRI +AN:ANALYSIS_TYPE NMR +AN:ACQUISITION_DATE 41527 +AN:SOFTWARE_VERSION TopSpin 3.0 +AN:OPERATOR_NAME Wimal Pathmasiri, Kevin Knagge +AN:DETECTOR_TYPE NMR +AN:DATA_FORMAT Bruker +#NMR +NM:INSTRUMENT_TYPE Bruker Avance III +NM:NMR_EXPERIMENT_TYPE 1D 1H +NM:FIELD_FREQUENCY_LOCK Deuterium +NM:STANDARD_CONCENTRATION 0.5mm +NM:SPECTROMETER_FREQUENCY 950 MHz +NM:NMR_PROBE 5mm, Inverse ATMA +NM:NMR_SOLVENT D2O +NM:NMR_TUBE_SIZE 5mm, 7 inch +NM:SHIMMING_METHOD Topshim +NM:PULSE_SEQUENCE noesypr1d +NM:WATER_SUPPRESSION presat +NM:PULSE_WIDTH 13.07 us +NM:POWER_LEVEL 12.589w +NM:RECEIVER_GAIN 4 +NM:OFFSET_FREQUENCY 4468.31 +NM:CHEMICAL_SHIFT_REF_CPD DSS +NM:TEMPERATURE 298 K +NM:NUMBER_OF_SCANS 64 +NM:DUMMY_SCANS 4 +NM:ACQUISITION_TIME 1.7301503 s +NM:RELAXATION_DELAY 2 s +NM:SPECTRAL_WIDTH 18939.395 Hz, 19.93 ppm +NM:NUM_DATA_POINTS_ACQUIRED 65536 +NM:REAL_DATA_POINTS 65536 +NM:LINE_BROADENING 0.5 +NM:ZERO_FILLING Yes +NM:APODIZATION Lorentzian +NM:BASELINE_CORRECTION_METHOD Polynomial +NM:CHEMICAL_SHIFT_REF_STD DSS +NM:INSTRUMENT_NAME Bruker Avance III +#NMR_BINNED_DATA +NMR_BINNED_DATA:UNITS Peak area +NMR_BINNED_DATA_START +Bin range(ppm) C0559 C0629 C0640 C0835 D0613 D0762 D1113 D1158 D2090 E0004 E0195 E0225 E0309 E0487 F0036 F0108 A0233 A0490 A2003 C0586 C2177 D0177 D0729 D0909 D0945 D1174 D2054 D2062 D2079 D2111 D2404 D2532 E0546 E0607 E2036 F0035 +0.4...0.46 0.000076 0 0.000344 0.000641 0.00663 0 0.00314 0 0 0.00172 0 0.00125 0.00411 0.00172 0.0166 0 0 0 0 0 0 0.00921 0 0 0 0 0.00155 0.0000239 0.0273 0 0 0 0.00145 0.00378 0 0.00427 +0.46...0.52 0 0.0178 0 0 0 0 0.00242 0.00371 0 0 0.00169 0 0 0 0 0.00231 0.0186 0 0 0.0169 0 0 0.00188 0.00108 0.000479 0.000933 0 0 0 0.0063 0.0108 0 0 0 0 0 +0.52...0.54 0.0732 0 0.00183 0.00644 0 0.0179 0.0802 0.0235 0.00245 0.0685 0.0557 0.0044 0.0223 0 0 0.0063 0 0.00299 0.0345 0 0.0302 0.0169 0.0558 0 0.007 0 0 0.0604 0.00181 0 0.00015 0.0172 0.00639 0.0103 0.0227 0.0218 +0.54...0.57 0.0848 0.0218 0.000186 0 0.0106 0.0198 0.104 0.0483 0.000998 0.0305 0.0629 0.0169 0.00754 0.01 0.0206 0.0111 0.0182 0.036 0.022 0.00664 0.0359 0.0147 0.0787 0.00569 0 0.00343 0.0122 0.0299 0.0117 0.00324 0.00141 0.0166 0.0147 0.0302 0 0 +0.57...0.6 0.032 0 0 0.00396 0 0 0.00532 0 0 0 0 0 0.00308 0 0 0 0 0 0 0 0 0 0.0292 0 0.00419 0.013 0 0 0 0 0 0 0 0 0 0.00275 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+9.7...9.74 0.00136 0.0023 0 0.00441 0.0073 0 0.00769 0.00201 0 0.00213 0 0.00511 0 0 0.00723 0.00726 0.000864 0.0105 0 0 0.0102 0.00231 0 0.00253 0.016 0.0047 0.259 0.00254 0.0443 0 0.0199 0.000539 0.00146 0.0191 0 0 +9.74...9.76 0.00237 0 0.00266 0.00325 0.00202 0.0291 0 0 0.000089 0 0.00157 0.0273 0.00637 0.000711 0.0343 0.0108 0.00302 0 0.00156 0.0493 0 0 0.0111 0 0 0 0 0 0.00493 0.00754 0 0 0.000636 0 0.003 0.00991 +9.76...9.81 0 0.00787 0 0.00048 0.00332 0 0 0 0.00702 0.00331 0 0 0 0 0 0 0 0.00327 0 0 0 0.0015 0.000969 0 0.000737 0.00486 0.0073 0 0 0.0197 0.00641 0 0 0.00238 0 0 +9.81...9.85 0 0 0.00923 0 0 0.000537 0.0000414 0 0 0 0 0.0142 0 0.00765 0 0 0 0.00308 0.00378 0.0276 0.00401 0.000268 0 0.0108 0 0 0 0.0185 0 0 0 0 0 0 0.00241 0.0306 +9.85...9.89 0 0 0.00417 0 0 0.0196 0.00268 0 0 0 0 0 0.00174 0 0.0151 0 0 0 0 0 0 0.00221 0 0 0.0023 0.00238 0 0 0.0817 0 0.00621 0 0 0.011 0.0136 0 +9.89...9.92 0.00156 0.00191 0 0.00344 0.0121 0 0.00142 0.0126 0.0066 0.00119 0.00206 0.00492 0 0.00159 0 0.00996 0.0201 0 0 0.0417 0 0 0 0 0.00447 0 0.00133 0.00568 0 0 0.00724 0.00214 0.0019 0 0 0.00326 +9.92...9.97 0 0.0154 0.00305 0 0 0 0 0 0 0.00224 0 0.00122 0.00421 0 0.0437 0.00288 0.00875 0.0078 0.0000109 0 0.00635 0 0.00486 0.0011 0 0.0192 0 0 0.0196 0.00243 0 0 0 0 0.000675 0.0297 +9.97...10 0.00048 0 0 0.000271 0.00875 0.0166 0.00753 0.0101 0.00357 0 0.000415 0 0 0.00333 0 0.00922 0.0166 0 0.00322 0 0 0 0 0.00245 0.0102 0 0.00438 0 0.0275 0 0.00939 0 0 0.00728 0.00445 0 +NMR_BINNED_DATA_END +#END + + + + + + diff --git a/tests/example_data/other_mwtab_files/ST000022_AN000041_missing_tab.txt b/tests/example_data/other_mwtab_files/ST000022_AN000041_missing_tab.txt new file mode 100644 index 0000000..7a31fba --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000022_AN000041_missing_tab.txt @@ -0,0 +1,407 @@ +#METABOLOMICS WORKBENCH wpathmasiri_20140228_9328071_mwtab.txt DATATRACK_ID:28 TEXT OUTPUT STUDY_ID:ST000022 ANALYSIS_ID:AN000041 PROJECT_ID:PR000021 +VERSION 1 +CREATED_ON 10-02-2015 +#PROJECT +PR:PROJECT_TITLE Johnston County Osteoarthritis Project +PR:PROJECT_TYPE Longitudinal study +PR:PROJECT_SUMMARY The Johnston County Osteoarthritis Project (JoCo) is a 20+ year ongoing, +PR:PROJECT_SUMMARY population-based, prospective cohort study of knee and hip OA designed to be +PR:PROJECT_SUMMARY representative of the civilian, non-institutionalized, African American or +PR:PROJECT_SUMMARY Caucasian population aged 45 years and older, who were residents of one of 6 +PR:PROJECT_SUMMARY townships in Johnston County NC for at least 1 year, and who were physically and +PR:PROJECT_SUMMARY mentally capable of completing the studys protocol. All participants had an +PR:PROJECT_SUMMARY initial home interview, a clinic examination with radiographs, and a subsequent +PR:PROJECT_SUMMARY second home interview. Approximately 3,200 individuals were recruited into the +PR:PROJECT_SUMMARY baseline evaluation between 1991 and 1997 (T0); the first follow up visit (T1) +PR:PROJECT_SUMMARY occurred from 1999-2004, and the second follow up visit (T2) from 2006-2010. +PR:PROJECT_SUMMARY Cohort enrichment (T1*) occurred from 2003-4, allowing new participants to be +PR:PROJECT_SUMMARY enrolled; the first follow up for these individuals was at T2. At T0, the sample +PR:PROJECT_SUMMARY was approximately 38% men and one-third African American. Between T0 and T1, +PR:PROJECT_SUMMARY radiographic knee OA, defined as Kellgren-Lawrence grade 2-4, developed in 12% +PR:PROJECT_SUMMARY of knees without knee OA at T0. +PR:INSTITUTE University of North Carolina at Chapel Hill +PR:DEPARTMENT Thurston Arthritis Research Center, Division of Rheumatology, Allergy, and +PR:DEPARTMENT Immunology +PR:LABORATORY Jordan Laboratory +PR:LAST_NAME Jordan +PR:FIRST_NAME Joanne +PR:ADDRESS 3300 Thurston Bldg., CB# 7280, Chapel Hill, NC 27599-7280 +PR:EMAIL wpathmasiri@rti.org +PR:PHONE 1-866-827-2762 +PR:FUNDING_SOURCE Center for Disease Control (CDC) +#STUDY +ST:STUDY_TITLE Biomarker Discovery in Knee Osteoarthritis (II) +ST:STUDY_TYPE Biomarker Discovery in Knee Osteoarthritis +ST:STUDY_SUMMARY This metabolomics pilot and feasibility (P & F) study was conducted to provide +ST:STUDY_SUMMARY data to be used to gain a better understanding of metabolic alterations in +ST:STUDY_SUMMARY people with knee osteoarthritis (OA) and to discover novel biomarkers of the +ST:STUDY_SUMMARY disease. The goal of the metabolomics study was to determine if metabolic +ST:STUDY_SUMMARY differences, detected by a comprehensive metabolomics analysis, can be used to +ST:STUDY_SUMMARY distinguish people who will develop symptomatic knee OA from those who will not. +ST:STUDY_SUMMARY For this metabolomics study, individuals participating in T1 or T1* with 5-year +ST:STUDY_SUMMARY follow-up at T2 were selected. At T2 subjects were on average 68.1(9.12) years +ST:STUDY_SUMMARY old with an average BMI of 31.4(7.01) with 32% men and one-third African +ST:STUDY_SUMMARY American. All had weight-bearing posterior-anterior knee films obtained with the +ST:STUDY_SUMMARY Synaflexer positioning device at both time points and read paired for +ST:STUDY_SUMMARY Kellgren-Lawrence grade and minimum joint space. Urine samples (second morning +ST:STUDY_SUMMARY void) collected from 36 overweight or obese participants in the JoCo at T1 or +ST:STUDY_SUMMARY T1* were selected from two subgroups (a group that developed radiographic +ST:STUDY_SUMMARY osteoarthritis (n=16) and an age, race, sex, and BMI matched group that did not +ST:STUDY_SUMMARY develop osteoarthritis (n=20). Radiographic knee OA was defined as +ST:STUDY_SUMMARY Kellgren-Lawrence grade 2-4 at T2 in a person with Kellgren-Lawrence grade 0 or +ST:STUDY_SUMMARY 1 at T1 or T1*. +ST:INSTITUTE RTI International +ST:DEPARTMENT Systems and Translational Sciences (STS) +ST:LABORATORY NIH Eastern Regional Comprehensive Metabolomics Resource Core (RTI RCMRC) +ST:LAST_NAME Sumner +ST:FIRST_NAME Susan +ST:ADDRESS 3040, East Cornwallis Road, Research Triangle Park, NC 27709 +ST:EMAIL ssumner@rti.org +ST:PHONE 919-541-7479 +ST:NUM_GROUPS 2 +ST:TOTAL_SUBJECTS 36 +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +SU:AGE_OR_AGE_RANGE Average age 68.1(9.12) +SU:WEIGHT_OR_WEIGHT_RANGE Average BMI of 31.4(7.01) +SU:GENDER Male, Female +SU:SPECIES_GROUP Human +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS - C0559 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0629 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0640 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0835 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0613 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0762 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1113 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1158 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D2090 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0004 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0195 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0225 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0309 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0487 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0036 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0108 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - A0233 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A0490 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A2003 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C0586 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C2177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0729 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0909 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0945 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D1174 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2054 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2062 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2079 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2111 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2404 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2532 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0546 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0607 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E2036 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - F0035 Disease Status:Control +#COLLECTION +CO:COLLECTION_SUMMARY Urine samples (second morning void) collected from 36 overweight or obese +CO:COLLECTION_SUMMARY participants in the JoCo at T1 or T1* were selected from two subgroups (a group +CO:COLLECTION_SUMMARY that developed radiographic osteoarthritis (n=16) and an age, race, sex, and BMI +CO:COLLECTION_SUMMARY matched group that did not develop osteoarthritis (n=20). Radiographic knee OA +CO:COLLECTION_SUMMARY was defined as Kellgren-Lawrence grade 2-4 at T2 in a person with +CO:COLLECTION_SUMMARY Kellgren-Lawrence grade 0 or 1 at T1 or T1*. +CO:SAMPLE_TYPE Urine +CO:STORAGE_CONDITIONS -80C +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Chenomx Internal Standard solution (70 ul) and 230 ul D20 was added to each of +SP:SAMPLEPREP_SUMMARY the 36 urine sample (400 ul), vortexed for 30s, and centrifuged at 12000 rcf for +SP:SAMPLEPREP_SUMMARY 5min. Chenomx ISTD (Chenomx, Edmonton, Alberta, Canada) contains 5mM +SP:SAMPLEPREP_SUMMARY 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS, Chemical Shift Indicator), 100 +SP:SAMPLEPREP_SUMMARY mM Imidazole (pH indicator), and 0.2% NaN3 (to inhibit bacterial growth) in D2O. +SP:SAMPLEPREP_SUMMARY 600 l aliquot of the supernatant was transferred into 5mm NMR tubes +SP:SAMPLEPREP_SUMMARY (Bruker-Biospin, Germany). Phenotypic pooled urine samples were made by +SP:SAMPLEPREP_SUMMARY combining 150 l aliquots from each of the study samples belonging to the same +SP:SAMPLEPREP_SUMMARY phenotype (cases and control). In addition, a combined phenotypic pooled sample +SP:SAMPLEPREP_SUMMARY was prepared by using 1000 l aliquot from each of the phenotypic pooled sample. +SP:SAMPLEPREP_SUMMARY Pooled NMR samples were prepared as described above and used as quality check +SP:SAMPLEPREP_SUMMARY (QC) samples. +SP:SAMPLEPREP_PROTOCOL_FILENAME Johnston_County_Procedure_March_4_2014.docx +SP:PROCESSING_METHOD Dilution using a mixture of D2O and Chenomx ISTD +SP:PROCESSING_STORAGE_CONDITIONS On ice +SP:EXTRACTION_METHOD None +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY None +CH:CHROMATOGRAPHY_TYPE - +CH:INSTRUMENT_NAME - +CH:COLUMN_NAME - +#ANALYSIS +AN:LABORATORY_NAME RTI, DHMRI +AN:ANALYSIS_TYPE NMR +AN:ACQUISITION_DATE 41527 +AN:SOFTWARE_VERSION TopSpin 3.0 +AN:OPERATOR_NAME Wimal Pathmasiri, Kevin Knagge +AN:DETECTOR_TYPE NMR +AN:DATA_FORMAT Bruker +#NMR +NM:INSTRUMENT_TYPE Bruker Avance III +NM:NMR_EXPERIMENT_TYPE 1D 1H +NM:FIELD_FREQUENCY_LOCK Deuterium +NM:STANDARD_CONCENTRATION 0.5mm +NM:SPECTROMETER_FREQUENCY 950 MHz +NM:NMR_PROBE 5mm, Inverse ATMA +NM:NMR_SOLVENT D2O +NM:NMR_TUBE_SIZE 5mm, 7 inch +NM:SHIMMING_METHOD Topshim +NM:PULSE_SEQUENCE noesypr1d +NM:WATER_SUPPRESSION presat +NM:PULSE_WIDTH 13.07 us +NM:POWER_LEVEL 12.589w +NM:RECEIVER_GAIN 4 +NM:OFFSET_FREQUENCY 4468.31 +NM:CHEMICAL_SHIFT_REF_CPD DSS +NM:TEMPERATURE 298 K +NM:NUMBER_OF_SCANS 64 +NM:DUMMY_SCANS 4 +NM:ACQUISITION_TIME 1.7301503 s +NM:RELAXATION_DELAY 2 s +NM:SPECTRAL_WIDTH 18939.395 Hz, 19.93 ppm +NM:NUM_DATA_POINTS_ACQUIRED 65536 +NM:REAL_DATA_POINTS 65536 +NM:LINE_BROADENING 0.5 +NM:ZERO_FILLING Yes +NM:APODIZATION Lorentzian +NM:BASELINE_CORRECTION_METHOD Polynomial +NM:CHEMICAL_SHIFT_REF_STD DSS +NM:INSTRUMENT_NAME Bruker Avance III +#NMR_BINNED_DATA +NMR_BINNED_DATA:UNITS Peak area +NMR_BINNED_DATA_START +Bin range(ppm) C0559 C0629 C0640 C0835 D0613 D0762 D1113 D1158 D2090 E0004 E0195 E0225 E0309 E0487 F0036 F0108 A0233 A0490 A2003 C0586 C2177 D0177 D0729 D0909 D0945 D1174 D2054 D2062 D2079 D2111 D2404 D2532 E0546 E0607 E2036 F0035 +0.4...0.46 0.000076 0 0.000344 0.000641 0.00663 0 0.00314 0 0 0.00172 0 0.00125 0.00411 0.00172 0.0166 0 0 0 0 0 0 0.00921 0 0 0 0 0.00155 0.0000239 0.0273 0 0 0 0.00145 0.00378 0 0.00427 +0.46...0.52 0 0.0178 0 0 0 0 0.00242 0.00371 0 0 0.00169 0 0 0 0 0.00231 0.0186 0 0 0.0169 0 0 0.00188 0.00108 0.000479 0.000933 0 0 0 0.0063 0.0108 0 0 0 0 0 +0.52...0.54 0.0732 0 0.00183 0.00644 0 0.0179 0.0802 0.0235 0.00245 0.0685 0.0557 0.0044 0.0223 0 0 0.0063 0 0.00299 0.0345 0 0.0302 0.0169 0.0558 0 0.007 0 0 0.0604 0.00181 0 0.00015 0.0172 0.00639 0.0103 0.0227 0.0218 +0.54...0.57 0.0848 0.0218 0.000186 0 0.0106 0.0198 0.104 0.0483 0.000998 0.0305 0.0629 0.0169 0.00754 0.01 0.0206 0.0111 0.0182 0.036 0.022 0.00664 0.0359 0.0147 0.0787 0.00569 0 0.00343 0.0122 0.0299 0.0117 0.00324 0.00141 0.0166 0.0147 0.0302 0 0 +0.57...0.6 0.032 0 0 0.00396 0 0 0.00532 0 0 0 0 0 0.00308 0 0 0 0 0 0 0 0 0 0.0292 0 0.00419 0.013 0 0 0 0 0 0 0 0 0 0.00275 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0.0254 0.016 0.194 0.0239 0.022 0.0729 0.071 0.000182 0.144 0.027 0.0345 0.0878 0.142 0.102 0 0.0167 0.00664 0.0204 0.121 0.0228 0 3.39 0.0275 0 0.0463 0.00648 0.0214 0.0771 0.0353 0.0225 0.0141 +9.28...9.33 0 0.00429 0.00574 0 0.00844 0 0 0 0 0 0.00564 0 0.00267 0 0 0 0.0205 0 0 0 0 0 0 0.000105 0 0.0047 0.000424 0 0 0 0 0 0 0 0 0 +9.33...9.36 0.00678 0.0221 0.000631 0.00392 0.00672 0.001 0.00353 0.00347 0.00678 0.0138 0 0.0144 0 0.00351 0.00348 0 0.00889 0.0044 0.00242 0.00908 0.000109 0.00298 0.00298 0.00067 0 0 0.00397 0 0.0441 0.00111 0.00955 0 0.0144 0 0.00279 0.017 +9.36...9.41 0 0 0 0 0 0.000173 0.000326 0 0.0063 0 0.000993 0.00122 0 0 0.0165 0.0112 0.0087 0.00696 0 0 0 0 0.00591 0 0 0.003 0 0.00574 0.0223 0 0.00465 0.00387 0 0 0 0 +9.41...9.46 0 0.0141 0 0.00265 0 0 0 0 0 0.00243 0.000198 0 0 0.00369 0 0 0 0 0.00111 0.0855 0 0.000979 0 0 0.00845 0 0.0000509 0 0.0109 0.0154 0 0 0 0.0082 0.0102 0.00947 +9.46...9.52 0.00215 0 0 0.00083 0 0.0135 0 0.00541 0 0 0.000605 0 0.00886 0 0 0 0 0.00163 0 0 0 0 0.00114 0.000367 0 0 0 0.0122 0 0 0.0086 0 0 0.00485 0.000589 0 +9.52...9.55 0.00222 0.0277 0 0.00759 0 0 0 0 0 0.00277 0 0.00221 0 0 0.00146 0.0161 0.0395 0.00135 0 0.103 0.00278 0.00592 0.00196 0.00846 0 0.03 0.00544 0.00753 0.0906 0.0445 0.0024 0 0.00269 0.00224 0 0.019 +9.55...9.58 0.00137 0 0.00278 0 0.0153 0.000841 0 0 0.00254 0 0 0.00767 0 0 0.0444 0.00127 0 0 0.00419 0 0.00322 0 0 0 0.00765 0 0 0 0 0 0 0.00385 0 0.0112 0.0000583 0.00498 +9.58...9.61 0 0 0.0000985 0 0 0 0 0 0.00132 0 0.00145 0 0.00756 0.0104 0 0.00374 0 0.00408 0 0 0 0.00218 0 0 0.000181 0.00728 0 0.00188 0.0266 0.00949 0 0 0 0 0 0 +9.61...9.66 0 0.0049 0.00223 0.00246 0.00543 0.00422 0.005 0.00427 0 0.000493 0 0 0 0 0.0188 0 0.0199 0.00497 0.00197 0.0482 0.00636 0 0 0.00864 0 0 0.0965 0 0.023 0 0.00847 0.00334 0 0.0204 0.00329 0 +9.66...9.7 0.000695 0 0 0 0.00412 0 0 0 0.0192 0.105 0.00237 0 0 0.0299 0 0 0 0 0.000187 0 0 0 0.00638 0 0 0 0.373 0.074 0 0.00743 0 0.00318 0 0 0 0.0000535 +9.7...9.74 0.00136 0.0023 0 0.00441 0.0073 0 0.00769 0.00201 0 0.00213 0 0.00511 0 0 0.00723 0.00726 0.000864 0.0105 0 0 0.0102 0.00231 0 0.00253 0.016 0.0047 0.259 0.00254 0.0443 0 0.0199 0.000539 0.00146 0.0191 0 0 +9.74...9.76 0.00237 0 0.00266 0.00325 0.00202 0.0291 0 0 0.000089 0 0.00157 0.0273 0.00637 0.000711 0.0343 0.0108 0.00302 0 0.00156 0.0493 0 0 0.0111 0 0 0 0 0 0.00493 0.00754 0 0 0.000636 0 0.003 0.00991 +9.76...9.81 0 0.00787 0 0.00048 0.00332 0 0 0 0.00702 0.00331 0 0 0 0 0 0 0 0.00327 0 0 0 0.0015 0.000969 0 0.000737 0.00486 0.0073 0 0 0.0197 0.00641 0 0 0.00238 0 0 +9.81...9.85 0 0 0.00923 0 0 0.000537 0.0000414 0 0 0 0 0.0142 0 0.00765 0 0 0 0.00308 0.00378 0.0276 0.00401 0.000268 0 0.0108 0 0 0 0.0185 0 0 0 0 0 0 0.00241 0.0306 +9.85...9.89 0 0 0.00417 0 0 0.0196 0.00268 0 0 0 0 0 0.00174 0 0.0151 0 0 0 0 0 0 0.00221 0 0 0.0023 0.00238 0 0 0.0817 0 0.00621 0 0 0.011 0.0136 0 +9.89...9.92 0.00156 0.00191 0 0.00344 0.0121 0 0.00142 0.0126 0.0066 0.00119 0.00206 0.00492 0 0.00159 0 0.00996 0.0201 0 0 0.0417 0 0 0 0 0.00447 0 0.00133 0.00568 0 0 0.00724 0.00214 0.0019 0 0 0.00326 +9.92...9.97 0 0.0154 0.00305 0 0 0 0 0 0 0.00224 0 0.00122 0.00421 0 0.0437 0.00288 0.00875 0.0078 0.0000109 0 0.00635 0 0.00486 0.0011 0 0.0192 0 0 0.0196 0.00243 0 0 0 0 0.000675 0.0297 +9.97...10 0.00048 0 0 0.000271 0.00875 0.0166 0.00753 0.0101 0.00357 0 0.000415 0 0 0.00333 0 0.00922 0.0166 0 0.00322 0 0 0 0 0.00245 0.0102 0 0.00438 0 0.0275 0 0.00939 0 0 0.00728 0.00445 0 +NMR_BINNED_DATA_END +#END + + + + + + diff --git a/tests/example_data/other_mwtab_files/ST000022_AN000041_results_file.txt b/tests/example_data/other_mwtab_files/ST000022_AN000041_results_file.txt new file mode 100644 index 0000000..99547d8 --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000022_AN000041_results_file.txt @@ -0,0 +1,408 @@ +#METABOLOMICS WORKBENCH wpathmasiri_20140228_9328071_mwtab.txt DATATRACK_ID:28 TEXT OUTPUT STUDY_ID:ST000022 ANALYSIS_ID:AN000041 PROJECT_ID:PR000021 +VERSION 1 +CREATED_ON 10-02-2015 +#PROJECT +PR:PROJECT_TITLE Johnston County Osteoarthritis Project +PR:PROJECT_TYPE Longitudinal study +PR:PROJECT_SUMMARY The Johnston County Osteoarthritis Project (JoCo) is a 20+ year ongoing, +PR:PROJECT_SUMMARY population-based, prospective cohort study of knee and hip OA designed to be +PR:PROJECT_SUMMARY representative of the civilian, non-institutionalized, African American or +PR:PROJECT_SUMMARY Caucasian population aged 45 years and older, who were residents of one of 6 +PR:PROJECT_SUMMARY townships in Johnston County NC for at least 1 year, and who were physically and +PR:PROJECT_SUMMARY mentally capable of completing the studys protocol. All participants had an +PR:PROJECT_SUMMARY initial home interview, a clinic examination with radiographs, and a subsequent +PR:PROJECT_SUMMARY second home interview. Approximately 3,200 individuals were recruited into the +PR:PROJECT_SUMMARY baseline evaluation between 1991 and 1997 (T0); the first follow up visit (T1) +PR:PROJECT_SUMMARY occurred from 1999-2004, and the second follow up visit (T2) from 2006-2010. +PR:PROJECT_SUMMARY Cohort enrichment (T1*) occurred from 2003-4, allowing new participants to be +PR:PROJECT_SUMMARY enrolled; the first follow up for these individuals was at T2. At T0, the sample +PR:PROJECT_SUMMARY was approximately 38% men and one-third African American. Between T0 and T1, +PR:PROJECT_SUMMARY radiographic knee OA, defined as Kellgren-Lawrence grade 2-4, developed in 12% +PR:PROJECT_SUMMARY of knees without knee OA at T0. +PR:INSTITUTE University of North Carolina at Chapel Hill +PR:DEPARTMENT Thurston Arthritis Research Center, Division of Rheumatology, Allergy, and +PR:DEPARTMENT Immunology +PR:LABORATORY Jordan Laboratory +PR:LAST_NAME Jordan +PR:FIRST_NAME Joanne +PR:ADDRESS 3300 Thurston Bldg., CB# 7280, Chapel Hill, NC 27599-7280 +PR:EMAIL wpathmasiri@rti.org +PR:PHONE 1-866-827-2762 +PR:FUNDING_SOURCE Center for Disease Control (CDC) +#STUDY +ST:STUDY_TITLE Biomarker Discovery in Knee Osteoarthritis (II) +ST:STUDY_TYPE Biomarker Discovery in Knee Osteoarthritis +ST:STUDY_SUMMARY This metabolomics pilot and feasibility (P & F) study was conducted to provide +ST:STUDY_SUMMARY data to be used to gain a better understanding of metabolic alterations in +ST:STUDY_SUMMARY people with knee osteoarthritis (OA) and to discover novel biomarkers of the +ST:STUDY_SUMMARY disease. The goal of the metabolomics study was to determine if metabolic +ST:STUDY_SUMMARY differences, detected by a comprehensive metabolomics analysis, can be used to +ST:STUDY_SUMMARY distinguish people who will develop symptomatic knee OA from those who will not. +ST:STUDY_SUMMARY For this metabolomics study, individuals participating in T1 or T1* with 5-year +ST:STUDY_SUMMARY follow-up at T2 were selected. At T2 subjects were on average 68.1(9.12) years +ST:STUDY_SUMMARY old with an average BMI of 31.4(7.01) with 32% men and one-third African +ST:STUDY_SUMMARY American. All had weight-bearing posterior-anterior knee films obtained with the +ST:STUDY_SUMMARY Synaflexer positioning device at both time points and read paired for +ST:STUDY_SUMMARY Kellgren-Lawrence grade and minimum joint space. Urine samples (second morning +ST:STUDY_SUMMARY void) collected from 36 overweight or obese participants in the JoCo at T1 or +ST:STUDY_SUMMARY T1* were selected from two subgroups (a group that developed radiographic +ST:STUDY_SUMMARY osteoarthritis (n=16) and an age, race, sex, and BMI matched group that did not +ST:STUDY_SUMMARY develop osteoarthritis (n=20). Radiographic knee OA was defined as +ST:STUDY_SUMMARY Kellgren-Lawrence grade 2-4 at T2 in a person with Kellgren-Lawrence grade 0 or +ST:STUDY_SUMMARY 1 at T1 or T1*. +ST:INSTITUTE RTI International +ST:DEPARTMENT Systems and Translational Sciences (STS) +ST:LABORATORY NIH Eastern Regional Comprehensive Metabolomics Resource Core (RTI RCMRC) +ST:LAST_NAME Sumner +ST:FIRST_NAME Susan +ST:ADDRESS 3040, East Cornwallis Road, Research Triangle Park, NC 27709 +ST:EMAIL ssumner@rti.org +ST:PHONE 919-541-7479 +ST:NUM_GROUPS 2 +ST:TOTAL_SUBJECTS 36 +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +SU:AGE_OR_AGE_RANGE Average age 68.1(9.12) +SU:WEIGHT_OR_WEIGHT_RANGE Average BMI of 31.4(7.01) +SU:GENDER Male, Female +SU:SPECIES_GROUP Human +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS - C0559 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0629 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0640 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0835 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0613 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0762 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1113 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1158 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D2090 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0004 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0195 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0225 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0309 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0487 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0036 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0108 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - A0233 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A0490 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A2003 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C0586 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C2177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0729 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0909 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0945 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D1174 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2054 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2062 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2079 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2111 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2404 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2532 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0546 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0607 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E2036 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - F0035 Disease Status:Control +#COLLECTION +CO:COLLECTION_SUMMARY Urine samples (second morning void) collected from 36 overweight or obese +CO:COLLECTION_SUMMARY participants in the JoCo at T1 or T1* were selected from two subgroups (a group +CO:COLLECTION_SUMMARY that developed radiographic osteoarthritis (n=16) and an age, race, sex, and BMI +CO:COLLECTION_SUMMARY matched group that did not develop osteoarthritis (n=20). Radiographic knee OA +CO:COLLECTION_SUMMARY was defined as Kellgren-Lawrence grade 2-4 at T2 in a person with +CO:COLLECTION_SUMMARY Kellgren-Lawrence grade 0 or 1 at T1 or T1*. +CO:SAMPLE_TYPE Urine +CO:STORAGE_CONDITIONS -80C +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Chenomx Internal Standard solution (70 ul) and 230 ul D20 was added to each of +SP:SAMPLEPREP_SUMMARY the 36 urine sample (400 ul), vortexed for 30s, and centrifuged at 12000 rcf for +SP:SAMPLEPREP_SUMMARY 5min. Chenomx ISTD (Chenomx, Edmonton, Alberta, Canada) contains 5mM +SP:SAMPLEPREP_SUMMARY 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS, Chemical Shift Indicator), 100 +SP:SAMPLEPREP_SUMMARY mM Imidazole (pH indicator), and 0.2% NaN3 (to inhibit bacterial growth) in D2O. +SP:SAMPLEPREP_SUMMARY 600 l aliquot of the supernatant was transferred into 5mm NMR tubes +SP:SAMPLEPREP_SUMMARY (Bruker-Biospin, Germany). Phenotypic pooled urine samples were made by +SP:SAMPLEPREP_SUMMARY combining 150 l aliquots from each of the study samples belonging to the same +SP:SAMPLEPREP_SUMMARY phenotype (cases and control). In addition, a combined phenotypic pooled sample +SP:SAMPLEPREP_SUMMARY was prepared by using 1000 l aliquot from each of the phenotypic pooled sample. +SP:SAMPLEPREP_SUMMARY Pooled NMR samples were prepared as described above and used as quality check +SP:SAMPLEPREP_SUMMARY (QC) samples. +SP:SAMPLEPREP_PROTOCOL_FILENAME Johnston_County_Procedure_March_4_2014.docx +SP:PROCESSING_METHOD Dilution using a mixture of D2O and Chenomx ISTD +SP:PROCESSING_STORAGE_CONDITIONS On ice +SP:EXTRACTION_METHOD None +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY None +CH:CHROMATOGRAPHY_TYPE - +CH:INSTRUMENT_NAME - +CH:COLUMN_NAME - +#ANALYSIS +AN:LABORATORY_NAME RTI, DHMRI +AN:ANALYSIS_TYPE NMR +AN:ACQUISITION_DATE 41527 +AN:SOFTWARE_VERSION TopSpin 3.0 +AN:OPERATOR_NAME Wimal Pathmasiri, Kevin Knagge +AN:DETECTOR_TYPE NMR +AN:DATA_FORMAT Bruker +#NMR +NM:INSTRUMENT_TYPE Bruker Avance III +NM:NMR_EXPERIMENT_TYPE 1D 1H +NM:FIELD_FREQUENCY_LOCK Deuterium +NM:STANDARD_CONCENTRATION 0.5mm +NM:SPECTROMETER_FREQUENCY 950 MHz +NM:NMR_PROBE 5mm, Inverse ATMA +NM:NMR_SOLVENT D2O +NM:NMR_TUBE_SIZE 5mm, 7 inch +NM:SHIMMING_METHOD Topshim +NM:PULSE_SEQUENCE noesypr1d +NM:WATER_SUPPRESSION presat +NM:PULSE_WIDTH 13.07 us +NM:POWER_LEVEL 12.589w +NM:RECEIVER_GAIN 4 +NM:OFFSET_FREQUENCY 4468.31 +NM:CHEMICAL_SHIFT_REF_CPD DSS +NM:TEMPERATURE 298 K +NM:NUMBER_OF_SCANS 64 +NM:DUMMY_SCANS 4 +NM:ACQUISITION_TIME 1.7301503 s +NM:RELAXATION_DELAY 2 s +NM:SPECTRAL_WIDTH 18939.395 Hz, 19.93 ppm +NM:NUM_DATA_POINTS_ACQUIRED 65536 +NM:REAL_DATA_POINTS 65536 +NM:LINE_BROADENING 0.5 +NM:ZERO_FILLING Yes +NM:APODIZATION Lorentzian +NM:BASELINE_CORRECTION_METHOD Polynomial +NM:CHEMICAL_SHIFT_REF_STD DSS +NM:INSTRUMENT_NAME Bruker Avance III +NM:NMR_RESULTS_FILE ST000071_AN000111_Results.txt UNITS:Peak area Has m/z:Yes Has RT:Yes RT units:Minutes +#NMR_BINNED_DATA +NMR_BINNED_DATA:UNITS Peak area +NMR_BINNED_DATA_START +Bin range(ppm) C0559 C0629 C0640 C0835 D0613 D0762 D1113 D1158 D2090 E0004 E0195 E0225 E0309 E0487 F0036 F0108 A0233 A0490 A2003 C0586 C2177 D0177 D0729 D0909 D0945 D1174 D2054 D2062 D2079 D2111 D2404 D2532 E0546 E0607 E2036 F0035 +0.4...0.46 0.000076 0 0.000344 0.000641 0.00663 0 0.00314 0 0 0.00172 0 0.00125 0.00411 0.00172 0.0166 0 0 0 0 0 0 0.00921 0 0 0 0 0.00155 0.0000239 0.0273 0 0 0 0.00145 0.00378 0 0.00427 +0.46...0.52 0 0.0178 0 0 0 0 0.00242 0.00371 0 0 0.00169 0 0 0 0 0.00231 0.0186 0 0 0.0169 0 0 0.00188 0.00108 0.000479 0.000933 0 0 0 0.0063 0.0108 0 0 0 0 0 +0.52...0.54 0.0732 0 0.00183 0.00644 0 0.0179 0.0802 0.0235 0.00245 0.0685 0.0557 0.0044 0.0223 0 0 0.0063 0 0.00299 0.0345 0 0.0302 0.0169 0.0558 0 0.007 0 0 0.0604 0.00181 0 0.00015 0.0172 0.00639 0.0103 0.0227 0.0218 +0.54...0.57 0.0848 0.0218 0.000186 0 0.0106 0.0198 0.104 0.0483 0.000998 0.0305 0.0629 0.0169 0.00754 0.01 0.0206 0.0111 0.0182 0.036 0.022 0.00664 0.0359 0.0147 0.0787 0.00569 0 0.00343 0.0122 0.0299 0.0117 0.00324 0.00141 0.0166 0.0147 0.0302 0 0 +0.57...0.6 0.032 0 0 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0.00835 0.0351 0 0.00575 0.00151 0.00239 0 0.00124 0 +9.22...9.28 0.0724 0.394 0.0584 0.0355 0.115 0.0254 0.016 0.194 0.0239 0.022 0.0729 0.071 0.000182 0.144 0.027 0.0345 0.0878 0.142 0.102 0 0.0167 0.00664 0.0204 0.121 0.0228 0 3.39 0.0275 0 0.0463 0.00648 0.0214 0.0771 0.0353 0.0225 0.0141 +9.28...9.33 0 0.00429 0.00574 0 0.00844 0 0 0 0 0 0.00564 0 0.00267 0 0 0 0.0205 0 0 0 0 0 0 0.000105 0 0.0047 0.000424 0 0 0 0 0 0 0 0 0 +9.33...9.36 0.00678 0.0221 0.000631 0.00392 0.00672 0.001 0.00353 0.00347 0.00678 0.0138 0 0.0144 0 0.00351 0.00348 0 0.00889 0.0044 0.00242 0.00908 0.000109 0.00298 0.00298 0.00067 0 0 0.00397 0 0.0441 0.00111 0.00955 0 0.0144 0 0.00279 0.017 +9.36...9.41 0 0 0 0 0 0.000173 0.000326 0 0.0063 0 0.000993 0.00122 0 0 0.0165 0.0112 0.0087 0.00696 0 0 0 0 0.00591 0 0 0.003 0 0.00574 0.0223 0 0.00465 0.00387 0 0 0 0 +9.41...9.46 0 0.0141 0 0.00265 0 0 0 0 0 0.00243 0.000198 0 0 0.00369 0 0 0 0 0.00111 0.0855 0 0.000979 0 0 0.00845 0 0.0000509 0 0.0109 0.0154 0 0 0 0.0082 0.0102 0.00947 +9.46...9.52 0.00215 0 0 0.00083 0 0.0135 0 0.00541 0 0 0.000605 0 0.00886 0 0 0 0 0.00163 0 0 0 0 0.00114 0.000367 0 0 0 0.0122 0 0 0.0086 0 0 0.00485 0.000589 0 +9.52...9.55 0.00222 0.0277 0 0.00759 0 0 0 0 0 0.00277 0 0.00221 0 0 0.00146 0.0161 0.0395 0.00135 0 0.103 0.00278 0.00592 0.00196 0.00846 0 0.03 0.00544 0.00753 0.0906 0.0445 0.0024 0 0.00269 0.00224 0 0.019 +9.55...9.58 0.00137 0 0.00278 0 0.0153 0.000841 0 0 0.00254 0 0 0.00767 0 0 0.0444 0.00127 0 0 0.00419 0 0.00322 0 0 0 0.00765 0 0 0 0 0 0 0.00385 0 0.0112 0.0000583 0.00498 +9.58...9.61 0 0 0.0000985 0 0 0 0 0 0.00132 0 0.00145 0 0.00756 0.0104 0 0.00374 0 0.00408 0 0 0 0.00218 0 0 0.000181 0.00728 0 0.00188 0.0266 0.00949 0 0 0 0 0 0 +9.61...9.66 0 0.0049 0.00223 0.00246 0.00543 0.00422 0.005 0.00427 0 0.000493 0 0 0 0 0.0188 0 0.0199 0.00497 0.00197 0.0482 0.00636 0 0 0.00864 0 0 0.0965 0 0.023 0 0.00847 0.00334 0 0.0204 0.00329 0 +9.66...9.7 0.000695 0 0 0 0.00412 0 0 0 0.0192 0.105 0.00237 0 0 0.0299 0 0 0 0 0.000187 0 0 0 0.00638 0 0 0 0.373 0.074 0 0.00743 0 0.00318 0 0 0 0.0000535 +9.7...9.74 0.00136 0.0023 0 0.00441 0.0073 0 0.00769 0.00201 0 0.00213 0 0.00511 0 0 0.00723 0.00726 0.000864 0.0105 0 0 0.0102 0.00231 0 0.00253 0.016 0.0047 0.259 0.00254 0.0443 0 0.0199 0.000539 0.00146 0.0191 0 0 +9.74...9.76 0.00237 0 0.00266 0.00325 0.00202 0.0291 0 0 0.000089 0 0.00157 0.0273 0.00637 0.000711 0.0343 0.0108 0.00302 0 0.00156 0.0493 0 0 0.0111 0 0 0 0 0 0.00493 0.00754 0 0 0.000636 0 0.003 0.00991 +9.76...9.81 0 0.00787 0 0.00048 0.00332 0 0 0 0.00702 0.00331 0 0 0 0 0 0 0 0.00327 0 0 0 0.0015 0.000969 0 0.000737 0.00486 0.0073 0 0 0.0197 0.00641 0 0 0.00238 0 0 +9.81...9.85 0 0 0.00923 0 0 0.000537 0.0000414 0 0 0 0 0.0142 0 0.00765 0 0 0 0.00308 0.00378 0.0276 0.00401 0.000268 0 0.0108 0 0 0 0.0185 0 0 0 0 0 0 0.00241 0.0306 +9.85...9.89 0 0 0.00417 0 0 0.0196 0.00268 0 0 0 0 0 0.00174 0 0.0151 0 0 0 0 0 0 0.00221 0 0 0.0023 0.00238 0 0 0.0817 0 0.00621 0 0 0.011 0.0136 0 +9.89...9.92 0.00156 0.00191 0 0.00344 0.0121 0 0.00142 0.0126 0.0066 0.00119 0.00206 0.00492 0 0.00159 0 0.00996 0.0201 0 0 0.0417 0 0 0 0 0.00447 0 0.00133 0.00568 0 0 0.00724 0.00214 0.0019 0 0 0.00326 +9.92...9.97 0 0.0154 0.00305 0 0 0 0 0 0 0.00224 0 0.00122 0.00421 0 0.0437 0.00288 0.00875 0.0078 0.0000109 0 0.00635 0 0.00486 0.0011 0 0.0192 0 0 0.0196 0.00243 0 0 0 0 0.000675 0.0297 +9.97...10 0.00048 0 0 0.000271 0.00875 0.0166 0.00753 0.0101 0.00357 0 0.000415 0 0 0.00333 0 0.00922 0.0166 0 0.00322 0 0 0 0 0.00245 0.0102 0 0.00438 0 0.0275 0 0.00939 0 0 0.00728 0.00445 0 +NMR_BINNED_DATA_END +#END + + + + + + diff --git a/tests/example_data/other_mwtab_files/ST000022_AN000041_results_file_bad_location.txt b/tests/example_data/other_mwtab_files/ST000022_AN000041_results_file_bad_location.txt new file mode 100644 index 0000000..b2c41b1 --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000022_AN000041_results_file_bad_location.txt @@ -0,0 +1,408 @@ +#METABOLOMICS WORKBENCH wpathmasiri_20140228_9328071_mwtab.txt DATATRACK_ID:28 TEXT OUTPUT STUDY_ID:ST000022 ANALYSIS_ID:AN000041 PROJECT_ID:PR000021 +VERSION 1 +CREATED_ON 10-02-2015 +#PROJECT +PR:PROJECT_TITLE Johnston County Osteoarthritis Project +PR:PROJECT_TYPE Longitudinal study +PR:PROJECT_SUMMARY The Johnston County Osteoarthritis Project (JoCo) is a 20+ year ongoing, +PR:PROJECT_SUMMARY population-based, prospective cohort study of knee and hip OA designed to be +PR:PROJECT_SUMMARY representative of the civilian, non-institutionalized, African American or +PR:PROJECT_SUMMARY Caucasian population aged 45 years and older, who were residents of one of 6 +PR:PROJECT_SUMMARY townships in Johnston County NC for at least 1 year, and who were physically and +PR:PROJECT_SUMMARY mentally capable of completing the studys protocol. All participants had an +PR:PROJECT_SUMMARY initial home interview, a clinic examination with radiographs, and a subsequent +PR:PROJECT_SUMMARY second home interview. Approximately 3,200 individuals were recruited into the +PR:PROJECT_SUMMARY baseline evaluation between 1991 and 1997 (T0); the first follow up visit (T1) +PR:PROJECT_SUMMARY occurred from 1999-2004, and the second follow up visit (T2) from 2006-2010. +PR:PROJECT_SUMMARY Cohort enrichment (T1*) occurred from 2003-4, allowing new participants to be +PR:PROJECT_SUMMARY enrolled; the first follow up for these individuals was at T2. At T0, the sample +PR:PROJECT_SUMMARY was approximately 38% men and one-third African American. Between T0 and T1, +PR:PROJECT_SUMMARY radiographic knee OA, defined as Kellgren-Lawrence grade 2-4, developed in 12% +PR:PROJECT_SUMMARY of knees without knee OA at T0. +PR:INSTITUTE University of North Carolina at Chapel Hill +PR:DEPARTMENT Thurston Arthritis Research Center, Division of Rheumatology, Allergy, and +PR:DEPARTMENT Immunology +PR:LABORATORY Jordan Laboratory +PR:LAST_NAME Jordan +PR:FIRST_NAME Joanne +PR:ADDRESS 3300 Thurston Bldg., CB# 7280, Chapel Hill, NC 27599-7280 +PR:EMAIL wpathmasiri@rti.org +PR:PHONE 1-866-827-2762 +PR:FUNDING_SOURCE Center for Disease Control (CDC) +#STUDY +ST:STUDY_TITLE Biomarker Discovery in Knee Osteoarthritis (II) +ST:STUDY_TYPE Biomarker Discovery in Knee Osteoarthritis +ST:STUDY_SUMMARY This metabolomics pilot and feasibility (P & F) study was conducted to provide +ST:STUDY_SUMMARY data to be used to gain a better understanding of metabolic alterations in +ST:STUDY_SUMMARY people with knee osteoarthritis (OA) and to discover novel biomarkers of the +ST:STUDY_SUMMARY disease. The goal of the metabolomics study was to determine if metabolic +ST:STUDY_SUMMARY differences, detected by a comprehensive metabolomics analysis, can be used to +ST:STUDY_SUMMARY distinguish people who will develop symptomatic knee OA from those who will not. +ST:STUDY_SUMMARY For this metabolomics study, individuals participating in T1 or T1* with 5-year +ST:STUDY_SUMMARY follow-up at T2 were selected. At T2 subjects were on average 68.1(9.12) years +ST:STUDY_SUMMARY old with an average BMI of 31.4(7.01) with 32% men and one-third African +ST:STUDY_SUMMARY American. All had weight-bearing posterior-anterior knee films obtained with the +ST:STUDY_SUMMARY Synaflexer positioning device at both time points and read paired for +ST:STUDY_SUMMARY Kellgren-Lawrence grade and minimum joint space. Urine samples (second morning +ST:STUDY_SUMMARY void) collected from 36 overweight or obese participants in the JoCo at T1 or +ST:STUDY_SUMMARY T1* were selected from two subgroups (a group that developed radiographic +ST:STUDY_SUMMARY osteoarthritis (n=16) and an age, race, sex, and BMI matched group that did not +ST:STUDY_SUMMARY develop osteoarthritis (n=20). Radiographic knee OA was defined as +ST:STUDY_SUMMARY Kellgren-Lawrence grade 2-4 at T2 in a person with Kellgren-Lawrence grade 0 or +ST:STUDY_SUMMARY 1 at T1 or T1*. +ST:INSTITUTE RTI International +ST:DEPARTMENT Systems and Translational Sciences (STS) +ST:LABORATORY NIH Eastern Regional Comprehensive Metabolomics Resource Core (RTI RCMRC) +ST:LAST_NAME Sumner +ST:FIRST_NAME Susan +ST:ADDRESS 3040, East Cornwallis Road, Research Triangle Park, NC 27709 +ST:EMAIL ssumner@rti.org +ST:PHONE 919-541-7479 +ST:NUM_GROUPS 2 +ST:TOTAL_SUBJECTS 36 +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +SU:AGE_OR_AGE_RANGE Average age 68.1(9.12) +SU:WEIGHT_OR_WEIGHT_RANGE Average BMI of 31.4(7.01) +SU:GENDER Male, Female +SU:SPECIES_GROUP Human +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS - C0559 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0629 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0640 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - C0835 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0613 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D0762 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1113 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D1158 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - D2090 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0004 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0195 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0225 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0309 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - E0487 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0036 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - F0108 Disease Status:Cases +SUBJECT_SAMPLE_FACTORS - A0233 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A0490 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - A2003 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C0586 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - C2177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0177 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0729 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0909 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D0945 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D1174 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2054 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2062 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2079 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2111 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2404 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - D2532 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0546 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E0607 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - E2036 Disease Status:Control +SUBJECT_SAMPLE_FACTORS - F0035 Disease Status:Control +#COLLECTION +CO:COLLECTION_SUMMARY Urine samples (second morning void) collected from 36 overweight or obese +CO:COLLECTION_SUMMARY participants in the JoCo at T1 or T1* were selected from two subgroups (a group +CO:COLLECTION_SUMMARY that developed radiographic osteoarthritis (n=16) and an age, race, sex, and BMI +CO:COLLECTION_SUMMARY matched group that did not develop osteoarthritis (n=20). Radiographic knee OA +CO:COLLECTION_SUMMARY was defined as Kellgren-Lawrence grade 2-4 at T2 in a person with +CO:COLLECTION_SUMMARY Kellgren-Lawrence grade 0 or 1 at T1 or T1*. +CO:SAMPLE_TYPE Urine +CO:STORAGE_CONDITIONS -80C +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Chenomx Internal Standard solution (70 ul) and 230 ul D20 was added to each of +SP:SAMPLEPREP_SUMMARY the 36 urine sample (400 ul), vortexed for 30s, and centrifuged at 12000 rcf for +SP:SAMPLEPREP_SUMMARY 5min. Chenomx ISTD (Chenomx, Edmonton, Alberta, Canada) contains 5mM +SP:SAMPLEPREP_SUMMARY 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS, Chemical Shift Indicator), 100 +SP:SAMPLEPREP_SUMMARY mM Imidazole (pH indicator), and 0.2% NaN3 (to inhibit bacterial growth) in D2O. +SP:SAMPLEPREP_SUMMARY 600 l aliquot of the supernatant was transferred into 5mm NMR tubes +SP:SAMPLEPREP_SUMMARY (Bruker-Biospin, Germany). Phenotypic pooled urine samples were made by +SP:SAMPLEPREP_SUMMARY combining 150 l aliquots from each of the study samples belonging to the same +SP:SAMPLEPREP_SUMMARY phenotype (cases and control). In addition, a combined phenotypic pooled sample +SP:SAMPLEPREP_SUMMARY was prepared by using 1000 l aliquot from each of the phenotypic pooled sample. +SP:SAMPLEPREP_SUMMARY Pooled NMR samples were prepared as described above and used as quality check +SP:SAMPLEPREP_SUMMARY (QC) samples. +SP:SAMPLEPREP_PROTOCOL_FILENAME Johnston_County_Procedure_March_4_2014.docx +SP:PROCESSING_METHOD Dilution using a mixture of D2O and Chenomx ISTD +SP:PROCESSING_STORAGE_CONDITIONS On ice +SP:EXTRACTION_METHOD None +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY None +CH:CHROMATOGRAPHY_TYPE - +CH:INSTRUMENT_NAME - +CH:COLUMN_NAME - +#ANALYSIS +AN:LABORATORY_NAME RTI, DHMRI +AN:ANALYSIS_TYPE NMR +AN:ACQUISITION_DATE 41527 +AN:SOFTWARE_VERSION TopSpin 3.0 +AN:OPERATOR_NAME Wimal Pathmasiri, Kevin Knagge +AN:DETECTOR_TYPE NMR +AN:DATA_FORMAT Bruker +#NMR +NM:INSTRUMENT_TYPE Bruker Avance III +NM:NMR_EXPERIMENT_TYPE 1D 1H +NM:FIELD_FREQUENCY_LOCK Deuterium +NM:STANDARD_CONCENTRATION 0.5mm +NM:SPECTROMETER_FREQUENCY 950 MHz +NM:NMR_PROBE 5mm, Inverse ATMA +NM:NMR_SOLVENT D2O +NM:NMR_TUBE_SIZE 5mm, 7 inch +NM:SHIMMING_METHOD Topshim +NM:PULSE_SEQUENCE noesypr1d +NM:WATER_SUPPRESSION presat +NM:PULSE_WIDTH 13.07 us +NM:POWER_LEVEL 12.589w +NM:RECEIVER_GAIN 4 +NM:OFFSET_FREQUENCY 4468.31 +NM:CHEMICAL_SHIFT_REF_CPD DSS +NM:TEMPERATURE 298 K +NM:NUMBER_OF_SCANS 64 +NM:DUMMY_SCANS 4 +NM:ACQUISITION_TIME 1.7301503 s +NM:RELAXATION_DELAY 2 s +NM:SPECTRAL_WIDTH 18939.395 Hz, 19.93 ppm +NM:NUM_DATA_POINTS_ACQUIRED 65536 +NM:REAL_DATA_POINTS 65536 +NM:LINE_BROADENING 0.5 +NM:ZERO_FILLING Yes +NM:APODIZATION Lorentzian +NM:BASELINE_CORRECTION_METHOD Polynomial +NM:CHEMICAL_SHIFT_REF_STD DSS +NM:INSTRUMENT_NAME Bruker Avance III +#NMR_BINNED_DATA +NMR_BINNED_DATA:NMR_RESULTS_FILE ST000071_AN000111_Results.txt UNITS:Peak area Has m/z:Yes Has RT:Yes RT units:Minutes +NMR_BINNED_DATA:UNITS Peak area +NMR_BINNED_DATA_START +Bin range(ppm) C0559 C0629 C0640 C0835 D0613 D0762 D1113 D1158 D2090 E0004 E0195 E0225 E0309 E0487 F0036 F0108 A0233 A0490 A2003 C0586 C2177 D0177 D0729 D0909 D0945 D1174 D2054 D2062 D2079 D2111 D2404 D2532 E0546 E0607 E2036 F0035 +0.4...0.46 0.000076 0 0.000344 0.000641 0.00663 0 0.00314 0 0 0.00172 0 0.00125 0.00411 0.00172 0.0166 0 0 0 0 0 0 0.00921 0 0 0 0 0.00155 0.0000239 0.0273 0 0 0 0.00145 0.00378 0 0.00427 +0.46...0.52 0 0.0178 0 0 0 0 0.00242 0.00371 0 0 0.00169 0 0 0 0 0.00231 0.0186 0 0 0.0169 0 0 0.00188 0.00108 0.000479 0.000933 0 0 0 0.0063 0.0108 0 0 0 0 0 +0.52...0.54 0.0732 0 0.00183 0.00644 0 0.0179 0.0802 0.0235 0.00245 0.0685 0.0557 0.0044 0.0223 0 0 0.0063 0 0.00299 0.0345 0 0.0302 0.0169 0.0558 0 0.007 0 0 0.0604 0.00181 0 0.00015 0.0172 0.00639 0.0103 0.0227 0.0218 +0.54...0.57 0.0848 0.0218 0.000186 0 0.0106 0.0198 0.104 0.0483 0.000998 0.0305 0.0629 0.0169 0.00754 0.01 0.0206 0.0111 0.0182 0.036 0.022 0.00664 0.0359 0.0147 0.0787 0.00569 0 0.00343 0.0122 0.0299 0.0117 0.00324 0.00141 0.0166 0.0147 0.0302 0 0 +0.57...0.6 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+} + +} + diff --git a/tests/example_data/other_mwtab_files/ST000122_AN000204_binned.json b/tests/example_data/other_mwtab_files/ST000122_AN000204_binned.json new file mode 100644 index 0000000..f8c01ef --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000122_AN000204_binned.json @@ -0,0 +1,244 @@ +{ +"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST000122","ANALYSIS_ID":"AN000204","VERSION":"1","CREATED_ON":"2016-09-17"}, + +"PROJECT":{"PROJECT_TITLE":"Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic","PROJECT_TYPE":"Pilot and Feasibility Projects","PROJECT_SUMMARY":"-","INSTITUTE":"University of California, Davis","DEPARTMENT":"Nutrition","LABORATORY":"Gaikwad Lab","LAST_NAME":"Gaikwad","FIRST_NAME":"Nilesh","ADDRESS":"-","EMAIL":"nwgaikwad@ucdavis.edu","PHONE":"530-752-2906","FUNDING_SOURCE":"NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)"}, + +"STUDY":{"STUDY_TITLE":"Perinatal DDT causes dysfunctional 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+} + +} + diff --git a/tests/example_data/validation_files/ST000122_AN000204_error_3.json b/tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.json similarity index 98% rename from tests/example_data/validation_files/ST000122_AN000204_error_3.json rename to tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.json index 476d00c..b4ef5c1 100644 --- a/tests/example_data/validation_files/ST000122_AN000204_error_3.json +++ b/tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.json @@ -1,242 +1,244 @@ -{ -"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST000122","ANALYSIS_ID":"AN000204","VERSION":"1","CREATED_ON":"2016-09-17"}, - -"PROJECT":{"PROJECT_TITLE":"Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic","PROJECT_TYPE":"Pilot and Feasibility Projects","PROJECT_SUMMARY":"-","INSTITUTE":"University of California, Davis","DEPARTMENT":"Nutrition","LABORATORY":"Gaikwad 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+} + +} + diff --git a/tests/example_data/validation_files/ST000122_AN000204_error_1.txt b/tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt similarity index 98% rename from tests/example_data/validation_files/ST000122_AN000204_error_1.txt rename to tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt index b71908f..44ac8da 100644 --- a/tests/example_data/validation_files/ST000122_AN000204_error_1.txt +++ b/tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt @@ -1,162 +1,162 @@ -#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 -VERSION 1 -CREATED_ON 2016-09-17 -#PROJECT -PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic -PR:PROJECT_TYPE Pilot and Feasibility Projects -PR:PROJECT_SUMMARY - -PR:INSTITUTE University of California, Davis -PR:DEPARTMENT Nutrition -PR:LABORATORY Gaikwad Lab -PR:LAST_NAME Gaikwad -PR:FIRST_NAME Nilesh -PR:ADDRESS - -PR:EMAIL nwgaikwad@ucdavis.edu -PR:PHONE 530-752-2906 -PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL -PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) -#STUDY -ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic -ST:STUDY_TYPE steroid panel -ST:STUDY_SUMMARY - -ST:INSTITUTE University of California, Davis -ST:DEPARTMENT Nutrition -ST:LABORATORY Gaikwad Lab -ST:LAST_NAME Gaikwad -ST:FIRST_NAME Nilesh -ST:ADDRESS - -ST:EMAIL nwgaikwad@ucdavis.edu -ST:PHONE - -ST:NUM_GROUPS NA -#SUBJECT -SU:SUBJECT_TYPE Human -SU:SUBJECT_SPECIES Homo sapiens -SU:TAXONOMY_ID 9606 -#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data -SUBJECT_SAMPLE_FACTORS Tissue/Fluid: -SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum -#COLLECTION -CO:COLLECTION_SUMMARY - -#TREATMENT -TR:TREATMENT_SUMMARY - -#SAMPLEPREP -SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction -SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx -SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac -SP:PROCESSING_STORAGE_CONDITIONS On Ice -SP:EXTRACTION_METHOD 1:1 Methanol: Water -SP:EXTRACT_STORAGE -80C -SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O -SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid -SP:ORGAN Fetal: Male and female brain, male and female liver -#CHROMATOGRAPHY -CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS -CH:CHROMATOGRAPHY_TYPE Reversed phase -CH:INSTRUMENT_NAME Waters Acquity -CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) -CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min -CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A -CH:FLOW_RATE 0.15 ml/min -CH:SAMPLE_INJECTION 10ul -CH:SOLVENT_A Water 0.1% formic acid -CH:SOLVENT_B CH3CN 0.1 % formic acid -CH:ANALYTICAL_TIME 12 mins -#ANALYSIS -AN:ANALYSIS_TYPE MS -AN:LABORATORY_NAME Gaikwad Laboratory -AN:ACQUISITION_DATE 41716 -AN:SOFTWARE_VERSION Masslynx -AN:OPERATOR_NAME Nilesh Gaikwad -#MS -MS:INSTRUMENT_NAME Waters Xevo-TQ -MS:INSTRUMENT_TYPE Triple quadrupole -MS:MS_TYPE ESI -MS:ION_MODE POSITIVE -MS:CAPILLARY_VOLTAGE 3.0 kV -MS:COLLISION_GAS N2 -MS:IONIZATION Electrospray Ionization -MS:SOURCE_TEMPERATURE 150C -MS:DESOLVATION_GAS_FLOW 600 L/h -MS:DESOLVATION_TEMPERATURE 350C -MS:MS_COMMENTS UPLC-MS/MS -#MS_METABOLITE_DATA -MS_METABOLITE_DATA:UNITS pg/ml -MS_METABOLITE_DATA_START -Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 -Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum -17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 -17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 -Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 -Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 -Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 -Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 -Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 -Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 -Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 -Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 -Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 -Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 -Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 -Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 -MS_METABOLITE_DATA_END -#METABOLITES -METABOLITES_START -metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type -17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID -17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID -Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID -Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID -Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID -Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID -Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID -Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID -Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID -Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID -Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID -Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID -Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID -Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID -METABOLITES_END -#END - - +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum key_1=value_1; key_1=value_3; key_2=value_2 +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum key_1=value_4; key_2=value_5 +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_error_2.json b/tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys_highest_level.json similarity index 98% rename from tests/example_data/validation_files/ST000122_AN000204_error_2.json rename to tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys_highest_level.json index 5c19d4c..152b43e 100644 --- a/tests/example_data/validation_files/ST000122_AN000204_error_2.json +++ b/tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys_highest_level.json @@ -1,242 +1,245 @@ -{ -"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST000122","ANALYSIS_ID":"AN000204","VERSION":"1","CREATED_ON":"2016-09-17"}, - -"PROJECT":{"PROJECT_TITLE":"Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic","PROJECT_TYPE":"Pilot and Feasibility Projects","PROJECT_SUMMARY":"-","INSTITUTE":"University of California, Davis","DEPARTMENT":"Nutrition","LABORATORY":"Gaikwad Lab","LAST_NAME":"Gaikwad","FIRST_NAME":"Nilesh","ADDRESS":"-","EMAIL":"nwgaikwad@ucdavis.edu","PHONE":"530-752-2906","FUNDING_SOURCE":"NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)"}, - -"STUDY":{"STUDY_TITLE":"Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic","STUDY_TYPE":"steroid panel","STUDY_SUMMARY":"-","INSTITUTE":"University of California, Davis","DEPARTMENT":"Nutrition","LABORATORY":"Gaikwad Lab","LAST_NAME":"Gaikwad","FIRST_NAME":"Nilesh","ADDRESS":"-","EMAIL":"nwgaikwad@ucdavis.edu","PHONE":"-","NUM_GROUPS":"NA"}, - -"SUBJECT":{"SUBJECT_TYPE":"Human","SUBJECT_SPECIES":"Homo sapiens","TAXONOMY_ID":"9606"}, -"SUBJECT_SAMPLE_FACTORS":[ -{ -"Subject ID":"CER030_294717_ML_1", -"Sample ID":"TEST", -"Factors":{"Tissue/Fluid":"Serum"} -}, -{ -"Subject ID":"CER040_242995_ML_2", -"Sample ID":"CER040_242995_ML_2", -"Factors":{"Tissue/Fluid":"Serum"} -}, -{ -"Subject ID":"CER055_249947_ML_3", -"Sample ID":"CER055_249947_ML_3", -"Factors":{"Tissue/Fluid":"Serum"} -}, -{ -"Subject ID":"CER062_246153_ML_4", -"Sample ID":"CER062_246153_ML_4", -"Factors":{"Tissue/Fluid":"Serum"} -}, -{ -"Subject ID":"CER085_251176_ML_5", -"Sample ID":"CER085_251176_ML_5", -"Factors":{"Tissue/Fluid":"Serum"} -}, -{ -"Subject ID":"CER093_242931_ML_6", -"Sample ID":"CER093_242931_ML_6", -"Factors":{"Tissue/Fluid":"Serum"} -}, -{ -"Subject ID":"CER110_238825_ML_7", -"Sample ID":"CER110_238825_ML_7", -"Factors":{"Tissue/Fluid":"Serum"} -}, -{ -"Subject ID":"CER120_253690_ML_8", -"Sample ID":"CER120_253690_ML_8", 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+} + +} + diff --git a/tests/example_data/validation_files/ST000122_AN000204_error_2.txt b/tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_subsections.txt similarity index 98% rename from tests/example_data/validation_files/ST000122_AN000204_error_2.txt rename to tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_subsections.txt index ee18be9..5f7d764 100644 --- a/tests/example_data/validation_files/ST000122_AN000204_error_2.txt +++ b/tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_subsections.txt @@ -1,162 +1,163 @@ -#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 -VERSION 1 -CREATED_ON 2016-09-17 -#PROJECT -PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic -PR:PROJECT_TYPE Pilot and Feasibility Projects -PR:PROJECT_SUMMARY - -PR:INSTITUTE University of California, Davis -PR:DEPARTMENT Nutrition -PR:LABORATORY Gaikwad Lab -PR:LAST_NAME Gaikwad -PR:FIRST_NAME Nilesh -PR:ADDRESS - -PR:EMAIL nwgaikwad@ucdavis.edu -PR:PHONE 530-752-2906 -PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL -PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) -#STUDY -ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic -ST:STUDY_TYPE steroid panel -ST:STUDY_SUMMARY - -ST:INSTITUTE University of California, Davis -ST:DEPARTMENT Nutrition -ST:LABORATORY Gaikwad Lab -ST:LAST_NAME Gaikwad -ST:FIRST_NAME Nilesh -ST:ADDRESS - -ST:EMAIL nwgaikwad@ucdavis.edu -ST:PHONE - -ST:NUM_GROUPS NA -#SUBJECT -SU:SUBJECT_TYPE Human -SU:SUBJECT_SPECIES Homo sapiens -SU:TAXONOMY_ID 9606 -#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data -SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 TEST Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum -#COLLECTION -CO:COLLECTION_SUMMARY - -#TREATMENT -TR:TREATMENT_SUMMARY - -#SAMPLEPREP -SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction -SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx -SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac -SP:PROCESSING_STORAGE_CONDITIONS On Ice -SP:EXTRACTION_METHOD 1:1 Methanol: Water -SP:EXTRACT_STORAGE -80C -SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O -SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid -SP:ORGAN Fetal: Male and female brain, male and female liver -#CHROMATOGRAPHY -CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS -CH:CHROMATOGRAPHY_TYPE Reversed phase -CH:INSTRUMENT_NAME Waters Acquity -CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) -CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min -CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A -CH:FLOW_RATE 0.15 ml/min -CH:SAMPLE_INJECTION 10ul -CH:SOLVENT_A Water 0.1% formic acid -CH:SOLVENT_B CH3CN 0.1 % formic acid -CH:ANALYTICAL_TIME 12 mins -#ANALYSIS -AN:ANALYSIS_TYPE MS -AN:LABORATORY_NAME Gaikwad Laboratory -AN:ACQUISITION_DATE 41716 -AN:SOFTWARE_VERSION Masslynx -AN:OPERATOR_NAME Nilesh Gaikwad -#MS -MS:INSTRUMENT_NAME Waters Xevo-TQ -MS:INSTRUMENT_TYPE Triple quadrupole -MS:MS_TYPE ESI -MS:ION_MODE POSITIVE -MS:CAPILLARY_VOLTAGE 3.0 kV -MS:COLLISION_GAS N2 -MS:IONIZATION Electrospray Ionization -MS:SOURCE_TEMPERATURE 150C -MS:DESOLVATION_GAS_FLOW 600 L/h -MS:DESOLVATION_TEMPERATURE 350C -MS:MS_COMMENTS UPLC-MS/MS -#MS_METABOLITE_DATA -MS_METABOLITE_DATA:UNITS pg/ml -MS_METABOLITE_DATA_START -Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 -Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum -17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 -17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 -Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 -Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 -Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 -Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 -Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 -Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 -Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 -Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 -Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 -Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 -Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 -Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 -MS_METABOLITE_DATA_END -#METABOLITES -METABOLITES_START -metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type -17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID -17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID -Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID -Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID -Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID -Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID -Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID -Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID -Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID -Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID -Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID -Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID -Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID -Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID -METABOLITES_END -#END - - +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 STUDY_ID:ST000123 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum key_1=value_1; key_1=value_3; key_2=value_2 +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum key_1=value_4; key_2=value_5 +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/other_mwtab_files/ST000122_AN000204_extra_sample.txt b/tests/example_data/other_mwtab_files/ST000122_AN000204_extra_sample.txt new file mode 100644 index 0000000..b10d43e --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000122_AN000204_extra_sample.txt @@ -0,0 +1,162 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum key_1=value_1; key_1=value_3; key_2=value_2 +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum key_1=value_4; key_2=value_5 +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 extra_sample +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 123.456 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/other_mwtab_files/ST000122_AN000204_results_file.txt b/tests/example_data/other_mwtab_files/ST000122_AN000204_results_file.txt new file mode 100644 index 0000000..bb11b82 --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000122_AN000204_results_file.txt @@ -0,0 +1,163 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum key_1=value_1; key_1=value_3; key_2=value_2 +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum key_1=value_4; key_2=value_5 +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +MS:MS_RESULTS_FILE ST000071_AN000111_Results.txt UNITS:Peak area Has m/z:Yes Has RT:Yes RT units:Minutes +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/other_mwtab_files/ST000122_AN000204_results_file_bad_location.txt b/tests/example_data/other_mwtab_files/ST000122_AN000204_results_file_bad_location.txt new file mode 100644 index 0000000..449b0f1 --- /dev/null +++ b/tests/example_data/other_mwtab_files/ST000122_AN000204_results_file_bad_location.txt @@ -0,0 +1,163 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum key_1=value_1; key_1=value_3; key_2=value_2 +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum key_1=value_4; key_2=value_5 +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:MS_RESULTS_FILE ST000071_AN000111_Results.txt UNITS:Peak area Has m/z:Yes Has RT:Yes RT units:Minutes +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/other_mwtab_files/bad_file.txt b/tests/example_data/other_mwtab_files/bad_file.txt new file mode 100644 index 0000000..caae386 --- /dev/null +++ b/tests/example_data/other_mwtab_files/bad_file.txt @@ -0,0 +1,10 @@ +Some random text here. +asdf +a +sdf +af +qw +rfd +bfg +h +gf \ No newline at end of file diff --git a/tests/example_data/other_mwtab_files/corrected_section_order.json b/tests/example_data/other_mwtab_files/corrected_section_order.json new file mode 100644 index 0000000..3c91aab --- /dev/null +++ b/tests/example_data/other_mwtab_files/corrected_section_order.json @@ -0,0 +1,271883 @@ +{ + "METABOLOMICS WORKBENCH": { + "VERSION": "1", + "CREATED_ON": "2023-05-03", + "STUDY_ID": "ST000000", + "ANALYSIS_ID": "AN000000" + }, + "PROJECT": { + "PROJECT_TITLE": "Metabolomics of lung injury after allogeneic hematopoietic cell transplantation", + "PROJECT_SUMMARY": "Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment option for a variety of hematological malignancies. Interactions between the donor immune system and the patient tissue result in a disease, called GVHD. The pathophysiology of acute GVHD can be hypothesized in three sequential phases: cytokine storm and activation of the antigen-presenting cells (APC), donor T cell activation and effector cell phase. Idiopathic pneumonia syndrome (IPS) is one of the most deleterious complications after allogeneic HCT and is considered not only to be related to conditioning regimen toxicity but also represents an end organ damage caused by allo-reactive T cells, therefore making the lung susceptible to a two-pronged attack, one of which overlaps with GVHD causing other target organ injury. IPS results in mortality of up to 90% of patients. We will use a murine model of IPS and GVHD which is well established in our group, and in which disease evolves either across disparities in major histocompatibility complex (MCH) class I and II, minor histocompatibility antigens (miHags) or both. Metabolomics changes following syngeneic and allogeneic HCT at post-transplantation Days +7 (cytokine storm phase) and Days +42 (cellular effector phase) are compared to baseline wild-type (naive) controls. Prior to analysis, na\u00efve - and experimental mice (N=3 from each group) were fed with semi-liquid diet supplemented with tracers (13C6-glucose ) over 24 hours. At the end of 7 days or 42 days, respectively, feces and aGVHD target organs (colon, liver and lung) were collected from all groups and further processed and / or analyzed. We expect to reveal metabolic pathways affected after allo-HCT which contribute to immune cell mediated lung injury (IPS) and will potentially identify different metabolic pathways in other GVHD target organs.", + "INSTITUTE": "University of Kentucky", + "DEPARTMENT": "Markey Cancer Center, Hematology and Blood and Marrow Transplant", + "LAST_NAME": "Hildebrandt", + "FIRST_NAME": "Gerhard", + "ADDRESS": "Gerhard C. Hildebrandt, MD, Room no. CC401A, Ben Roach Building, Markey Cancer Center University of Kentucky, Lexington, 40536", + "EMAIL": "gerhard.hildebrandt@uky.edu", + "PHONE": "800-333-8874" + }, + "STUDY": { + "STUDY_TITLE": "Metabolomics of lung injury after allogeneic hematopoietic cell transplantation", + "STUDY_SUMMARY": "Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment option for a variety of hematological malignancies. Interactions between the donor immune system and the patient tissue result in a disease, called GVHD. The pathophysiology of acute GVHD can be hypothesized in three sequential phases: cytokine storm and activation of the antigen-presenting cells (APC), donor T cell activation and effector cell phase. Idiopathic pneumonia syndrome (IPS) is one of the most deleterious complications after allogeneic HCT and is considered not only to be related to conditioning regimen toxicity but also represents an end organ damage caused by allo-reactive T cells, therefore making the lung susceptible to a two-pronged attack, one of which overlaps with GVHD causing other target organ injury. IPS results in mortality of up to 90% of patients. We will use a murine model of IPS and GVHD which is well established in our group, and in which disease evolves either across disparities in major histocompatibility complex (MCH) class I and II, minor histocompatibility antigens (miHags) or both. Metabolomics changes following syngeneic and allogeneic HCT at post-transplantation Days +7 (cytokine storm phase) and Days +42 (cellular effector phase) are compared to baseline wild-type (naive) controls. Prior to analysis, na\u00efve - and experimental mice (N=3 from each group) were fed with semi-liquid diet supplemented with tracers (13C6-glucose ) over 24 hours. At the end of 7 days or 42 days, respectively, feces and aGVHD target organs (colon, liver and lung) were collected from all groups and further processed and / or analyzed. We expect to reveal metabolic pathways affected after allo-HCT which contribute to immune cell mediated lung injury (IPS) and will potentially identify different metabolic pathways in other GVHD target organs.", + "INSTITUTE": "University of Kentucky", + "DEPARTMENT": "MCC", + "LAST_NAME": "Hildebrandt", + "FIRST_NAME": "Gerhard", + "ADDRESS": "CTW-453, 900 South Limestone street. UKY. Lexington, Kentucky-40536", + "EMAIL": "gerhard.hildebrandt@uky.edu", + "PHONE": "800-333-8874" + }, + "ANALYSIS": { + "ANALYSIS_TYPE": "MS" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Mouse", + "SUBJECT_SPECIES": "Mus musculus", + "TAXONOMY_ID": "10090" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "01_A0_naive_0days_UKy_GCH_rep1", + "Sample ID": "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "naive", + "Time Point": "0" + }, + "Additional sample data": { + "lineage0_id": "01_A0_naive_0days_UKy_GCH_rep1", + "lineage0_protocol.id": "['naive']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "0", + "lineage0_type": "subject", + "lineage1_id": "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "0.6181768442446792", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "01_A0_Colon_naive_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "02_A1_naive_0days_UKy_GCH_rep2", + "Sample ID": "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A", + "Factors": { + "Treatment": "naive", + "Time Point": "0" + }, + "Additional sample data": { + "lineage0_id": "02_A1_naive_0days_UKy_GCH_rep2", + "lineage0_protocol.id": "['naive']", + "lineage0_replicate": "2", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "0", + "lineage0_type": "subject", + "lineage1_id": "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-protein", + "lineage2_protein_weight": "0.4626846570186025", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "02_A1_Colon_naive_170427_UKy_GCB_rep2-quench_ICMSA.raw" + } + }, + { + "Subject ID": "03_A2_naive_0days_UKy_GCH_rep3", + "Sample ID": "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A", + "Factors": { + "Treatment": "naive", + "Time Point": "0" + }, + "Additional sample data": { + "lineage0_id": "03_A2_naive_0days_UKy_GCH_rep3", + "lineage0_protocol.id": "['naive']", + "lineage0_replicate": "3", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "0", + "lineage0_type": "subject", + "lineage1_id": "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-protein", + "lineage2_protein_weight": "0.6317204628207644", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "03_A2_Colon_naive_170427_UKy_GCB_rep3-quench_ICMSA.raw" + } + }, + { + "Subject ID": "04_B0_syngenic_42days_UKy_GCH_rep1", + "Sample ID": "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "04_B0_syngenic_42days_UKy_GCH_rep1", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "0.6679368105226386", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "04_B0_Colon_syngenic_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "05_B1_syngenic_42days_UKy_GCH_rep2", + "Sample ID": "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "05_B1_syngenic_42days_UKy_GCH_rep2", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "2", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-protein", + "lineage2_protein_weight": "0.6160223770621668", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "05_B1_Colon_syngenic_170427_UKy_GCB_rep2-quench_ICMSA.raw" + } + }, + { + "Subject ID": "06_B2_syngenic_42days_UKy_GCH_rep3", + "Sample ID": "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "06_B2_syngenic_42days_UKy_GCH_rep3", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "3", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-protein", + "lineage2_protein_weight": "1.5623114343705382", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "06_B2_Colon_syngenic_170427_UKy_GCB_rep3-quench_ICMSA.raw" + } + }, + { + "Subject ID": "07_C1-1_allogenic_42days_UKy_GCH_rep1", + "Sample ID": "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "07_C1-1_allogenic_42days_UKy_GCH_rep1", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "1.2093335004111359", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "07_C1-1_Colon_allogenic_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "08_C1-2_allogenic_42days_UKy_GCH_rep2", + "Sample ID": "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "08_C1-2_allogenic_42days_UKy_GCH_rep2", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "2", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-protein", + "lineage2_protein_weight": "1.6115317800148334", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "08_C1-2_Colon_allogenic_170427_UKy_GCB_rep2-quench_ICMSA.raw" + } + }, + { + "Subject ID": "09_C2-0_allogenic_42days_UKy_GCH_rep1", + "Sample ID": "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "09_C2-0_allogenic_42days_UKy_GCH_rep1", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "1.6843458692847952", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "09_C2-0_Colon_allogenic_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "10_B1-0_syngenic_7days_UKy_GCH_rep1", + "Sample ID": "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "10_B1-0_syngenic_7days_UKy_GCH_rep1", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "0.9744309499653369", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "10_B1-0_Colon_syngenic_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "11_B1-1_syngenic_7days_UKy_GCH_rep2", + "Sample ID": "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "11_B1-1_syngenic_7days_UKy_GCH_rep2", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "2", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-protein", + "lineage2_protein_weight": "1.2443973624103961", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "11_B1-1_Colon_syngenic_170427_UKy_GCB_rep2-quench_ICMSA.raw" + } + }, + { + "Subject ID": "12_B1-2_syngenic_7days_UKy_GCH_rep3", + "Sample ID": "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "12_B1-2_syngenic_7days_UKy_GCH_rep3", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "3", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-protein", + "lineage2_protein_weight": "1.6127238309055993", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "12_B1-2_Colon_syngenic_170427_UKy_GCB_rep3-quench_ICMSA.raw" + } + }, + { + "Subject ID": "13_C1-1_allogenic_7days_UKy_GCH_rep1", + "Sample ID": "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "13_C1-1_allogenic_7days_UKy_GCH_rep1", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "2.0735556513766173", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "13_C1-1_Colon_allogenic_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "14_C1-2_allogenic_7days_UKy_GCH_rep2", + "Sample ID": "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "14_C1-2_allogenic_7days_UKy_GCH_rep2", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "2", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-protein", + "lineage2_protein_weight": "1.7420773775641787", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "14_C1-2_Colon_allogenic_170427_UKy_GCB_rep2-quench_ICMSA.raw" + } + }, + { + "Subject ID": "15_C1-20_allogenic_7days_UKy_GCH_rep3", + "Sample ID": "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "15_C1-20_allogenic_7days_UKy_GCH_rep3", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "3", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-protein", + "lineage2_protein_weight": "1.9186640291000003", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "15_C1-20_Colon_allogenic_170427_UKy_GCB_rep3-quench_ICMSA.raw" + } + } + ], + "COLLECTION": { + "COLLECTION_SUMMARY": "Mouse is sacrificed and tissues are harvested.", + "COLLECTION_PROTOCOL_ID": "mouse_tissue_collection", + "COLLECTION_PROTOCOL_FILENAME": "mouse_tissue_procedure.pdf", + "SAMPLE_TYPE": "mouse" + }, + "TREATMENT": { + "TREATMENT_SUMMARY": "Mouse with allogenic bone marrow transplant. Fed with semi-liquid diet supplemented with fully labeled glucose for 24 hours before harvest.;Mouse with no treatment. Fed with semi-liquid diet supplemented with fully labeled glucose for 24 hours before harvest.;Mouse with syngenic bone marrow transplant. Fed with semi-liquid diet supplemented with fully labeled glucose for 24 hours before harvest.", + "TREATMENT_PROTOCOL_ID": "allogenic;naive;syngenic", + "TREATMENT_PROTOCOL_FILENAME": "study_treatments.pdf;study_treatments.pdf;study_treatments.pdf" + }, + "SAMPLEPREP": { + "SAMPLEPREP_SUMMARY": "Tissue is frozen in liquid nitrogen to stop metabolic processes.;Frozen tissue is ground in a SPEX grinder under liquid nitrogen to homogenize the sample.;Polar extraction from homogenate, lypholized, and frozen.;Protein extraction and quantification.;Before going into the IC-FTMS the frozen sample is reconstituted in water.", + "SAMPLEPREP_PROTOCOL_ID": "tissue_quench;frozen_tissue_grind;polar_extraction;protein_extraction;IC-FTMS_preparation", + "SAMPLEPREP_PROTOCOL_FILENAME": "No tissue_quench file.;No frozen_tissue_grind file.;4B_Extract_Polar_Lipid_Prot_Fan_070417.pdf;['4D_17Jun4_Fan_Prot_Quant.pdf', '4B_Extract_Polar_Lipid_Prot_Fan_070417.pdf'];No IC-FTMS_preparation file." + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted IC", + "CHROMATOGRAPHY_TYPE": "Targeted IC", + "INSTRUMENT_NAME": "Thermo Dionex ICS-5000+", + "COLUMN_NAME": "Dionex IonPac AS11-HC-4um 2 mm i.d. x 250 mm" + }, + "MS": { + "INSTRUMENT_NAME": "Orbitrap Fusion", + "INSTRUMENT_TYPE": "IC-FTMS", + "MS_TYPE": "ESI", + "ION_MODE": "NEGATIVE", + "MS_COMMENTS": "ICMS Analytical Experiment with detection of compounds by comparison to standards. \nThermo RAW files are loaded into TraceFinder and peaks are manually curated. The area under the chromatograms is then exported to an Excel file. The area is then corrected for natural abundance. The natural abundance corrected area is then used to calculate the concentration of each compound for each sample. This calculation is done using standards. The first sample ran on the ICMS is a standard that has known concentrations of certain compounds. Then a number of samples are ran (typically 3-4) followed by another standard. The equation to calculate the concentration is \"intensity in sample\"/(\"intensity in first standard\" + ((\"intensity in second standard\" - \"intensity in first standard\")/# of samples) * \"known concentration in standard\", where the \"intensity\" is the aforementioned natural abundance corrected area, and the unlabeled intensity from the standard is used for all isotopologues of the compound. The reconstitution volume is simply the volume that the polar part of the sample was reconstituted to before going into the ICMS. The injection volume is how much of the reconstitution volume was injected into the ICMS. The protein is how much protein was in the entire sample (not only the small portion that was aliquoted for the ICMS). The polar split ratio is the fraction of the polar part of the sample that was aliquoted for the ICMS. This is calculated by dividing the weight of the polar aliquot for ICMS by the total weight of the polar portion of the sample. The protein normalized concentration is calculated using the equation, concentration * (reconstitution volume / 1000 / polar split ratio / protein)." + }, + "MS_METABOLITE_DATA": { + "Units": "natural abundance corrected and protein normalized peak area", + "Data": [ + { + "Metabolite": "(S)-2-Acetolactate Glutaric acid Methylsuccinic acid-13C0", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "13664945.509939667", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "6235697.006728272", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "12630053.331886068", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "4033821.6968634618", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "9518947.818154072", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "1280698.1009686766", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "4462132.172535896", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "1840075.8498431297", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "2245973.413528375", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "3244644.055941028", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "3647211.8925893256", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "1634213.3345670574", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "12555806.024456313", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "1591210.8487373295", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "1137971.7758424724" + }, + { + "Metabolite": "(S)-2-Acetolactate Glutaric acid Methylsuccinic acid-13C1", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "2380.9370001498482", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "19531.77093343344", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "42972.85144266533" + }, + { + "Metabolite": "(S)-2-Acetolactate Glutaric acid Methylsuccinic acid-13C2", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "56483.83305632133", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "24970.63620360221", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "102185.32948364424", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "36340.36068203385", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "25589.74463521666", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "13042.013326433487", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "30304.275451511676", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "4657.904421401434", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "24063.546980472755", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "22136.729162663938", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "28775.576152732556", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "10517.883388115499", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "33524.749397369276", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "49494.73237707745", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "33579.19522044789" + }, + { + "Metabolite": "(S)-2-Acetolactate Glutaric acid Methylsuccinic acid-13C3", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "8778.178664182", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "21159.506433441922", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "21558.930053947195", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "11800.936343727442", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "17000.149886995343", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "14215.111186104737", + 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"2815.5455928487863" + }, + { + "Metabolite": "1-Pyrroline 2-carboxylate 1-Pyrroline-5-carboxylate-13C0", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "32125.163975957093", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "9501.913587601925", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "211411.49438417438", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "19604.244421795018", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "25050.261524416506", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "37245.4398832103", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "86112.08464877213", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "587910.6970731171", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "264893.83979517943", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "52893.53837183994", + 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"61791.541041904275", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "50123.96928341658", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "77814.23910021782", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "37858.974230852196", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "61454.682084415515", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "40608.58205129394", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "94700.0281074869", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "69931.47813369849", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "48495.20980855114", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "78324.76124636238", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "51096.32168765752", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "110377.49728996522", + 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"06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "216.24191781504842", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "205.30023162061178", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "2929.7497743104514", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "1017.5497416896876" + }, + { + "Metabolite": "ADP-glucose ADP-Mannose GDP-D-Rhamnose GDP-L-fucose-13C2", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "326900.56730920565", + 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"13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "201827.779798994", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "20496.408758332862", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "28097.104193998675" + }, + { + "Metabolite": "ADP-glucose ADP-Mannose GDP-D-Rhamnose GDP-L-fucose-13C3", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "571176.6391289239", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "189830.3294737707", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "597413.4854391728", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "140914.35797310327", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "286100.7299954202", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "122861.10283660323", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "265876.1105102006", + 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"04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "406833.82270902663", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "618343.0019954607", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "426351.6156036913", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "488478.4291662862", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "465810.330555251", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "745104.0263499435", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "264159.3095530849", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "270082.5143135896", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "471318.27832926245", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "508363.42766598845", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "934968.9554131149", + 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"Metabolite": "Cytidine monophosphate N-acetylneuraminic acid-13C12", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "9854.260248963432", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "26487.567434956574", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "4711.332061178522", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "7623.192731803211", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "19432.72067289193", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "4077.0742616924877", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "7170.355184018286", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "3648.84760183624", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "27006.96894962279", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "6635.232107621374", + 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"01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "2658.3425486891247", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "26453.674030967235", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "598.4079687240657", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "2299.4223267441007", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "9855.502213318645", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "3942.1541104712123", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "6680.191605602182", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "1050.0227343791062", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "20466.623242907783", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "7011.819161083759", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "491.9871513775263", + 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"01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "62709.822786659126", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "97847.18581208486", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "76395.34448481652", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "65647.11641613263", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "113308.3134698465", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "25990.350000644965", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "66704.2043088819", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "57840.238875054776", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "72263.36087295583", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "63979.21880305394", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "48186.12042085349", + 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"15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0" + }, + { + "Metabolite": "NADPH-13C8", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0" + }, + { + "Metabolite": "NADPH-13C9", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0" + }, + { + "Metabolite": "Nicotinic acid From NADH-13C0", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "674904.1187554821", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "331782.6659439624", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "577062.8616623274", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "326269.8884621715", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "592961.8357794449", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "226869.92917055974", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "158747.10199025556", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "447776.08199098584", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "236603.3085088508", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "269713.6181525731", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "359256.5717224343", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "373710.9874178317", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "210202.84196068265", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "180210.4204044944", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "153827.03967116345" + }, + { + "Metabolite": "Nicotinic acid From NADH-13C1", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0" + }, + { + "Metabolite": "Nicotinic acid From NADH-13C2", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "60.80449355265492", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + 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"10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "585.2785788108306", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "756.8031902581355", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "176.85449831099757", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "525.5584672475541", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "432.421978422854", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "356.1868472994557" + }, + { + "Metabolite": "Nicotinic acid From NADH-13C4", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "761.6647316507639", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + 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"02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "465.0941135088126", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "507.32126004831827", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "609.4244035573655", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "203.64234221057683", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + 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"Metabolite": "Succinic acid semialdehyde 2-Methyl-3-oxopropanoic acid-13C1", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "11550.364399161914", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "76413.58345642847", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "29611.921717355424", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "12019.505705244988", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "30736.268218531317", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "14873.820701608369", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "19065.293371978445", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "110890.76183490187", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "75009.27724164315", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "23342.682626110272", + 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"01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "4698.638263422791", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "37798.439733645275", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "9097.226061364028", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "12822.764004334967", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "15644.691778327098", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "14155.192680523476", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "7894.857460811355", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "12265.096397551717", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "28001.43826002116", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "9289.63209746366", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "6698.036574149475", + 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"compound": "D-Galacturonate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucarate-13C0", + "assignment%method": "database", + "formula": "C6H10O8", + "compound": "D-Glucarate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucarate-13C1", + "assignment%method": "database", + "formula": "C6H10O8", + "compound": "D-Glucarate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucarate-13C2", + "assignment%method": "database", + "formula": "C6H10O8", + "compound": "D-Glucarate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucarate-13C3", + "assignment%method": "database", + "formula": "C6H10O8", + "compound": "D-Glucarate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucarate-13C4", + "assignment%method": "database", + "formula": "C6H10O8", + "compound": "D-Glucarate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucarate-13C5", + "assignment%method": "database", + "formula": "C6H10O8", + "compound": "D-Glucarate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucarate-13C6", + "assignment%method": "database", + "formula": "C6H10O8", + "compound": "D-Glucarate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C0", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C1", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C2", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C3", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C4", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C5", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C6", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 5-phosphate D-Ribulose 5-phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 5-phosphate D-Ribulose 5-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 5-phosphate D-Ribulose 5-phosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 5-phosphate D-Ribulose 5-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 5-phosphate D-Ribulose 5-phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 5-phosphate D-Ribulose 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"formula": "C17H21N4O9P1", + "compound": "Flavin Mononucleotide", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Flavin Mononucleotide-13C9", + "assignment%method": "database", + "formula": "C17H21N4O9P1", + "compound": "Flavin Mononucleotide", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-6-bisphosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H14O12P2", + "compound": "Fructose 1-6-bisphosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-6-bisphosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H14O12P2", + "compound": "Fructose 1-6-bisphosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-6-bisphosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H14O12P2", + "compound": "Fructose 1-6-bisphosphate", + "isotopologue": "13C2", + 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"compound": "Gamma Glutamylglutamic acid", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gamma Glutamylglutamic acid-13C8", + "assignment%method": "database", + "formula": "C10H16N2O7", + "compound": "Gamma Glutamylglutamic acid", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gamma Glutamylglutamic acid-13C9", + "assignment%method": "database", + "formula": "C10H16N2O7", + "compound": "Gamma Glutamylglutamic acid", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gluconic acid-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H12O7", + "compound": "Gluconic acid", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gluconic acid-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O7", + "compound": "Gluconic acid", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gluconic acid-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O7", + "compound": "Gluconic acid", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gluconic acid-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O7", + "compound": "Gluconic acid", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gluconic acid-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O7", + "compound": "Gluconic acid", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gluconic acid-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O7", + "compound": "Gluconic acid", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gluconic acid-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O7", + "compound": "Gluconic acid", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C0", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C1", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C2", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C3", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C4", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C5", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C6", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C0", + "assignment%method": "direct_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerol 3-phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C3H9O6P1", + "compound": "Glycerol 3-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerol 3-phosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C3H9O6P1", + "compound": "Glycerol 3-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerol 3-phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C3H9O6P1", + "compound": "Glycerol 3-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerol 3-phosphate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C3H9O6P1", + "compound": "Glycerol 3-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C0", + "assignment%method": "direct_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C0", + "assignment%method": "direct_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C11", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C11", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C12", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C12", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C13", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C13", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C14", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C14", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C15", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C15", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C16", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C16", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Homocysteinesulfinic acid-13C0", + "assignment%method": "database", + "formula": "C4H9N1O4S1", + "compound": "Homocysteinesulfinic acid", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Homocysteinesulfinic acid-13C1", + "assignment%method": "database", + "formula": "C4H9N1O4S1", + "compound": "Homocysteinesulfinic acid", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Homocysteinesulfinic acid-13C2", + "assignment%method": "database", + "formula": "C4H9N1O4S1", + "compound": "Homocysteinesulfinic acid", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Homocysteinesulfinic acid-13C3", + "assignment%method": "database", + "formula": "C4H9N1O4S1", + "compound": "Homocysteinesulfinic acid", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Homocysteinesulfinic acid-13C4", + "assignment%method": "database", + "formula": "C4H9N1O4S1", + "compound": "Homocysteinesulfinic acid", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hydroxypropanedioic acid-13C0", + "assignment%method": "database", + "formula": "C3H4O5", + "compound": "Hydroxypropanedioic acid", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hydroxypropanedioic acid-13C1", + "assignment%method": "database", + "formula": "C3H4O5", + "compound": "Hydroxypropanedioic acid", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hydroxypropanedioic acid-13C2", + "assignment%method": "database", + "formula": "C3H4O5", + "compound": "Hydroxypropanedioic acid", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hydroxypropanedioic acid-13C3", + "assignment%method": "database", + "formula": "C3H4O5", + "compound": "Hydroxypropanedioic acid", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C0", + "assignment%method": "direct_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C0", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C1", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C2", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C3", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C4", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C5", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C6", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C0", + "assignment%method": "direct_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C0", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C1", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C2", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C3", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C4", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C5", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C0", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C1", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C2", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C3", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C4", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C5", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C6", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C7", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C8", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C9", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Lactate-13C0", + "assignment%method": "direct_to_standard", + 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"isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C4H6O5", + "compound": "Malate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C4H6O5", + "compound": "Malate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C4H6O5", + "compound": "Malate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malonic acid-13C0", + "assignment%method": "direct_to_standard", + "formula": "C3H4O4", + "compound": "Malonic acid", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malonic acid-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C3H4O4", + "compound": "Malonic acid", + "isotopologue": "13C1", + 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b/tests/example_data/other_mwtab_files/incorrect_section_order.json new file mode 100644 index 0000000..deb891c --- /dev/null +++ b/tests/example_data/other_mwtab_files/incorrect_section_order.json @@ -0,0 +1,271883 @@ +{ + "ANALYSIS": { + "ANALYSIS_TYPE": "MS" + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted IC", + "CHROMATOGRAPHY_TYPE": "Targeted IC", + "COLUMN_NAME": "Dionex IonPac AS11-HC-4um 2 mm i.d. x 250 mm", + "INSTRUMENT_NAME": "Thermo Dionex ICS-5000+" + }, + "COLLECTION": { + "COLLECTION_PROTOCOL_FILENAME": "mouse_tissue_procedure.pdf", + "COLLECTION_PROTOCOL_ID": "mouse_tissue_collection", + "COLLECTION_SUMMARY": "Mouse is sacrificed and tissues are harvested.", + "SAMPLE_TYPE": "mouse" + }, + "METABOLOMICS WORKBENCH": { + "ANALYSIS_ID": "AN000000", + "CREATED_ON": "2023-05-03", + "STUDY_ID": "ST000000", + "VERSION": "1" + }, + "MS": { + "INSTRUMENT_NAME": "Orbitrap Fusion", + "INSTRUMENT_TYPE": "IC-FTMS", + "ION_MODE": "NEGATIVE", + "MS_COMMENTS": "ICMS Analytical Experiment with detection of compounds by comparison to standards. \nThermo RAW files are loaded into TraceFinder and peaks are manually curated. The area under the chromatograms is then exported to an Excel file. The area is then corrected for natural abundance. The natural abundance corrected area is then used to calculate the concentration of each compound for each sample. This calculation is done using standards. The first sample ran on the ICMS is a standard that has known concentrations of certain compounds. Then a number of samples are ran (typically 3-4) followed by another standard. The equation to calculate the concentration is \"intensity in sample\"/(\"intensity in first standard\" + ((\"intensity in second standard\" - \"intensity in first standard\")/# of samples) * \"known concentration in standard\", where the \"intensity\" is the aforementioned natural abundance corrected area, and the unlabeled intensity from the standard is used for all isotopologues of the compound. The reconstitution volume is simply the volume that the polar part of the sample was reconstituted to before going into the ICMS. The injection volume is how much of the reconstitution volume was injected into the ICMS. The protein is how much protein was in the entire sample (not only the small portion that was aliquoted for the ICMS). The polar split ratio is the fraction of the polar part of the sample that was aliquoted for the ICMS. This is calculated by dividing the weight of the polar aliquot for ICMS by the total weight of the polar portion of the sample. The protein normalized concentration is calculated using the equation, concentration * (reconstitution volume / 1000 / polar split ratio / protein).", + "MS_TYPE": "ESI" + }, + "MS_METABOLITE_DATA": { + "Data": [ + { + "Metabolite": "(S)-2-Acetolactate Glutaric acid Methylsuccinic acid-13C0", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "13664945.509939667", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "6235697.006728272", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "12630053.331886068", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "4033821.6968634618", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "9518947.818154072", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "1280698.1009686766", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "4462132.172535896", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "1840075.8498431297", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "2245973.413528375", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "3244644.055941028", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "3647211.8925893256", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "1634213.3345670574", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "12555806.024456313", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "1591210.8487373295", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "1137971.7758424724" + }, + { + "Metabolite": "(S)-2-Acetolactate Glutaric acid Methylsuccinic acid-13C1", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "2380.9370001498482", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "19531.77093343344", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "42972.85144266533" + }, + { + "Metabolite": "(S)-2-Acetolactate Glutaric acid Methylsuccinic acid-13C2", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "56483.83305632133", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "24970.63620360221", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "102185.32948364424", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "36340.36068203385", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "25589.74463521666", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "13042.013326433487", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "30304.275451511676", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "4657.904421401434", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "24063.546980472755", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "22136.729162663938", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "28775.576152732556", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "10517.883388115499", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "33524.749397369276", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "49494.73237707745", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "33579.19522044789" + }, + { + "Metabolite": "(S)-2-Acetolactate Glutaric acid Methylsuccinic acid-13C3", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "8778.178664182", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "21159.506433441922", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "21558.930053947195", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "11800.936343727442", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "17000.149886995343", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "14215.111186104737", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "9654.405727560417", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "1827.5908616973702", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "11077.017212695326", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "4971.5910749061595", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "5202.379323779814", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "14115.766028469214", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "15851.624950880067", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "11602.804769592007", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "14911.036748638027" + }, + { + "Metabolite": "(S)-2-Acetolactate Glutaric acid Methylsuccinic acid-13C4", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "3136.7189408707627", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "8259.812972912872", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "5780.422379694323", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "3435.7519805269335", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "7105.81374973048", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "3801.24637004448", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "4347.12723407788", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "195.04517298945692", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "4085.6787510465874", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "3021.4382070425127", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "741.1838124820921", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "5249.880617077326", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "8476.743846846235", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "13000.664392110571", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "8119.204973529047" + }, + { + "Metabolite": "(S)-2-Acetolactate Glutaric acid Methylsuccinic acid-13C5", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "613.0307766752976", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "6663.195343272522", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "9442.931396402326", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "6954.689402767319", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "10313.133887665355", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "4508.950458912953", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "4567.709687296376", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "2022.08828110111", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "1948.175634689172", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "1384.7003488733583", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "1865.4845801211245", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "2525.863295591396", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "3373.5872486353824", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "7280.957223516158", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "4540.822065646762" + }, + { + "Metabolite": "(S)-3-Sulfonatolactate-13C0", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "2047989.2810557943", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "276956.43664027477", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "711675.9588402916", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "505429.92842368217", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "414730.8968976259", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "382793.69021065463", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "250459.69388595122", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "52983.02076680998", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "556376.7616195839", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "456341.30232297955", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "281310.5105196696", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "128603.32246627547", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "171286.0937989314", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "249452.4449985231", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "84342.90137961706" + }, + { + "Metabolite": "(S)-3-Sulfonatolactate-13C1", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "3498.9830684751378", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "7915.248818864551", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "674.5927716975568", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "1760.4952754345393", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "829.0121328099902" + }, + { + "Metabolite": "(S)-3-Sulfonatolactate-13C2", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "7699.9209470813885", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "2244.0214386408497", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "3397.575886691656", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": 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"02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "38625.036400940044", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "12469.014508644936", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "6811.870554895531", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "23770.00150746519", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "13604.527178963845", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "17128.3146223584", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "5161.915971929171", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "50419.19739997332", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "8338.679897840933", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "4433.300796109027", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "71494.22288765636", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "13987.154915396171", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "465530.63165193546", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "189001.29418077494" + }, + { + "Metabolite": "ADP-glucose ADP-Mannose GDP-D-Rhamnose GDP-L-fucose-13C13", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "7443.376134287492", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "16190.769869182852", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "3941.5418113762544", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "3618.5952705747436", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "8620.390720034016", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "4734.386678869893", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "3023.4628364028063", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "1067.9126127094823", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "18549.166695179043", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "3966.5535575789054", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "627.8591131345764", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "26120.89285878843", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "3994.8860792525916", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "294506.7698917975", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "95242.20095725566" + }, + { + "Metabolite": "ADP-glucose ADP-Mannose GDP-D-Rhamnose GDP-L-fucose-13C14", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "5858.061104176667", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "1556.378582233704", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "460.7986608720796", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "1314.3543427827306", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "1827.4105350194034", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "796.8685528403665", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "7933.979753976766", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "2735.695099601288", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "395.8277999544266", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "6136.717840674922", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "992.0321381653532", + 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"10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "239.7962198135772", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "2959.279411900348", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "204.4477899050132", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "29714.92642644853", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "8414.87167264682" + }, + { + "Metabolite": "ADP-glucose ADP-Mannose GDP-D-Rhamnose GDP-L-fucose-13C16", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "216.24191781504842", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "205.30023162061178", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "2929.7497743104514", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "1017.5497416896876" + }, + { + "Metabolite": "ADP-glucose ADP-Mannose GDP-D-Rhamnose GDP-L-fucose-13C2", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "326900.56730920565", + 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"13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "201827.779798994", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "20496.408758332862", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "28097.104193998675" + }, + { + "Metabolite": "ADP-glucose ADP-Mannose GDP-D-Rhamnose GDP-L-fucose-13C3", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "571176.6391289239", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "189830.3294737707", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "597413.4854391728", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "140914.35797310327", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "286100.7299954202", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "122861.10283660323", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "265876.1105102006", + 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"04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "406833.82270902663", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "618343.0019954607", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "426351.6156036913", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "488478.4291662862", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "465810.330555251", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "745104.0263499435", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "264159.3095530849", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "270082.5143135896", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "471318.27832926245", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "508363.42766598845", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "934968.9554131149", + 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"Metabolite": "ADP-glucose ADP-Mannose GDP-D-Rhamnose GDP-L-fucose-13C9", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "60772.629578843", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "120009.45815881563", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "47995.907821498884", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "47987.268629228536", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "129043.70722717713", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "51536.13059533161", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "79784.0838948045", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "49366.29889232957", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "167996.9909868639", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "53626.81092627329", + 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"168421.22762227265", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "74511.52485791994", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "35223.39311011144", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "35358.97613878362", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "148477.62130107495", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "271047.45322319487", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "156302.48663185714", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "118092.4390803939", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "33278.72393364121", + "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "37285.79031121838" + }, + { + "Metabolite": "ADP-ribose 2-phosphate-13C1", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + 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"04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "14148.093633296921", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "23627.481094134266", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "3304.504269764916", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "12738.105078593171", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "6771.658511754311", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "13701.881982588046", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "14442.246049656584", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "16723.411846590454", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "16907.003774656827", + "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "22992.816180529146", + "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "50776.72527886388", + 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"15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0" + }, + { + "Metabolite": "Cytidine monophosphate N-acetylneuraminic acid-13C10", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "25970.32437023087", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "72565.95218447034", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "14449.108074230295", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "17118.024667563168", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "43639.32404177434", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "20676.982875194364", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "19568.284768225385", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "10329.75110627158", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "37551.889650049794", + 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"Metabolite": "Cytidine monophosphate N-acetylneuraminic acid-13C12", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "9854.260248963432", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "26487.567434956574", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "4711.332061178522", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "7623.192731803211", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "19432.72067289193", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "4077.0742616924877", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "7170.355184018286", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "3648.84760183624", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "27006.96894962279", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "6635.232107621374", + 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"01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "2658.3425486891247", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "26453.674030967235", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "598.4079687240657", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "2299.4223267441007", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "9855.502213318645", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "3942.1541104712123", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "6680.191605602182", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "1050.0227343791062", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "20466.623242907783", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "7011.819161083759", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "491.9871513775263", + 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acid-13C19", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "1047.10336052121", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "928.0896303802327", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "0.0", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "505.556665977739", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "0.0", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "0.0", + "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A": "1546.2367923339534", + 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"01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "62709.822786659126", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "97847.18581208486", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "76395.34448481652", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "65647.11641613263", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "113308.3134698465", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "25990.350000644965", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "66704.2043088819", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "57840.238875054776", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "72263.36087295583", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "63979.21880305394", + "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A": "48186.12042085349", + 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"Metabolite": "Succinic acid semialdehyde 2-Methyl-3-oxopropanoic acid-13C1", + "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A": "11550.364399161914", + "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A": "76413.58345642847", + "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A": "29611.921717355424", + "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "12019.505705244988", + "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "30736.268218531317", + "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A": "14873.820701608369", + "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "19065.293371978445", + "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A": "110890.76183490187", + "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A": "75009.27724164315", + "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A": "23342.682626110272", + 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{ + "Metabolite": "D-Glucuronate 1-phosphate-13C0", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C1", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C2", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C3", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C4", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C5", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Glucuronate 1-phosphate-13C6", + "assignment%method": "database", + "formula": "C6H11O10P1", + "compound": "D-Glucuronate 1-phosphate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 1-phosphate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 1-phosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 5-phosphate D-Ribulose 5-phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 5-phosphate D-Ribulose 5-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 5-phosphate D-Ribulose 5-phosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 5-phosphate D-Ribulose 5-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 5-phosphate D-Ribulose 5-phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 5-phosphate D-Ribulose 5-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 5-phosphate D-Ribulose 5-phosphate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C5H11O8P1", + "compound": "D-Ribose 5-phosphate D-Ribulose 5-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "D-Ribose 5-phosphate D-Ribulose 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"formula": "C6H14O12P2", + "compound": "Fructose 1-6-bisphosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-6-bisphosphate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H14O12P2", + "compound": "Fructose 1-6-bisphosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-6-bisphosphate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H14O12P2", + "compound": "Fructose 1-6-bisphosphate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-phosphate-13C0", + "assignment%method": "database", + "formula": "C6H13O9P1", + "compound": "Fructose 1-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-phosphate-13C1", + "assignment%method": "database", + "formula": "C6H13O9P1", + "compound": "Fructose 1-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-phosphate-13C2", + "assignment%method": "database", + "formula": "C6H13O9P1", + "compound": "Fructose 1-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-phosphate-13C3", + "assignment%method": "database", + "formula": "C6H13O9P1", + "compound": "Fructose 1-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-phosphate-13C4", + "assignment%method": "database", + "formula": "C6H13O9P1", + "compound": "Fructose 1-phosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-phosphate-13C5", + "assignment%method": "database", + "formula": "C6H13O9P1", + "compound": "Fructose 1-phosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Fructose 1-phosphate-13C6", + "assignment%method": "database", + "formula": "C6H13O9P1", + "compound": "Fructose 1-phosphate", + 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"isotopologue%type": "13C" + }, + { + "Metabolite": "Gluconic acid-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O7", + "compound": "Gluconic acid", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gluconic acid-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O7", + "compound": "Gluconic acid", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Gluconic acid-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O7", + "compound": "Gluconic acid", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C0", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C1", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C2", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C3", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C4", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C5", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-6-diphosphate-13C6", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Glucose 1-6-diphosphate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucose 1-phosphate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Glucose 1-phosphate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glucuronate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H10O7", + "compound": "Glucuronate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutamate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C5H9N1O4", + "compound": "Glutamate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C0", + "assignment%method": "direct_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glutathione-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C10H17N3O6S1", + "compound": "Glutathione", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerol 3-phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C3H9O6P1", + "compound": "Glycerol 3-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerol 3-phosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C3H9O6P1", + "compound": "Glycerol 3-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerol 3-phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C3H9O6P1", + "compound": "Glycerol 3-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerol 3-phosphate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C3H9O6P1", + "compound": "Glycerol 3-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C0", + "assignment%method": "direct_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerophosphocholine-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C8H20N1O6P1", + "compound": "Glycerophosphocholine", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Glycerylphosphorylethanolamine-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C5H14N1O6P1", + "compound": "Glycerylphosphorylethanolamine", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C0", + "assignment%method": "direct_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C11", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C11", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C12", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C12", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C13", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C13", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C14", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C14", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C15", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C15", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C16", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C16", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Guanosine diphosphate mannose-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C16H25N5O16P2", + "compound": "Guanosine diphosphate mannose", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hexoses-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H12O6", + "compound": "Hexoses", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Homocysteinesulfinic acid-13C0", + "assignment%method": "database", + "formula": "C4H9N1O4S1", + "compound": "Homocysteinesulfinic acid", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Homocysteinesulfinic acid-13C1", + "assignment%method": "database", + "formula": "C4H9N1O4S1", + "compound": "Homocysteinesulfinic acid", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Homocysteinesulfinic acid-13C2", + "assignment%method": "database", + "formula": "C4H9N1O4S1", + "compound": "Homocysteinesulfinic acid", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Homocysteinesulfinic acid-13C3", + "assignment%method": "database", + "formula": "C4H9N1O4S1", + "compound": "Homocysteinesulfinic acid", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Homocysteinesulfinic acid-13C4", + "assignment%method": "database", + "formula": "C4H9N1O4S1", + "compound": "Homocysteinesulfinic acid", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hydroxypropanedioic acid-13C0", + "assignment%method": "database", + "formula": "C3H4O5", + "compound": "Hydroxypropanedioic acid", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hydroxypropanedioic acid-13C1", + "assignment%method": "database", + "formula": "C3H4O5", + "compound": "Hydroxypropanedioic acid", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hydroxypropanedioic acid-13C2", + "assignment%method": "database", + "formula": "C3H4O5", + "compound": "Hydroxypropanedioic acid", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Hydroxypropanedioic acid-13C3", + "assignment%method": "database", + "formula": "C3H4O5", + "compound": "Hydroxypropanedioic acid", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C0", + "assignment%method": "direct_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "IMP-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C10H13N4O8P1", + "compound": "IMP", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C0", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C1", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C2", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C3", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C4", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C5", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ino145-trisP Ino134-trisP@mz Ino-bisP-13C6", + "assignment%method": "database", + "formula": "C6H14O12P2", + "compound": "Ino145-trisP Ino134-trisP@mz Ino-bisP", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C0", + "assignment%method": "direct_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Inosine-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C10H12N4O5", + "compound": "Inosine", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Isocitrate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H8O7", + "compound": "Isocitrate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Itaconate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O4", + "compound": "Itaconate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C0", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C1", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C2", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C3", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C4", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-4-Hydroxyglutamate semialdehyde-13C5", + "assignment%method": "database", + "formula": "C5H9N1O4", + "compound": "L-4-Hydroxyglutamate semialdehyde", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C0", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C1", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C2", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C3", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C4", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C5", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C6", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C7", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C8", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "L-beta-aspartyl-L-glutamic acid -13C9", + "assignment%method": "database", + "formula": "C9H14N2O7", + "compound": "L-beta-aspartyl-L-glutamic acid", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Lactate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C3H6O3", + "compound": "Lactate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Lactate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C3H6O3", + "compound": "Lactate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Lactate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C3H6O3", + "compound": "Lactate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Lactate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C3H6O3", + "compound": "Lactate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C4H6O5", + "compound": "Malate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C4H6O5", + "compound": "Malate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C4H6O5", + "compound": "Malate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C4H6O5", + "compound": "Malate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C4H6O5", + "compound": "Malate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malonic acid-13C0", + "assignment%method": "direct_to_standard", + "formula": "C3H4O4", + "compound": "Malonic acid", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malonic acid-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C3H4O4", + "compound": "Malonic acid", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malonic acid-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C3H4O4", + "compound": "Malonic acid", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Malonic acid-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C3H4O4", + "compound": "Malonic acid", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Mannose 6-phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H13O9P1", + "compound": "Mannose 6-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Mannose 6-phosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Mannose 6-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Mannose 6-phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Mannose 6-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Mannose 6-phosphate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Mannose 6-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Mannose 6-phosphate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Mannose 6-phosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Mannose 6-phosphate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Mannose 6-phosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Mannose 6-phosphate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H13O9P1", + "compound": "Mannose 6-phosphate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Methylmalonic acid-13C0", + "assignment%method": "direct_to_standard", + "formula": "C4H6O4", + "compound": "Methylmalonic acid", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Methylmalonic acid-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C4H6O4", + "compound": "Methylmalonic acid", + "isotopologue": "13C1", + 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"assignment%method": "indirect_to_standard", + "formula": "C8H16N1O9P1", + "compound": "N-Acetyl-D-Glucosamine 6-Phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "N-Acetyl-D-Glucosamine 6-Phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C8H16N1O9P1", + "compound": "N-Acetyl-D-Glucosamine 6-Phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "N-Acetyl-D-Glucosamine 6-Phosphate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C8H16N1O9P1", + "compound": "N-Acetyl-D-Glucosamine 6-Phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "N-Acetyl-D-Glucosamine 6-Phosphate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C8H16N1O9P1", + "compound": "N-Acetyl-D-Glucosamine 6-Phosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "N-Acetyl-D-Glucosamine 6-Phosphate-13C5", + 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"isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pantothenate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C9H17N1O5", + "compound": "Pantothenate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pantothenate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C9H17N1O5", + "compound": "Pantothenate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pantothenate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C9H17N1O5", + "compound": "Pantothenate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pantothenate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C9H17N1O5", + "compound": "Pantothenate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pantothenate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C9H17N1O5", + "compound": "Pantothenate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pantothenate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C9H17N1O5", + "compound": "Pantothenate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pantothenate-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C9H17N1O5", + "compound": "Pantothenate", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pantothenate-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C9H17N1O5", + "compound": "Pantothenate", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pantothenate-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C9H17N1O5", + "compound": "Pantothenate", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "H3O4P1", + "compound": "Phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C0", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C1", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C10", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C2", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C3", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C4", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C5", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C6", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C7", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C8", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphoadenosine phosphosulfate-13C9", + "assignment%method": "database", + "formula": "C10H15N5O13P2S1", + "compound": "Phosphoadenosine phosphosulfate", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphocreatine-13C0", + "assignment%method": "direct_to_standard", + "formula": "C4H10N3O5P1", + "compound": "Phosphocreatine", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphocreatine-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C4H10N3O5P1", + "compound": "Phosphocreatine", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphocreatine-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C4H10N3O5P1", + "compound": "Phosphocreatine", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphocreatine-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C4H10N3O5P1", + "compound": "Phosphocreatine", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Phosphocreatine-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C4H10N3O5P1", + "compound": "Phosphocreatine", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pyridoxal 5-phosphate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C8H10N1O6P1", + "compound": "Pyridoxal 5-phosphate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pyridoxal 5-phosphate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C8H10N1O6P1", + "compound": "Pyridoxal 5-phosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pyridoxal 5-phosphate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C8H10N1O6P1", + "compound": "Pyridoxal 5-phosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pyridoxal 5-phosphate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C8H10N1O6P1", + "compound": "Pyridoxal 5-phosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pyridoxal 5-phosphate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C8H10N1O6P1", + "compound": "Pyridoxal 5-phosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pyridoxal 5-phosphate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C8H10N1O6P1", + "compound": "Pyridoxal 5-phosphate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pyridoxal 5-phosphate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C8H10N1O6P1", + "compound": "Pyridoxal 5-phosphate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pyridoxal 5-phosphate-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C8H10N1O6P1", + "compound": "Pyridoxal 5-phosphate", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pyridoxal 5-phosphate-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C8H10N1O6P1", + "compound": "Pyridoxal 5-phosphate", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Pyruvate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C3H4O3", + "compound": "Pyruvate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + 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"Metabolite": "Riboflavin cyclic-4-5-phosphate-13C14", + "assignment%method": "database", + "formula": "C17H19N4O8P1", + "compound": "Riboflavin cyclic-4-5-phosphate", + "isotopologue": "13C14", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Riboflavin cyclic-4-5-phosphate-13C15", + "assignment%method": "database", + "formula": "C17H19N4O8P1", + "compound": "Riboflavin cyclic-4-5-phosphate", + "isotopologue": "13C15", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Riboflavin cyclic-4-5-phosphate-13C16", + "assignment%method": "database", + "formula": "C17H19N4O8P1", + "compound": "Riboflavin cyclic-4-5-phosphate", + "isotopologue": "13C16", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Riboflavin cyclic-4-5-phosphate-13C17", + "assignment%method": "database", + "formula": "C17H19N4O8P1", + "compound": "Riboflavin cyclic-4-5-phosphate", + "isotopologue": "13C17", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Riboflavin cyclic-4-5-phosphate-13C2", + 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"isotopologue%type": "13C" + }, + { + "Metabolite": "Ribose 1-5-bisphosphate-13C1", + "assignment%method": "database", + "formula": "C5H12O11P2", + "compound": "Ribose 1-5-bisphosphate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ribose 1-5-bisphosphate-13C2", + "assignment%method": "database", + "formula": "C5H12O11P2", + "compound": "Ribose 1-5-bisphosphate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ribose 1-5-bisphosphate-13C3", + "assignment%method": "database", + "formula": "C5H12O11P2", + "compound": "Ribose 1-5-bisphosphate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ribose 1-5-bisphosphate-13C4", + "assignment%method": "database", + "formula": "C5H12O11P2", + "compound": "Ribose 1-5-bisphosphate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Ribose 1-5-bisphosphate-13C5", + "assignment%method": "database", + "formula": 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"compound": "UMP", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UMP-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C9H13N2O9P1", + "compound": "UMP", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UTP@mzUDP-13C0", + "assignment%method": "direct_to_standard", + "formula": "C9H14N2O12P2", + "compound": "UTP@mzUDP", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UTP@mzUDP-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C9H14N2O12P2", + "compound": "UTP@mzUDP", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UTP@mzUDP-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C9H14N2O12P2", + "compound": "UTP@mzUDP", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UTP@mzUDP-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C9H14N2O12P2", + "compound": "UTP@mzUDP", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UTP@mzUDP-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C9H14N2O12P2", + "compound": "UTP@mzUDP", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UTP@mzUDP-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C9H14N2O12P2", + "compound": "UTP@mzUDP", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UTP@mzUDP-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C9H14N2O12P2", + "compound": "UTP@mzUDP", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UTP@mzUDP-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C9H14N2O12P2", + "compound": "UTP@mzUDP", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UTP@mzUDP-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C9H14N2O12P2", + "compound": "UTP@mzUDP", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "UTP@mzUDP-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C9H14N2O12P2", + "compound": "UTP@mzUDP", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphate glucose-13C0", + "assignment%method": "direct_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphate glucose", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphate glucose-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphate glucose", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphate glucose-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphate glucose", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, 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"indirect_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphate glucose", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphate glucose-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphate glucose", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphate glucose-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphate glucose", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphate glucose-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphate glucose", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphate glucose-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphate glucose", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphategalactose-13C0", + "assignment%method": "direct_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphategalactose", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphategalactose-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphategalactose", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphategalactose-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine diphosphategalactose", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine diphosphategalactose-13C11", + "assignment%method": "indirect_to_standard", + "formula": "C15H24N2O17P2", + "compound": "Uridine 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"Metabolite": "Uridine-13C0", + "assignment%method": "direct_to_standard", + "formula": "C9H12N2O6", + "compound": "Uridine", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C9H12N2O6", + "compound": "Uridine", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C9H12N2O6", + "compound": "Uridine", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C9H12N2O6", + "compound": "Uridine", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C9H12N2O6", + "compound": "Uridine", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C9H12N2O6", + "compound": "Uridine", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C9H12N2O6", + "compound": "Uridine", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C9H12N2O6", + "compound": "Uridine", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C9H12N2O6", + "compound": "Uridine", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "Uridine-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C9H12N2O6", + "compound": "Uridine", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C0", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C1", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C10", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C2", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C3", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C4", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C5", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C6", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C7", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C8", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "XMP-13C9", + "assignment%method": "database", + "formula": "C10H13N4O9P1", + "compound": "XMP", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "alpha-Ketoglutarate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C5H6O5", + "compound": "alpha-Ketoglutarate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "alpha-Ketoglutarate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O5", + "compound": "alpha-Ketoglutarate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "alpha-Ketoglutarate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O5", + "compound": "alpha-Ketoglutarate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "alpha-Ketoglutarate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O5", + "compound": "alpha-Ketoglutarate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "alpha-Ketoglutarate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O5", + "compound": "alpha-Ketoglutarate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "alpha-Ketoglutarate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C5H6O5", + "compound": "alpha-Ketoglutarate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "cis-Aconitate-13C0", + "assignment%method": "direct_to_standard", + "formula": "C6H6O6", + "compound": "cis-Aconitate", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "cis-Aconitate-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C6H6O6", + "compound": "cis-Aconitate", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "cis-Aconitate-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C6H6O6", + "compound": "cis-Aconitate", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "cis-Aconitate-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C6H6O6", + "compound": "cis-Aconitate", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "cis-Aconitate-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C6H6O6", + "compound": "cis-Aconitate", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "cis-Aconitate-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C6H6O6", + "compound": "cis-Aconitate", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "cis-Aconitate-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C6H6O6", + "compound": "cis-Aconitate", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C0", + "assignment%method": "direct_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTDP-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C10H16N2O11P2", + "compound": "dTDP", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C0", + "assignment%method": "direct_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C10", + "assignment%method": "indirect_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C10", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dTMP-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C10H15N2O8P1", + "compound": "dTMP", + "isotopologue": "13C9", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dUMP-13C0", + "assignment%method": "direct_to_standard", + "formula": "C9H13N2O8P1", + "compound": "dUMP", + "isotopologue": "13C0", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dUMP-13C1", + "assignment%method": "indirect_to_standard", + "formula": "C9H13N2O8P1", + "compound": "dUMP", + "isotopologue": "13C1", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dUMP-13C2", + "assignment%method": "indirect_to_standard", + "formula": "C9H13N2O8P1", + "compound": "dUMP", + "isotopologue": "13C2", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dUMP-13C3", + "assignment%method": "indirect_to_standard", + "formula": "C9H13N2O8P1", + "compound": "dUMP", + "isotopologue": "13C3", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dUMP-13C4", + "assignment%method": "indirect_to_standard", + "formula": "C9H13N2O8P1", + "compound": "dUMP", + "isotopologue": "13C4", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dUMP-13C5", + "assignment%method": "indirect_to_standard", + "formula": "C9H13N2O8P1", + "compound": "dUMP", + "isotopologue": "13C5", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dUMP-13C6", + "assignment%method": "indirect_to_standard", + "formula": "C9H13N2O8P1", + "compound": "dUMP", + "isotopologue": "13C6", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dUMP-13C7", + "assignment%method": "indirect_to_standard", + "formula": "C9H13N2O8P1", + "compound": "dUMP", + "isotopologue": "13C7", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dUMP-13C8", + "assignment%method": "indirect_to_standard", + "formula": "C9H13N2O8P1", + "compound": "dUMP", + "isotopologue": "13C8", + "isotopologue%type": "13C" + }, + { + "Metabolite": "dUMP-13C9", + "assignment%method": "indirect_to_standard", + "formula": "C9H13N2O8P1", + "compound": "dUMP", + "isotopologue": "13C9", + "isotopologue%type": "13C" + } + ], + "Units": "natural abundance corrected and protein normalized peak area" + }, + "PROJECT": { + "ADDRESS": "Gerhard C. Hildebrandt, MD, Room no. CC401A, Ben Roach Building, Markey Cancer Center University of Kentucky, Lexington, 40536", + "DEPARTMENT": "Markey Cancer Center, Hematology and Blood and Marrow Transplant", + "EMAIL": "gerhard.hildebrandt@uky.edu", + "FIRST_NAME": "Gerhard", + "INSTITUTE": "University of Kentucky", + "LAST_NAME": "Hildebrandt", + "PHONE": "800-333-8874", + "PROJECT_SUMMARY": "Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment option for a variety of hematological malignancies. Interactions between the donor immune system and the patient tissue result in a disease, called GVHD. The pathophysiology of acute GVHD can be hypothesized in three sequential phases: cytokine storm and activation of the antigen-presenting cells (APC), donor T cell activation and effector cell phase. Idiopathic pneumonia syndrome (IPS) is one of the most deleterious complications after allogeneic HCT and is considered not only to be related to conditioning regimen toxicity but also represents an end organ damage caused by allo-reactive T cells, therefore making the lung susceptible to a two-pronged attack, one of which overlaps with GVHD causing other target organ injury. IPS results in mortality of up to 90% of patients. We will use a murine model of IPS and GVHD which is well established in our group, and in which disease evolves either across disparities in major histocompatibility complex (MCH) class I and II, minor histocompatibility antigens (miHags) or both. Metabolomics changes following syngeneic and allogeneic HCT at post-transplantation Days +7 (cytokine storm phase) and Days +42 (cellular effector phase) are compared to baseline wild-type (naive) controls. Prior to analysis, na\u00efve - and experimental mice (N=3 from each group) were fed with semi-liquid diet supplemented with tracers (13C6-glucose ) over 24 hours. At the end of 7 days or 42 days, respectively, feces and aGVHD target organs (colon, liver and lung) were collected from all groups and further processed and / or analyzed. We expect to reveal metabolic pathways affected after allo-HCT which contribute to immune cell mediated lung injury (IPS) and will potentially identify different metabolic pathways in other GVHD target organs.", + "PROJECT_TITLE": "Metabolomics of lung injury after allogeneic hematopoietic cell transplantation" + }, + "SAMPLEPREP": { + "SAMPLEPREP_PROTOCOL_FILENAME": "No tissue_quench file.;No frozen_tissue_grind file.;4B_Extract_Polar_Lipid_Prot_Fan_070417.pdf;['4D_17Jun4_Fan_Prot_Quant.pdf', '4B_Extract_Polar_Lipid_Prot_Fan_070417.pdf'];No IC-FTMS_preparation file.", + "SAMPLEPREP_PROTOCOL_ID": "tissue_quench;frozen_tissue_grind;polar_extraction;protein_extraction;IC-FTMS_preparation", + "SAMPLEPREP_SUMMARY": "Tissue is frozen in liquid nitrogen to stop metabolic processes.;Frozen tissue is ground in a SPEX grinder under liquid nitrogen to homogenize the sample.;Polar extraction from homogenate, lypholized, and frozen.;Protein extraction and quantification.;Before going into the IC-FTMS the frozen sample is reconstituted in water." + }, + "STUDY": { + "ADDRESS": "CTW-453, 900 South Limestone street. UKY. Lexington, Kentucky-40536", + "DEPARTMENT": "MCC", + "EMAIL": "gerhard.hildebrandt@uky.edu", + "FIRST_NAME": "Gerhard", + "INSTITUTE": "University of Kentucky", + "LAST_NAME": "Hildebrandt", + "PHONE": "800-333-8874", + "STUDY_SUMMARY": "Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment option for a variety of hematological malignancies. Interactions between the donor immune system and the patient tissue result in a disease, called GVHD. The pathophysiology of acute GVHD can be hypothesized in three sequential phases: cytokine storm and activation of the antigen-presenting cells (APC), donor T cell activation and effector cell phase. Idiopathic pneumonia syndrome (IPS) is one of the most deleterious complications after allogeneic HCT and is considered not only to be related to conditioning regimen toxicity but also represents an end organ damage caused by allo-reactive T cells, therefore making the lung susceptible to a two-pronged attack, one of which overlaps with GVHD causing other target organ injury. IPS results in mortality of up to 90% of patients. We will use a murine model of IPS and GVHD which is well established in our group, and in which disease evolves either across disparities in major histocompatibility complex (MCH) class I and II, minor histocompatibility antigens (miHags) or both. Metabolomics changes following syngeneic and allogeneic HCT at post-transplantation Days +7 (cytokine storm phase) and Days +42 (cellular effector phase) are compared to baseline wild-type (naive) controls. Prior to analysis, na\u00efve - and experimental mice (N=3 from each group) were fed with semi-liquid diet supplemented with tracers (13C6-glucose ) over 24 hours. At the end of 7 days or 42 days, respectively, feces and aGVHD target organs (colon, liver and lung) were collected from all groups and further processed and / or analyzed. We expect to reveal metabolic pathways affected after allo-HCT which contribute to immune cell mediated lung injury (IPS) and will potentially identify different metabolic pathways in other GVHD target organs.", + "STUDY_TITLE": "Metabolomics of lung injury after allogeneic hematopoietic cell transplantation" + }, + "SUBJECT": { + "SUBJECT_SPECIES": "Mus musculus", + "SUBJECT_TYPE": "Mouse", + "TAXONOMY_ID": "10090" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "01_A0_naive_0days_UKy_GCH_rep1", + "Sample ID": "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "naive", + "Time Point": "0" + }, + "Additional sample data": { + "lineage0_id": "01_A0_naive_0days_UKy_GCH_rep1", + "lineage0_protocol.id": "['naive']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "0", + "lineage0_type": "subject", + "lineage1_id": "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "01_A0_Colon_naive_0days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "0.6181768442446792", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "01_A0_Colon_naive_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "02_A1_naive_0days_UKy_GCH_rep2", + "Sample ID": "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-polar-ICMS_A", + "Factors": { + "Treatment": "naive", + "Time Point": "0" + }, + "Additional sample data": { + "lineage0_id": "02_A1_naive_0days_UKy_GCH_rep2", + "lineage0_protocol.id": "['naive']", + "lineage0_replicate": "2", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "0", + "lineage0_type": "subject", + "lineage1_id": "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "02_A1_Colon_naive_0days_170427_UKy_GCH_rep2-protein", + "lineage2_protein_weight": "0.4626846570186025", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "02_A1_Colon_naive_170427_UKy_GCB_rep2-quench_ICMSA.raw" + } + }, + { + "Subject ID": "03_A2_naive_0days_UKy_GCH_rep3", + "Sample ID": "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-polar-ICMS_A", + "Factors": { + "Treatment": "naive", + "Time Point": "0" + }, + "Additional sample data": { + "lineage0_id": "03_A2_naive_0days_UKy_GCH_rep3", + "lineage0_protocol.id": "['naive']", + "lineage0_replicate": "3", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "0", + "lineage0_type": "subject", + "lineage1_id": "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "03_A2_Colon_naive_0days_170427_UKy_GCH_rep3-protein", + "lineage2_protein_weight": "0.6317204628207644", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "03_A2_Colon_naive_170427_UKy_GCB_rep3-quench_ICMSA.raw" + } + }, + { + "Subject ID": "04_B0_syngenic_42days_UKy_GCH_rep1", + "Sample ID": "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "04_B0_syngenic_42days_UKy_GCH_rep1", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "04_B0_Colon_syngenic_42days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "0.6679368105226386", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "04_B0_Colon_syngenic_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "05_B1_syngenic_42days_UKy_GCH_rep2", + "Sample ID": "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "05_B1_syngenic_42days_UKy_GCH_rep2", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "2", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "05_B1_Colon_syngenic_42days_170427_UKy_GCH_rep2-protein", + "lineage2_protein_weight": "0.6160223770621668", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "05_B1_Colon_syngenic_170427_UKy_GCB_rep2-quench_ICMSA.raw" + } + }, + { + "Subject ID": "06_B2_syngenic_42days_UKy_GCH_rep3", + "Sample ID": "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "06_B2_syngenic_42days_UKy_GCH_rep3", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "3", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "06_B2_Colon_syngenic_42days_170427_UKy_GCH_rep3-protein", + "lineage2_protein_weight": "1.5623114343705382", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "06_B2_Colon_syngenic_170427_UKy_GCB_rep3-quench_ICMSA.raw" + } + }, + { + "Subject ID": "07_C1-1_allogenic_42days_UKy_GCH_rep1", + "Sample ID": "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "07_C1-1_allogenic_42days_UKy_GCH_rep1", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "07_C1-1_Colon_allogenic_42days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "1.2093335004111359", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "07_C1-1_Colon_allogenic_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "08_C1-2_allogenic_42days_UKy_GCH_rep2", + "Sample ID": "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "08_C1-2_allogenic_42days_UKy_GCH_rep2", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "2", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "08_C1-2_Colon_allogenic_42days_170427_UKy_GCH_rep2-protein", + "lineage2_protein_weight": "1.6115317800148334", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "08_C1-2_Colon_allogenic_170427_UKy_GCB_rep2-quench_ICMSA.raw" + } + }, + { + "Subject ID": "09_C2-0_allogenic_42days_UKy_GCH_rep1", + "Sample ID": "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "42" + }, + "Additional sample data": { + "lineage0_id": "09_C2-0_allogenic_42days_UKy_GCH_rep1", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "42", + "lineage0_type": "subject", + "lineage1_id": "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "09_C2-0_Colon_allogenic_42days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "1.6843458692847952", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "09_C2-0_Colon_allogenic_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "10_B1-0_syngenic_7days_UKy_GCH_rep1", + "Sample ID": "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "10_B1-0_syngenic_7days_UKy_GCH_rep1", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "10_B1-0_Colon_syngenic_7days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "0.9744309499653369", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "10_B1-0_Colon_syngenic_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "11_B1-1_syngenic_7days_UKy_GCH_rep2", + "Sample ID": "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "11_B1-1_syngenic_7days_UKy_GCH_rep2", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "2", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "11_B1-1_Colon_syngenic_7days_170427_UKy_GCH_rep2-protein", + "lineage2_protein_weight": "1.2443973624103961", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "11_B1-1_Colon_syngenic_170427_UKy_GCB_rep2-quench_ICMSA.raw" + } + }, + { + "Subject ID": "12_B1-2_syngenic_7days_UKy_GCH_rep3", + "Sample ID": "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A", + "Factors": { + "Treatment": "syngenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "12_B1-2_syngenic_7days_UKy_GCH_rep3", + "lineage0_protocol.id": "['syngenic']", + "lineage0_replicate": "3", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "12_B1-2_Colon_syngenic_7days_170427_UKy_GCH_rep3-protein", + "lineage2_protein_weight": "1.6127238309055993", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "12_B1-2_Colon_syngenic_170427_UKy_GCB_rep3-quench_ICMSA.raw" + } + }, + { + "Subject ID": "13_C1-1_allogenic_7days_UKy_GCH_rep1", + "Sample ID": "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "13_C1-1_allogenic_7days_UKy_GCH_rep1", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "1", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "13_C1-1_Colon_allogenic_7days_170427_UKy_GCH_rep1-protein", + "lineage2_protein_weight": "2.0735556513766173", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "13_C1-1_Colon_allogenic_170427_UKy_GCB_rep1-quench_ICMSA.raw" + } + }, + { + "Subject ID": "14_C1-2_allogenic_7days_UKy_GCH_rep2", + "Sample ID": "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "14_C1-2_allogenic_7days_UKy_GCH_rep2", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "2", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "14_C1-2_Colon_allogenic_7days_170427_UKy_GCH_rep2-protein", + "lineage2_protein_weight": "1.7420773775641787", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "14_C1-2_Colon_allogenic_170427_UKy_GCB_rep2-quench_ICMSA.raw" + } + }, + { + "Subject ID": "15_C1-20_allogenic_7days_UKy_GCH_rep3", + "Sample ID": "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-polar-ICMS_A", + "Factors": { + "Treatment": "allogenic", + "Time Point": "7" + }, + "Additional sample data": { + "lineage0_id": "15_C1-20_allogenic_7days_UKy_GCH_rep3", + "lineage0_protocol.id": "['allogenic']", + "lineage0_replicate": "3", + "lineage0_species": "Mus musculus", + "lineage0_species_type": "Mouse", + "lineage0_taxonomy_id": "10090", + "lineage0_time_point": "7", + "lineage0_type": "subject", + "lineage1_id": "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3", + "lineage1_protocol.id": "['mouse_tissue_collection', 'tissue_quench', 'frozen_tissue_grind']", + "lineage1_type": "sample", + "lineage2_id": "15_C1-20_Colon_allogenic_7days_170427_UKy_GCH_rep3-protein", + "lineage2_protein_weight": "1.9186640291000003", + "lineage2_protein_weight%units": "mg", + "lineage2_protocol.id": "['protein_extraction']", + "lineage2_type": "sample", + "RAW_FILE_NAME": "15_C1-20_Colon_allogenic_170427_UKy_GCB_rep3-quench_ICMSA.raw" + } + } + ], + "TREATMENT": { + "TREATMENT_PROTOCOL_FILENAME": "study_treatments.pdf;study_treatments.pdf;study_treatments.pdf", + "TREATMENT_PROTOCOL_ID": "allogenic;naive;syngenic", + "TREATMENT_SUMMARY": "Mouse with allogenic bone marrow transplant. Fed with semi-liquid diet supplemented with fully labeled glucose for 24 hours before harvest.;Mouse with no treatment. Fed with semi-liquid diet supplemented with fully labeled glucose for 24 hours before harvest.;Mouse with syngenic bone marrow transplant. Fed with semi-liquid diet supplemented with fully labeled glucose for 24 hours before harvest." + } +} \ No newline at end of file diff --git a/tests/example_data/other_test_data/download_studies_an_ids.json b/tests/example_data/other_test_data/download_studies_an_ids.json new file mode 100644 index 0000000..be04f8f --- /dev/null +++ b/tests/example_data/other_test_data/download_studies_an_ids.json @@ -0,0 +1 @@ +["AN000001", "AN000002"] \ No newline at end of file diff --git a/tests/example_data/other_test_data/download_studies_mixed_ids.json b/tests/example_data/other_test_data/download_studies_mixed_ids.json new file mode 100644 index 0000000..081afc1 --- /dev/null +++ b/tests/example_data/other_test_data/download_studies_mixed_ids.json @@ -0,0 +1 @@ +["000001", "AN000002", "ST000003", "asdf"] \ No newline at end of file diff --git a/tests/example_data/other_test_data/download_studies_study_ids.json b/tests/example_data/other_test_data/download_studies_study_ids.json new file mode 100644 index 0000000..73339dd --- /dev/null +++ b/tests/example_data/other_test_data/download_studies_study_ids.json @@ -0,0 +1 @@ +["ST000001", "ST000002"] \ No newline at end of file diff --git a/tests/example_data/other_test_data/download_studies_with_error.json b/tests/example_data/other_test_data/download_studies_with_error.json new file mode 100644 index 0000000..2f9d4af --- /dev/null +++ b/tests/example_data/other_test_data/download_studies_with_error.json @@ -0,0 +1,5 @@ +[ +"204", +"9999999", +"206" +] \ No newline at end of file diff --git a/tests/example_data/other_test_data/unknown_file_format.asdf b/tests/example_data/other_test_data/unknown_file_format.asdf new file mode 100644 index 0000000..e69de29 diff --git a/tests/example_data/validation_files/ST000122_AN000204_error_1.json b/tests/example_data/validation_files/ST000122_AN000204_error_1.json deleted file mode 100644 index a32251b..0000000 --- a/tests/example_data/validation_files/ST000122_AN000204_error_1.json +++ /dev/null @@ -1,242 +0,0 @@ -{ -"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST000122","ANALYSIS_ID":"AN000204","VERSION":"1","CREATED_ON":"2016-09-17"}, - -"PROJECT":{"PROJECT_TITLE":"Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic","PROJECT_TYPE":"Pilot and Feasibility Projects","PROJECT_SUMMARY":"-","INSTITUTE":"University of California, Davis","DEPARTMENT":"Nutrition","LABORATORY":"Gaikwad Lab","LAST_NAME":"Gaikwad","FIRST_NAME":"Nilesh","ADDRESS":"-","EMAIL":"nwgaikwad@ucdavis.edu","PHONE":"530-752-2906","FUNDING_SOURCE":"NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)"}, - -"STUDY":{"STUDY_TITLE":"Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic","STUDY_TYPE":"steroid panel","STUDY_SUMMARY":"-","INSTITUTE":"University of California, Davis","DEPARTMENT":"Nutrition","LABORATORY":"Gaikwad 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-} - -} - diff --git a/tests/example_data/validation_files/ST000122_AN000204_schema_errors.json b/tests/example_data/validation_files/ST000122_AN000204_schema_errors.json new file mode 100644 index 0000000..b30e71a --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_schema_errors.json @@ -0,0 +1,387 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "BADSUBSECTION": "bad value", + "BADSUBSECTION2": "bad value2", + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad at ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "BADSUBSECTION": "bad value", + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "-", + "NUM_GROUPS": "NA" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "", + "Sample ID": "", + "Factors": { + "Tissue/Fluid": "" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER340_244448_ML_27", + "Sample ID": "CER340_244448_ML_27", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER346_246320_ML_28", + "Sample ID": "CER346_246320_ML_28", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER356_269662_ML_29", + "Sample ID": "CER356_269662_ML_29", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER368_250104_ML_30", + "Sample ID": "CER368_250104_ML_30", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER369_276355_ML_31", + "Sample ID": "CER369_276355_ML_31", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER384_264971_ML_32", + "Sample ID": "CER384_264971_ML_32", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER445_286527_ML_33", + "Sample ID": "CER445_286527_ML_33", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER452_240972_ML_34", + "Sample ID": "CER452_240972_ML_34", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER463_271249_ML_35", + "Sample ID": "CER463_271249_ML_35", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER465_265004_ML_36", + "Sample ID": "CER465_265004_ML_36", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER483_294606_ML_37", + "Sample ID": "CER483_294606_ML_37", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER488_274343_ML_38", + "Sample ID": "CER488_274343_ML_38", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER530_249229_ML_39", + "Sample ID": "CER530_249229_ML_39", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER540_240346_ML_40", + "Sample ID": "CER540_240346_ML_40", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER552_241945_ML_41", + "Sample ID": "CER552_241945_ML_41", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER555_251239_ML_42", + "Sample ID": "CER555_251239_ML_42", + "Factors": { + "Tissue/Fluid": "Serum" + } + } + ], + "TREATMENT": { + "TREATMENT_SUMMARY": "-" + }, + "SAMPLEPREP": { + "SAMPLEPREP_SUMMARY": "Methanol: Water Extraction", + "SAMPLEPREP_PROTOCOL_FILENAME": "NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx", + "PROCESSING_METHOD": "Homogenization and Solvent Removal w/ Speed Vac", + "PROCESSING_STORAGE_CONDITIONS": "On Ice", + "EXTRACTION_METHOD": "1:1 Methanol: Water", + "EXTRACT_STORAGE": "-80C", + "SAMPLE_RESUSPENSION": "150ul CH3OH/H2O", + "ORGAN": "Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid Fetal: Male and female brain, male and female liver" + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted UPLC-MS/MS", + "CHROMATOGRAPHY_TYPE": "Reversed phase", + "INSTRUMENT_NAME": "Waters Acquity", + "COLUMN_NAME": "Waters Acquity HSS T3 (150 x 2.1mm, 1.8um)", + "FLOW_GRADIENT": "0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A", + "FLOW_RATE": "0.15 ml/min", + "SAMPLE_INJECTION": "10ul", + "SOLVENT_A": "Water 0.1% formic acid", + "SOLVENT_B": "CH3CN 0.1 % formic acid", + "ANALYTICAL_TIME": "12 mins" + }, + "ANALYSIS": { + "ANALYSIS_TYPE": "MS", + "LABORATORY_NAME": "Gaikwad Laboratory", + "ACQUISITION_DATE": "41716", + "SOFTWARE_VERSION": "Masslynx", + "OPERATOR_NAME": "Nilesh Gaikwad" + }, + "MS": { + "INSTRUMENT_NAME": "Waters Xevo-TQ", + "INSTRUMENT_TYPE": "Triple quadrupole", + "MS_TYPE": "ESI", + "ION_MODE": "POSITIVE", + "CAPILLARY_VOLTAGE": "3.0 kV", + "COLLISION_GAS": "N2", + "IONIZATION": "positive", + "SOURCE_TEMPERATURE": "150C", + "DESOLVATION_GAS_FLOW": "600 L/h", + "DESOLVATION_TEMPERATURE": "350C", + "MS_COMMENTS": "UPLC-MS/MS" + }, + "BADSECTION": {} +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_schema_errors.txt b/tests/example_data/validation_files/ST000122_AN000204_schema_errors.txt new file mode 100644 index 0000000..7a268f3 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_schema_errors.txt @@ -0,0 +1,144 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#BADSECTION +#PROJECT +PR:BADSUBSECTION bad value +PR:BADSUBSECTION2 bad value2 +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad at ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:BADSUBSECTION bad value +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS Tissue/Fluid: +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION positive +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_extended.json b/tests/example_data/validation_files/ST000122_AN000204_validate_extended.json new file mode 100644 index 0000000..fdf00c7 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_extended.json @@ -0,0 +1,1192 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "-", + "NUM_GROUPS": "NA" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER340_244448_ML_27", + "Sample ID": "CER340_244448_ML_27", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER346_246320_ML_28", + "Sample ID": "CER346_246320_ML_28", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER356_269662_ML_29", + "Sample ID": "CER356_269662_ML_29", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER368_250104_ML_30", + "Sample ID": "CER368_250104_ML_30", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER369_276355_ML_31", + "Sample ID": "CER369_276355_ML_31", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER384_264971_ML_32", + "Sample ID": "CER384_264971_ML_32", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER445_286527_ML_33", + "Sample ID": "CER445_286527_ML_33", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER452_240972_ML_34", + "Sample ID": "CER452_240972_ML_34", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER463_271249_ML_35", + "Sample ID": "CER463_271249_ML_35", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER465_265004_ML_36", + "Sample ID": "CER465_265004_ML_36", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER483_294606_ML_37", + "Sample ID": "CER483_294606_ML_37", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER488_274343_ML_38", + "Sample ID": "CER488_274343_ML_38", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER530_249229_ML_39", + "Sample ID": "CER530_249229_ML_39", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER540_240346_ML_40", + "Sample ID": "CER540_240346_ML_40", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER552_241945_ML_41", + "Sample ID": "CER552_241945_ML_41", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER555_251239_ML_42", + "Sample ID": "CER555_251239_ML_42", + "Factors": { + "Tissue/Fluid": "Serum" + } + } + ], + "COLLECTION": { + "COLLECTION_SUMMARY": "-" + }, + "TREATMENT": { + "TREATMENT_SUMMARY": "-" + }, + "SAMPLEPREP": { + "SAMPLEPREP_SUMMARY": "Methanol: Water Extraction", + "SAMPLEPREP_PROTOCOL_FILENAME": "NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx", + "PROCESSING_METHOD": "Homogenization and Solvent Removal w/ Speed Vac", + "PROCESSING_STORAGE_CONDITIONS": "On Ice", + "EXTRACTION_METHOD": "1:1 Methanol: Water", + "EXTRACT_STORAGE": "-80C", + "SAMPLE_RESUSPENSION": "150ul CH3OH/H2O", + "ORGAN": "Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid Fetal: Male and female brain, male and female liver" + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted UPLC-MS/MS", + "CHROMATOGRAPHY_TYPE": "Reversed phase", + "INSTRUMENT_NAME": "Waters Acquity", + "COLUMN_NAME": "Waters Acquity HSS T3 (150 x 2.1mm, 1.8um)", + "FLOW_GRADIENT": "0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A", + "FLOW_RATE": "0.15 ml/min", + "SAMPLE_INJECTION": "10ul", + "SOLVENT_A": "Water 0.1% formic acid", + "SOLVENT_B": "CH3CN 0.1 % formic acid", + "ANALYTICAL_TIME": "12 mins" + }, + "ANALYSIS": { + "ANALYSIS_TYPE": "MS", + "LABORATORY_NAME": "Gaikwad Laboratory", + "ACQUISITION_DATE": "41716", + "SOFTWARE_VERSION": "Masslynx", + "OPERATOR_NAME": "Nilesh Gaikwad" + }, + "MS": { + "INSTRUMENT_NAME": "Waters Xevo-TQ", + "INSTRUMENT_TYPE": "Triple quadrupole", + "MS_TYPE": "ESI", + "ION_MODE": "POSITIVE", + "CAPILLARY_VOLTAGE": "3.0 kV", + "COLLISION_GAS": "N2", + "IONIZATION": "Electrospray Ionization", + "SOURCE_TEMPERATURE": "150C", + "DESOLVATION_GAS_FLOW": "600 L/h", + "DESOLVATION_TEMPERATURE": "350C", + "MS_COMMENTS": "UPLC-MS/MS" + }, + "MS_METABOLITE_DATA": { + "Units": "pg/ml", + "Data": [ + { + "Metabolite": "17-hydroxypregnenolone", + "CER030_294717_ML_1": "946.2500", + "CER040_242995_ML_2": "0.0000", + "CER055_249947_ML_3": "676.2500", + "CER062_246153_ML_4": "0.0000", + "CER085_251176_ML_5": "2251.2500", + "CER093_242931_ML_6": "0.0000", + "CER110_238825_ML_7": "0.0000", + "CER120_253690_ML_8": "1134.7500", + "CER147_254803_ML_9": "0.0000", + "CER149_266689_ML_10": "0.0000", + "CER158_254231_ML_11": "2016.7500", + "CER165_287001_ML_12": "0.0000", + "CER178_295145_ML_13": "0.0000", + 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"CER178_295145_ML_13": "94.5000", + "CER181_244392_ML_14": "160.7500", + "CER188_250760_ML_15": "0.0000", + "CER192_254091_ML_16": "3214.5000", + "CER201_244193_ML_17": "218.2500", + "CER216_242490_ML_18": "1.0000", + "CER220_274308_ML_19": "0.0000", + "CER223_264067_ML_20": "20.0000", + "CER226_254303_ML_21": "4.5000", + "CER277_255328_ML_22": "55.7500", + "CER287_248530_ML_23": "24.5000", + "CER303_253023_ML_24": "57.0000", + "CER315_282966_ML_25": "200.5000", + "CER324_285069_ML_26": "138.7500", + "CER340_244448_ML_27": "132.2500", + "CER346_246320_ML_28": "120.5000", + "CER356_269662_ML_29": "80.5000", + "CER368_250104_ML_30": "59.5000", + "CER369_276355_ML_31": "315.7500", + "CER384_264971_ML_32": "247.2500", + "CER445_286527_ML_33": "211.5000", + "CER452_240972_ML_34": "198.5000", + "CER463_271249_ML_35": "232.2500", + "CER465_265004_ML_36": "241.0000", + "CER483_294606_ML_37": "199.5000", + "CER488_274343_ML_38": "282.5000", + "CER530_249229_ML_39": "216.5000", + "CER540_240346_ML_40": "358.5000", + "CER552_241945_ML_41": "289.5000", + "CER555_251239_ML_42": "199.2500" + }, + { + "Metabolite": "Testosterone", + "CER030_294717_ML_1": "75.7500", + "CER040_242995_ML_2": "63.2500", + "CER055_249947_ML_3": "42.7500", + "CER062_246153_ML_4": "98.0000", + "CER085_251176_ML_5": "24.2500", + "CER093_242931_ML_6": "35.0000", + "CER110_238825_ML_7": "165.7500", + "CER120_253690_ML_8": "23.2500", + "CER147_254803_ML_9": "73.7500", + "CER149_266689_ML_10": "52.7500", + "CER158_254231_ML_11": "118.7500", + "CER165_287001_ML_12": "35.7500", + "CER178_295145_ML_13": "65.2500", + "CER181_244392_ML_14": "127.2500", + "CER188_250760_ML_15": "14.2500", + "CER192_254091_ML_16": "202.5000", + "CER201_244193_ML_17": "110.7500", + "CER216_242490_ML_18": "53.5000", + "CER220_274308_ML_19": "54.2500", + "CER223_264067_ML_20": "2.2500", + "CER226_254303_ML_21": "105.2500", + "CER277_255328_ML_22": "182.7500", + "CER287_248530_ML_23": "116.0000", + "CER303_253023_ML_24": "66.2500", + "CER315_282966_ML_25": "52.5000", + "CER324_285069_ML_26": "106.2500", + "CER340_244448_ML_27": "43.2500", + "CER346_246320_ML_28": "57.2500", + "CER356_269662_ML_29": "97.2500", + "CER368_250104_ML_30": "16.0000", + "CER369_276355_ML_31": "192.0000", + "CER384_264971_ML_32": "53.7500", + "CER445_286527_ML_33": "182.5000", + "CER452_240972_ML_34": "0.2500", + "CER463_271249_ML_35": "11.5000", + "CER465_265004_ML_36": "87.2500", + "CER483_294606_ML_37": "33.7500", + "CER488_274343_ML_38": "45.5000", + "CER530_249229_ML_39": "26.2500", + "CER540_240346_ML_40": "96.0000", + "CER552_241945_ML_41": "17.5000", + "CER555_251239_ML_42": "79.7500" + } + ], + "Metabolites": [ + { + "Metabolite": "17-hydroxypregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "91451", + "inchi_key": "", + "kegg_id": "", + "other_id": "2Q4710", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "17-hydroxyprogesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6238", + "inchi_key": "", + "kegg_id": "", + "other_id": "6Q3360", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Allodihydrotestosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "10635", + "inchi_key": "", + "kegg_id": "", + "other_id": "14A2570", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Androstenedione", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6128", + "inchi_key": "", + "kegg_id": "", + "other_id": "12A6030", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Androstenolone (DHEA)", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5881", + "inchi_key": "", + "kegg_id": "", + "other_id": "3A8500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortexolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "440707", + "inchi_key": "", + "kegg_id": "", + "other_id": "7Q1610", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortexone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6166", + "inchi_key": "", + "kegg_id": "", + "other_id": "9Q3460", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Corticosterone, DOC", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5753", + "inchi_key": "", + "kegg_id": "", + "other_id": "10Q1550", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortisol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5754", + "inchi_key": "", + "kegg_id": "", + "other_id": "8Q3880", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Estradiol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5757", + "inchi_key": "", + "kegg_id": "", + "other_id": "16E0950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Estrone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5870", + "inchi_key": "", + "kegg_id": "", + "other_id": "15E2300", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Pregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "8955", + "inchi_key": "", + "kegg_id": "", + "other_id": "1Q5500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Progesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5994", + "inchi_key": "", + "kegg_id": "", + "other_id": "5Q2600", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Testosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6013", + "inchi_key": "", + "kegg_id": "", + "other_id": "13A6950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + } + ], + "Extended": [ + { + "Metabolite": "IMF1", + "assigned_mass": "[518.13104102, 513.17564587]", + "assigned_mass%type": "adduct subtracted", + "assigned_mass%units": "daltons", + "assignment%type": "['SMIRFE', 'voted']", + "assignment_scores": "[0.99991183, 'NA']", + "expectation_values": "[8.817e-05, 'NA']", + "raw_mz": "536.16541515", + "raw_mz%units": "daltons/charge", + "raw_mz_sd": "2.412e-05", + "sample_id": "16_A0_Lung_naive_0days_170427_UKy_GCH_rep1-lipid" + } + ] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_extended.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_extended.txt new file mode 100644 index 0000000..56e80f9 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_extended.txt @@ -0,0 +1,166 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 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53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +EXTENDED_MS_METABOLITE_DATA_START +metabolite_name assigned_mass assigned_mass%type assigned_mass%units assignment%type assignment_scores expectation_values raw_mz raw_mz%units raw_mz_sd sample_id +IMF1 [518.13104102, 513.17564587] adduct subtracted daltons ['SMIRFE', 'voted'] [0.99991183, 'NA'] [8.817e-05, 'NA'] 536.16541515 daltons/charge 2.412e-05 16_A0_Lung_naive_0days_170427_UKy_GCH_rep1-lipid +EXTENDED_MS_METABOLITE_DATA_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_extended2.json b/tests/example_data/validation_files/ST000122_AN000204_validate_extended2.json new file mode 100644 index 0000000..c8f9098 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_extended2.json @@ -0,0 +1,1192 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "-", + "NUM_GROUPS": "NA" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { 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"6238", + "inchi_key": "", + "kegg_id": "", + "other_id": "6Q3360", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Allodihydrotestosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "10635", + "inchi_key": "", + "kegg_id": "", + "other_id": "14A2570", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Androstenedione", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6128", + "inchi_key": "", + "kegg_id": "", + "other_id": "12A6030", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Androstenolone (DHEA)", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5881", + "inchi_key": "", + "kegg_id": "", + "other_id": "3A8500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortexolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "440707", + "inchi_key": "", + "kegg_id": "", + "other_id": "7Q1610", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortexone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6166", + "inchi_key": "", + "kegg_id": "", + "other_id": "9Q3460", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Corticosterone, DOC", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5753", + "inchi_key": "", + "kegg_id": "", + "other_id": "10Q1550", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortisol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5754", + "inchi_key": "", + "kegg_id": "", + "other_id": "8Q3880", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Estradiol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5757", + "inchi_key": "", + "kegg_id": "", + "other_id": "16E0950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Estrone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5870", + "inchi_key": "", + "kegg_id": "", + "other_id": "15E2300", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Pregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "8955", + "inchi_key": "", + "kegg_id": "", + "other_id": "1Q5500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Progesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5994", + "inchi_key": "", + "kegg_id": "", + "other_id": "5Q2600", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Testosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6013", + "inchi_key": "", + "kegg_id": "", + "other_id": "13A6950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + } + ], + "Extended": [ + { + "Metabolite": "IMF1", + "assigned_mass": "[518.13104102, 513.17564587]", + "assigned_mass%type": "adduct subtracted", + "assigned_mass%units": "daltons", + "assignment%type": "['SMIRFE', 'voted']", + "assignment_scores": "[0.99991183, 'NA']", + "expectation_values": "[8.817e-05, 'NA']", + "raw_mz": "536.16541515", + "raw_mz%units": "daltons/charge", + "raw_mz_sd": "2.412e-05", + "asdf": "16_A0_Lung_naive_0days_170427_UKy_GCH_rep1-lipid" + } + ] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_extended2.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_extended2.txt new file mode 100644 index 0000000..9fe87f6 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_extended2.txt @@ -0,0 +1,166 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +EXTENDED_MS_METABOLITE_DATA_START +metabolite_name assigned_mass assigned_mass%type assigned_mass%units assignment%type assignment_scores expectation_values raw_mz raw_mz%units raw_mz_sd asdf +IMF1 [518.13104102, 513.17564587] adduct subtracted daltons ['SMIRFE', 'voted'] [0.99991183, 'NA'] [8.817e-05, 'NA'] 536.16541515 daltons/charge 2.412e-05 16_A0_Lung_naive_0days_170427_UKy_GCH_rep1-lipid +EXTENDED_MS_METABOLITE_DATA_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_error_3.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_factors.txt similarity index 98% rename from tests/example_data/validation_files/ST000122_AN000204_error_3.txt rename to tests/example_data/validation_files/ST000122_AN000204_validate_factors.txt index c5d1330..336adb4 100644 --- a/tests/example_data/validation_files/ST000122_AN000204_error_3.txt +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_factors.txt @@ -1,162 +1,162 @@ -#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 -VERSION 1 -CREATED_ON 2016-09-17 -#PROJECT -PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic -PR:PROJECT_TYPE Pilot and Feasibility Projects -PR:PROJECT_SUMMARY - -PR:INSTITUTE University of California, Davis -PR:DEPARTMENT Nutrition -PR:LABORATORY Gaikwad Lab -PR:LAST_NAME Gaikwad -PR:FIRST_NAME Nilesh -PR:ADDRESS - -PR:EMAIL nwgaikwad@ucdavis.edu -PR:PHONE 530-752-2906 -PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL -PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) -#STUDY -ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic -ST:STUDY_TYPE steroid panel -ST:STUDY_SUMMARY - -ST:INSTITUTE University of California, Davis -ST:DEPARTMENT Nutrition -ST:LABORATORY Gaikwad Lab -ST:LAST_NAME Gaikwad -ST:FIRST_NAME Nilesh -ST:ADDRESS - -ST:EMAIL nwgaikwad@ucdavis.edu -ST:PHONE - -ST:NUM_GROUPS NA -#SUBJECT -SU:SUBJECT_TYPE Human -SU:SUBJECT_SPECIES Homo sapiens -SU:TAXONOMY_ID 9606 -#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data -SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum -#COLLECTION -CO:COLLECTION_SUMMARY - -#TREATMENT -TR:TREATMENT_SUMMARY - -#SAMPLEPREP -SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction -SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx -SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac -SP:PROCESSING_STORAGE_CONDITIONS On Ice -SP:EXTRACTION_METHOD 1:1 Methanol: Water -SP:EXTRACT_STORAGE -80C -SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O -SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid -SP:ORGAN Fetal: Male and female brain, male and female liver -#CHROMATOGRAPHY -CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS -CH:CHROMATOGRAPHY_TYPE Reversed phase -CH:INSTRUMENT_NAME Waters Acquity -CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) -CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min -CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A -CH:FLOW_RATE 0.15 ml/min -CH:SAMPLE_INJECTION 10ul -CH:SOLVENT_A Water 0.1% formic acid -CH:SOLVENT_B CH3CN 0.1 % formic acid -CH:ANALYTICAL_TIME 12 mins -#ANALYSIS -AN:ANALYSIS_TYPE MS -AN:LABORATORY_NAME Gaikwad Laboratory -AN:ACQUISITION_DATE 41716 -AN:SOFTWARE_VERSION Masslynx -AN:OPERATOR_NAME Nilesh Gaikwad -#MS -MS:INSTRUMENT_NAME Waters Xevo-TQ -MS:INSTRUMENT_TYPE Triple quadrupole -MS:MS_TYPE ESI -MS:ION_MODE POSITIVE -MS:CAPILLARY_VOLTAGE 3.0 kV -MS:COLLISION_GAS N2 -MS:IONIZATION Electrospray Ionization -MS:SOURCE_TEMPERATURE 150C -MS:DESOLVATION_GAS_FLOW 600 L/h -MS:DESOLVATION_TEMPERATURE 350C -MS:MS_COMMENTS UPLC-MS/MS -#MS_METABOLITE_DATA -MS_METABOLITE_DATA:UNITS pg/ml -MS_METABOLITE_DATA_START -Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 -Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum -17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 -17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 -Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 -Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 -Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 -Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 -Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 -Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 -Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 -Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 -Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 -Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 -Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 -Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 -MS_METABOLITE_DATA_END -#METABOLITES -METABOLITES_START -metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type -17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID -17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID -Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID -Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID -Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID -Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID -Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID -Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID -Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID -Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID -Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID -Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID -Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID -Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID -METABOLITES_END -#END - - +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:asdf +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_file.json b/tests/example_data/validation_files/ST000122_AN000204_validate_file.json new file mode 100644 index 0000000..cf87944 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_file.json @@ -0,0 +1,1009 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "asdf", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "asdf", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "asdf", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "asdf", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "asdf", + "NUM_GROUPS": "2" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER340_244448_ML_27", + "Sample ID": "CER340_244448_ML_27", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER346_246320_ML_28", + "Sample ID": "CER346_246320_ML_28", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER356_269662_ML_29", + "Sample ID": "CER356_269662_ML_29", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER368_250104_ML_30", + "Sample ID": "CER368_250104_ML_30", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER369_276355_ML_31", + "Sample ID": "CER369_276355_ML_31", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER384_264971_ML_32", + "Sample ID": "CER384_264971_ML_32", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER445_286527_ML_33", + "Sample ID": "CER445_286527_ML_33", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER452_240972_ML_34", + "Sample ID": "CER452_240972_ML_34", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER463_271249_ML_35", + "Sample ID": "CER463_271249_ML_35", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER465_265004_ML_36", + "Sample ID": "CER465_265004_ML_36", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER483_294606_ML_37", + "Sample ID": "CER483_294606_ML_37", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER488_274343_ML_38", + "Sample ID": "CER488_274343_ML_38", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER530_249229_ML_39", + "Sample ID": "CER530_249229_ML_39", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER540_240346_ML_40", + "Sample ID": "CER540_240346_ML_40", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER552_241945_ML_41", + "Sample ID": "CER552_241945_ML_41", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER555_251239_ML_42", + "Sample ID": "CER555_251239_ML_42", + "Factors": { + "Tissue/Fluid": "Serum" + } + } + ], + "COLLECTION": { + "COLLECTION_SUMMARY": "asdf" + }, + "TREATMENT": { + "TREATMENT_SUMMARY": "asdf" + }, + "SAMPLEPREP": { + "SAMPLEPREP_SUMMARY": "Methanol: Water Extraction", + "SAMPLEPREP_PROTOCOL_FILENAME": "NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx", + "PROCESSING_METHOD": "Homogenization and Solvent Removal w/ Speed Vac", + "PROCESSING_STORAGE_CONDITIONS": "On Ice", + "EXTRACTION_METHOD": "1:1 Methanol: Water", + "EXTRACT_STORAGE": "-80C", + "SAMPLE_RESUSPENSION": "150ul CH3OH/H2O", + "ORGAN": "Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid Fetal: Male and female brain, male and female liver" + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted UPLC-MS/MS", + "CHROMATOGRAPHY_TYPE": "Reversed phase", + "INSTRUMENT_NAME": "Waters Acquity", + "COLUMN_NAME": "Waters Acquity HSS T3 (150 x 2.1mm, 1.8um)", + "FLOW_GRADIENT": "0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A", + "FLOW_RATE": "0.15 mL/min", + "SAMPLE_INJECTION": "10 uL", + "SOLVENT_A": "Water 0.1% formic acid", + "SOLVENT_B": "CH3CN 0.1 % formic acid", + "ANALYTICAL_TIME": "12 min", + "COLUMN_TEMPERATURE": "150 C" + }, + "ANALYSIS": { + "ANALYSIS_TYPE": "MS", + "LABORATORY_NAME": "Gaikwad Laboratory", + "ACQUISITION_DATE": "41716", + "SOFTWARE_VERSION": "Masslynx", + "OPERATOR_NAME": "Nilesh Gaikwad" + }, + "MS_METABOLITE_DATA": { + "Units": "pg/ml", + "Data": [ + { + "Metabolite": "17-hydroxypregnenolone", + "CER030_294717_ML_1": "946.2500", + "CER040_242995_ML_2": "0.0000", + "CER055_249947_ML_3": "676.2500", + "CER062_246153_ML_4": "0.0000", + "CER085_251176_ML_5": "2251.2500", + "CER093_242931_ML_6": "0.0000", + "CER110_238825_ML_7": "0.0000", + "CER120_253690_ML_8": "1134.7500", + "CER147_254803_ML_9": "0.0000", + "CER149_266689_ML_10": "0.0000", + "CER158_254231_ML_11": "2016.7500", + "CER165_287001_ML_12": "0.0000", + "CER178_295145_ML_13": "0.0000", + "CER181_244392_ML_14": "0.0000", + "CER188_250760_ML_15": "0.0000", + "CER192_254091_ML_16": "0.0000", + "CER201_244193_ML_17": "0.0000", + "CER216_242490_ML_18": "0.0000", + 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"211831.0000", + "CER178_295145_ML_13": "334929.7500", + "CER181_244392_ML_14": "235466.7500", + "CER188_250760_ML_15": "352555.0000", + "CER192_254091_ML_16": "410500.0000", + "CER201_244193_ML_17": "887955.0000", + "CER216_242490_ML_18": "865791.7500", + "CER220_274308_ML_19": "1648163.5000", + "CER223_264067_ML_20": "856726.7500", + "CER226_254303_ML_21": "579044.2500", + "CER277_255328_ML_22": "254013.2500", + "CER287_248530_ML_23": "326272.7500", + "CER303_253023_ML_24": "239893.7500", + "CER315_282966_ML_25": "329553.2500", + "CER324_285069_ML_26": "438715.5000", + "CER340_244448_ML_27": "248489.0000", + "CER346_246320_ML_28": "380251.0000", + "CER356_269662_ML_29": "338965.5000", + "CER368_250104_ML_30": "337231.2500", + "CER369_276355_ML_31": "342754.5000", + "CER384_264971_ML_32": "370657.2500", + "CER445_286527_ML_33": "2028106.5000", + "CER452_240972_ML_34": "733521.0000", + "CER463_271249_ML_35": "399244.2500", + "CER465_265004_ML_36": "321007.5000", + "CER483_294606_ML_37": "634463.0000", + "CER488_274343_ML_38": "231294.0000", + "CER530_249229_ML_39": "349439.2500", + "CER540_240346_ML_40": "75746.7500", + "CER552_241945_ML_41": "399415.5000", + "CER555_251239_ML_42": "303855.7500" + }, + { + "Metabolite": "Estrone", + "CER030_294717_ML_1": "484.5000", + "CER040_242995_ML_2": "1663.7500", + "CER055_249947_ML_3": "1680.7500", + "CER062_246153_ML_4": "794.5000", + "CER085_251176_ML_5": "557.2500", + "CER093_242931_ML_6": "625.7500", + "CER110_238825_ML_7": "669.7500", + "CER120_253690_ML_8": "885.0000", + "CER147_254803_ML_9": "715.0000", + "CER149_266689_ML_10": "1225.5000", + "CER158_254231_ML_11": "697.7500", + "CER165_287001_ML_12": "478.2500", + "CER178_295145_ML_13": "659.0000", + "CER181_244392_ML_14": "575.5000", + "CER188_250760_ML_15": "871.7500", + "CER192_254091_ML_16": "1089.0000", + "CER201_244193_ML_17": "1726.2500", + "CER216_242490_ML_18": "2325.2500", + "CER220_274308_ML_19": "3286.7500", + "CER223_264067_ML_20": "1955.7500", + "CER226_254303_ML_21": "1094.0000", + "CER277_255328_ML_22": "486.2500", + "CER287_248530_ML_23": "650.5000", + "CER303_253023_ML_24": "574.2500", + "CER315_282966_ML_25": "601.7500", + "CER324_285069_ML_26": "842.7500", + "CER340_244448_ML_27": "757.7500", + "CER346_246320_ML_28": "732.7500", + "CER356_269662_ML_29": "571.7500", + "CER368_250104_ML_30": "693.7500", + "CER369_276355_ML_31": "1004.2500", + "CER384_264971_ML_32": "879.2500", + "CER445_286527_ML_33": "3154.7500", + "CER452_240972_ML_34": "1095.2500", + "CER463_271249_ML_35": "22680.2500", + "CER465_265004_ML_36": "637.2500", + "CER483_294606_ML_37": "1108.2500", + "CER488_274343_ML_38": "474.2500", + "CER530_249229_ML_39": "810.2500", + "CER540_240346_ML_40": "421.2500", + "CER552_241945_ML_41": "680.7500", + "CER555_251239_ML_42": "623.7500" + }, + { + "Metabolite": "Pregnenolone", + "CER030_294717_ML_1": "12.2500", + "CER040_242995_ML_2": "0.0000", + "CER055_249947_ML_3": "0.0000", + "CER062_246153_ML_4": "0.0000", + "CER085_251176_ML_5": "0.0000", + "CER093_242931_ML_6": "144.2500", + "CER110_238825_ML_7": "14.7500", + "CER120_253690_ML_8": "807.2500", + "CER147_254803_ML_9": "0.0000", + "CER149_266689_ML_10": "30.0000", + "CER158_254231_ML_11": "0.0000", + "CER165_287001_ML_12": "0.0000", + "CER178_295145_ML_13": "0.0000", + "CER181_244392_ML_14": "0.0000", + "CER188_250760_ML_15": "16.5000", + "CER192_254091_ML_16": "139.5000", + "CER201_244193_ML_17": "132.5000", + "CER216_242490_ML_18": "0.0000", + "CER220_274308_ML_19": "0.0000", + "CER223_264067_ML_20": "13.7500", + "CER226_254303_ML_21": "0.0000", + "CER277_255328_ML_22": "0.0000", + "CER287_248530_ML_23": "0.0000", + "CER303_253023_ML_24": "0.0000", + "CER315_282966_ML_25": "0.0000", + "CER324_285069_ML_26": "0.0000", + "CER340_244448_ML_27": "0.0000", + "CER346_246320_ML_28": "0.0000", + "CER356_269662_ML_29": "0.0000", + "CER368_250104_ML_30": "0.0000", + "CER369_276355_ML_31": "488.5000", + "CER384_264971_ML_32": "0.0000", + "CER445_286527_ML_33": "0.0000", + "CER452_240972_ML_34": "0.0000", + "CER463_271249_ML_35": "0.0000", + "CER465_265004_ML_36": "280.7500", + "CER483_294606_ML_37": "0.0000", + "CER488_274343_ML_38": "0.0000", + "CER530_249229_ML_39": "0.0000", + "CER540_240346_ML_40": "0.0000", + "CER552_241945_ML_41": "0.0000", + "CER555_251239_ML_42": "205.5000" + }, + { + "Metabolite": "Progesterone", + "CER030_294717_ML_1": "28.2500", + "CER040_242995_ML_2": "6.2500", + "CER055_249947_ML_3": "725.2500", + "CER062_246153_ML_4": "57.2500", + "CER085_251176_ML_5": "767.0000", + "CER093_242931_ML_6": "2.7500", + "CER110_238825_ML_7": "388.0000", + "CER120_253690_ML_8": "9.0000", + "CER147_254803_ML_9": "19.5000", + "CER149_266689_ML_10": "242.5000", + "CER158_254231_ML_11": "4.0000", + "CER165_287001_ML_12": "0.0000", + "CER178_295145_ML_13": "94.5000", + "CER181_244392_ML_14": "160.7500", + "CER188_250760_ML_15": "0.0000", + "CER192_254091_ML_16": "3214.5000", + "CER201_244193_ML_17": "218.2500", + "CER216_242490_ML_18": "1.0000", + "CER220_274308_ML_19": "0.0000", + "CER223_264067_ML_20": "20.0000", + "CER226_254303_ML_21": "4.5000", + "CER277_255328_ML_22": "55.7500", + "CER287_248530_ML_23": "24.5000", + "CER303_253023_ML_24": "57.0000", + "CER315_282966_ML_25": "200.5000", + "CER324_285069_ML_26": "138.7500", + "CER340_244448_ML_27": "132.2500", + "CER346_246320_ML_28": "120.5000", + "CER356_269662_ML_29": "80.5000", + "CER368_250104_ML_30": "59.5000", + "CER369_276355_ML_31": "315.7500", + "CER384_264971_ML_32": "247.2500", + "CER445_286527_ML_33": "211.5000", + "CER452_240972_ML_34": "198.5000", + "CER463_271249_ML_35": "232.2500", + "CER465_265004_ML_36": "241.0000", + "CER483_294606_ML_37": "199.5000", + "CER488_274343_ML_38": "282.5000", + "CER530_249229_ML_39": "216.5000", + "CER540_240346_ML_40": "358.5000", + "CER552_241945_ML_41": "289.5000", + "CER555_251239_ML_42": "199.2500" + }, + { + "Metabolite": "Testosterone", + "CER030_294717_ML_1": "75.7500", + "CER040_242995_ML_2": "63.2500", + "CER055_249947_ML_3": "42.7500", + "CER062_246153_ML_4": "98.0000", + "CER085_251176_ML_5": "24.2500", + "CER093_242931_ML_6": "35.0000", + "CER110_238825_ML_7": "165.7500", + "CER120_253690_ML_8": "23.2500", + "CER147_254803_ML_9": "73.7500", + "CER149_266689_ML_10": "52.7500", + "CER158_254231_ML_11": "118.7500", + "CER165_287001_ML_12": "35.7500", + "CER178_295145_ML_13": "65.2500", + "CER181_244392_ML_14": "127.2500", + "CER188_250760_ML_15": "14.2500", + "CER192_254091_ML_16": "202.5000", + "CER201_244193_ML_17": "110.7500", + "CER216_242490_ML_18": "53.5000", + "CER220_274308_ML_19": "54.2500", + "CER223_264067_ML_20": "2.2500", + "CER226_254303_ML_21": "105.2500", + "CER277_255328_ML_22": "182.7500", + "CER287_248530_ML_23": "116.0000", + "CER303_253023_ML_24": "66.2500", + "CER315_282966_ML_25": "52.5000", + "CER324_285069_ML_26": "106.2500", + "CER340_244448_ML_27": "43.2500", + "CER346_246320_ML_28": "57.2500", + "CER356_269662_ML_29": "97.2500", + "CER368_250104_ML_30": "16.0000", + "CER369_276355_ML_31": "192.0000", + "CER384_264971_ML_32": "53.7500", + "CER445_286527_ML_33": "182.5000", + "CER452_240972_ML_34": "0.2500", + "CER463_271249_ML_35": "11.5000", + "CER465_265004_ML_36": "87.2500", + "CER483_294606_ML_37": "33.7500", + "CER488_274343_ML_38": "45.5000", + "CER530_249229_ML_39": "26.2500", + "CER540_240346_ML_40": "96.0000", + "CER552_241945_ML_41": "17.5000", + "CER555_251239_ML_42": "79.7500" + } + ] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_file.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_file.txt new file mode 100644 index 0000000..b5b607e --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_file.txt @@ -0,0 +1,133 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY asdf +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS asdf +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY asdf +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS asdf +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE asdf +ST:NUM_GROUPS 2 +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY asdf +#TREATMENT +TR:TREATMENT_SUMMARY asdf +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 mL/min +CH:SAMPLE_INJECTION 10 uL +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 min +CH:COLUMN_TEMPERATURE 150 C +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone, DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_header_lengths.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_header_lengths.txt new file mode 100644 index 0000000..c790b95 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_header_lengths.txt @@ -0,0 +1,162 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 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0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 123.24 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID asdf +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_data.json b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_data.json new file mode 100644 index 0000000..2b02e3e --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_data.json @@ -0,0 +1,1267 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "-", + "NUM_GROUPS": "NA" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER340_244448_ML_27", + "Sample ID": "CER340_244448_ML_27", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER346_246320_ML_28", + "Sample ID": "CER346_246320_ML_28", + "Factors": { + "Tissue/Fluid": "Serum" + } + 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Extraction", + "SAMPLEPREP_PROTOCOL_FILENAME": "NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx", + "PROCESSING_METHOD": "Homogenization and Solvent Removal w/ Speed Vac", + "PROCESSING_STORAGE_CONDITIONS": "On Ice", + "EXTRACTION_METHOD": "1:1 Methanol: Water", + "EXTRACT_STORAGE": "-80C", + "SAMPLE_RESUSPENSION": "150ul CH3OH/H2O", + "ORGAN": "Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid Fetal: Male and female brain, male and female liver" + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted UPLC-MS/MS", + "CHROMATOGRAPHY_TYPE": "Reversed phase", + "INSTRUMENT_NAME": "Waters Acquity", + "COLUMN_NAME": "Waters Acquity HSS T3 (150 x 2.1mm, 1.8um)", + "FLOW_GRADIENT": "0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A", + "FLOW_RATE": "0.15 ml/min", + "SAMPLE_INJECTION": "10ul", + "SOLVENT_A": "Water 0.1% formic acid", + "SOLVENT_B": "CH3CN 0.1 % formic acid", + "ANALYTICAL_TIME": "12 mins" + }, + "ANALYSIS": { + "ANALYSIS_TYPE": "MS", + "LABORATORY_NAME": "Gaikwad Laboratory", + "ACQUISITION_DATE": "41716", + "SOFTWARE_VERSION": "Masslynx", + "OPERATOR_NAME": "Nilesh Gaikwad" + }, + "MS": { + "INSTRUMENT_NAME": "Waters Xevo-TQ", + "INSTRUMENT_TYPE": "Triple quadrupole", + "MS_TYPE": "ESI", + "ION_MODE": "POSITIVE", + "CAPILLARY_VOLTAGE": "3.0 kV", + "COLLISION_GAS": "N2", + "IONIZATION": "Electrospray Ionization", + "SOURCE_TEMPERATURE": "150C", + "DESOLVATION_GAS_FLOW": "600 L/h", + "DESOLVATION_TEMPERATURE": "350C", + "MS_COMMENTS": "UPLC-MS/MS" + }, + "MS_METABOLITE_DATA": { + "Units": "pg/ml", + "Data": [ + { + "Metabolite": "17-hydroxypregnenolone", + "CER030_294717_ML_1": "946.2500", + "CER040_242995_ML_02": "0.0000", + "CER055_249947_ML_3": "676.2500", + "CER055_249947_ML_3": "0.0000", + "CER085_251176_ML_5": "2251.2500", + "CER093_242931_ML_6": "0.0000", + "CER110_238825_ML_7": "0.0000", + "CER120_253690_ML_8": "1134.7500", + 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"CER120_253690_ML_8": "9.0000", + "CER147_254803_ML_9": "19.5000", + "CER149_266689_ML_10": "242.5000", + "CER158_254231_ML_11": "4.0000", + "CER165_287001_ML_12": "0.0000", + "CER178_295145_ML_13": "94.5000", + "CER181_244392_ML_14": "160.7500", + "CER188_250760_ML_15": "0.0000", + "CER192_254091_ML_16": "3214.5000", + "CER201_244193_ML_17": "218.2500", + "CER216_242490_ML_18": "1.0000", + "CER220_274308_ML_19": "0.0000", + "CER223_264067_ML_20": "20.0000", + "CER226_254303_ML_21": "4.5000", + "CER277_255328_ML_22": "55.7500", + "CER287_248530_ML_23": "24.5000", + "CER303_253023_ML_24": "57.0000", + "CER315_282966_ML_25": "200.5000", + "CER324_285069_ML_26": "138.7500", + "CER340_244448_ML_27": "132.2500", + "CER346_246320_ML_28": "120.5000", + "CER356_269662_ML_29": "80.5000", + "CER368_250104_ML_30": "59.5000", + "CER369_276355_ML_31": "315.7500", + "CER384_264971_ML_32": "247.2500", + "CER445_286527_ML_33": "211.5000", + "CER452_240972_ML_34": "198.5000", + "CER463_271249_ML_35": "232.2500", + "CER465_265004_ML_36": "241.0000", + "CER483_294606_ML_37": "199.5000", + "CER488_274343_ML_38": "282.5000", + "CER530_249229_ML_39": "216.5000", + "CER540_240346_ML_40": "358.5000", + "CER552_241945_ML_41": "289.5000", + "CER555_251239_ML_42": "199.2500" + }, + { + "Metabolite": "Testosterone", + "CER030_294717_ML_1": "75.7500", + "CER040_242995_ML_02": "63.2500", + "CER055_249947_ML_3": "42.7500", + "CER055_249947_ML_3": "98.0000", + "CER085_251176_ML_5": "24.2500", + "CER093_242931_ML_6": "35.0000", + "CER110_238825_ML_7": "165.7500", + "CER120_253690_ML_8": "23.2500", + "CER147_254803_ML_9": "73.7500", + "CER149_266689_ML_10": "52.7500", + "CER158_254231_ML_11": "118.7500", + "CER165_287001_ML_12": "35.7500", + "CER178_295145_ML_13": "65.2500", + "CER181_244392_ML_14": "127.2500", + "CER188_250760_ML_15": "14.2500", + "CER192_254091_ML_16": "202.5000", + "CER201_244193_ML_17": "110.7500", + "CER216_242490_ML_18": "53.5000", + "CER220_274308_ML_19": "54.2500", + "CER223_264067_ML_20": "2.2500", + "CER226_254303_ML_21": "105.2500", + "CER277_255328_ML_22": "182.7500", + "CER287_248530_ML_23": "116.0000", + "CER303_253023_ML_24": "66.2500", + "CER315_282966_ML_25": "52.5000", + "CER324_285069_ML_26": "106.2500", + "CER340_244448_ML_27": "43.2500", + "CER346_246320_ML_28": "57.2500", + "CER356_269662_ML_29": "97.2500", + "CER368_250104_ML_30": "16.0000", + "CER369_276355_ML_31": "192.0000", + "CER384_264971_ML_32": "53.7500", + "CER445_286527_ML_33": "182.5000", + "CER452_240972_ML_34": "0.2500", + "CER463_271249_ML_35": "11.5000", + "CER465_265004_ML_36": "87.2500", + "CER483_294606_ML_37": "33.7500", + "CER488_274343_ML_38": "45.5000", + "CER530_249229_ML_39": "26.2500", + "CER540_240346_ML_40": "96.0000", + "CER552_241945_ML_41": "17.5000", + "CER555_251239_ML_42": "79.7500" + }, + { + "Metabolite": "Testosterone", + "CER030_294717_ML_1": "75.7500", + "CER040_242995_ML_02": "63.2500", + "CER055_249947_ML_3": "42.7500", + "CER055_249947_ML_3": 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"CER384_264971_ML_32": "53.7500", + "CER445_286527_ML_33": "182.5000", + "CER452_240972_ML_34": "0.2500", + "CER463_271249_ML_35": "11.5000", + "CER465_265004_ML_36": "87.2500", + "CER483_294606_ML_37": "33.7500", + "CER488_274343_ML_38": "45.5000", + "CER530_249229_ML_39": "26.2500", + "CER540_240346_ML_40": "96.0000", + "CER552_241945_ML_41": "17.5000", + "CER555_251239_ML_42": "79.7500" + }, + { + "Metabolite": "", + "CER030_294717_ML_1": "", + "CER040_242995_ML_02": "", + "CER055_249947_ML_3": "", + "CER055_249947_ML_3": "", + "CER085_251176_ML_5": "", + "CER093_242931_ML_6": "", + "CER110_238825_ML_7": "", + "CER120_253690_ML_8": "", + "CER147_254803_ML_9": "", + "CER149_266689_ML_10": "", + "CER158_254231_ML_11": "123", + "CER165_287001_ML_12": "", + "CER178_295145_ML_13": "", + "CER181_244392_ML_14": "", + "CER188_250760_ML_15": "", + "CER192_254091_ML_16": "", + "CER201_244193_ML_17": "", + "CER216_242490_ML_18": "", + "CER220_274308_ML_19": "", + "CER223_264067_ML_20": "", + "CER226_254303_ML_21": "", + "CER277_255328_ML_22": "", + "CER287_248530_ML_23": "", + "CER303_253023_ML_24": "", + "CER315_282966_ML_25": "", + "CER324_285069_ML_26": "", + "CER340_244448_ML_27": "", + "CER346_246320_ML_28": "", + "CER356_269662_ML_29": "", + "CER368_250104_ML_30": "", + "CER369_276355_ML_31": "", + "CER384_264971_ML_32": "", + "CER445_286527_ML_33": "", + "CER452_240972_ML_34": "", + "CER463_271249_ML_35": "", + "CER465_265004_ML_36": "", + "CER483_294606_ML_37": "", + "CER488_274343_ML_38": "", + "CER530_249229_ML_39": "", + "CER540_240346_ML_40": "", + "CER552_241945_ML_41": "", + "CER555_251239_ML_42": "" + } + ], + "Metabolites": [ + { + "Metabolite": "17-hydroxypregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "91451", + "inchi_key": "", + "kegg_id": "", + "other_id": "2Q4710", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "17-hydroxyprogesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6238", + "inchi_key": "", + "kegg_id": "", + "other_id": "6Q3360", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Allodihydrotestosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "10635", + "inchi_key": "", + "kegg_id": "", + "other_id": "14A2570", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Androstenedione", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6128", + "inchi_key": "", + "kegg_id": "", + "other_id": "12A6030", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Androstenolone (DHEA)", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5881", + "inchi_key": "", + "kegg_id": "", + "other_id": "3A8500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortexolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "440707", + "inchi_key": "", + "kegg_id": "", + "other_id": "7Q1610", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortexone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6166", + "inchi_key": "", + "kegg_id": "", + "other_id": "9Q3460", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Corticosterone, DOC", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5753", + "inchi_key": "", + "kegg_id": "", + "other_id": "10Q1550", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortisol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5754", + "inchi_key": "", + "kegg_id": "", + "other_id": "8Q3880", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Estradiol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5757", + "inchi_key": "", + "kegg_id": "", + "other_id": "16E0950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Estrone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5870", + "inchi_key": "", + "kegg_id": "", + "other_id": "15E2300", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Pregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "8955", + "inchi_key": "", + "kegg_id": "", + "other_id": "1Q5500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Progesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5994", + "inchi_key": "", + "kegg_id": "", + "other_id": "5Q2600", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Testosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6013", + "inchi_key": "", + "kegg_id": "", + "other_id": "13A6950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + } + ] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_data.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_data.txt new file mode 100644 index 0000000..34268b9 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_data.txt @@ -0,0 +1,164 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_02 CER055_249947_ML_3 CER055_249947_ML_3 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 + 123 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_names.json b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_names.json new file mode 100644 index 0000000..816093f --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_names.json @@ -0,0 +1,1261 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "-", + "NUM_GROUPS": "NA" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", + 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"Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER555_251239_ML_42", + "Sample ID": "CER555_251239_ML_42", + "Factors": { + "Tissue/Fluid": "Serum" + } + } + ], + "COLLECTION": { + "COLLECTION_SUMMARY": "-" + }, + "TREATMENT": { + "TREATMENT_SUMMARY": "-" + }, + "SAMPLEPREP": { + "SAMPLEPREP_SUMMARY": "Methanol: Water Extraction", + "SAMPLEPREP_PROTOCOL_FILENAME": "NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx", + "PROCESSING_METHOD": "Homogenization and Solvent Removal w/ Speed Vac", + "PROCESSING_STORAGE_CONDITIONS": "On Ice", + "EXTRACTION_METHOD": "1:1 Methanol: Water", + "EXTRACT_STORAGE": "-80C", + "SAMPLE_RESUSPENSION": "150ul CH3OH/H2O", + "ORGAN": "Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid Fetal: Male and female brain, male and female liver" + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted UPLC-MS/MS", + "CHROMATOGRAPHY_TYPE": "Reversed phase", + "INSTRUMENT_NAME": "Waters Acquity", + "COLUMN_NAME": "Waters 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"moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "10635", + "inchi_key": "", + "kegg_id": "", + "other_id": "14A2570", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Androstenedione", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6128", + "inchi_key": "", + "kegg_id": "", + "other_id": "12A6030", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Androstenolone (DHEA)", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5881", + "inchi_key": "", + "kegg_id": "", + "other_id": "3A8500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortexolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "440707", + "inchi_key": "", + "kegg_id": "", + "other_id": "7Q1610", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortexone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6166", + "inchi_key": "", + "kegg_id": "", + "other_id": "9Q3460", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Corticosterone, DOC", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5753", + "inchi_key": "", + "kegg_id": "", + "other_id": "10Q1550", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortisol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5754", + "inchi_key": "", + "kegg_id": "", + "other_id": "8Q3880", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Estradiol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5757", + "inchi_key": "", + "kegg_id": "", + "other_id": "16E0950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Estrone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5870", + "inchi_key": "", + "kegg_id": "", + "other_id": "15E2300", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Pregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "8955", + "inchi_key": "", + "kegg_id": "", + "other_id": "1Q5500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Progesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5994", + "inchi_key": "", + "kegg_id": "", + "other_id": "5Q2600", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Testosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6013", + "inchi_key": "", + "kegg_id": "", + "other_id": "13A6950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "factors", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "", + "inchi_key": "", + "kegg_id": "", + "other_id": "", + "other_id_type": "" + } + ], + "Extended": [ + { + "Metabolite": "IMF1", + "assigned_mass": "[518.13104102, 513.17564587]", + "assigned_mass%type": "adduct subtracted", + "assigned_mass%units": "daltons", + "assignment%type": "['SMIRFE', 'voted']", + "assignment_scores": "[0.99991183, 'NA']", + "expectation_values": "[8.817e-05, 'NA']", + "raw_mz": "536.16541515", + "raw_mz%units": "daltons/charge", + "raw_mz_sd": "2.412e-05", + "sample_id": "CER040_242995_ML_2" + }, + { + "Metabolite": "metabolite_name", + "assigned_mass": "", + "assigned_mass%type": "", + "assigned_mass%units": "", + "assignment%type": "", + "assignment_scores": "", + "expectation_values": "", + "raw_mz": "", + "raw_mz%units": "", + "raw_mz_sd": "", + "sample_id": "" + } + ] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_names.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_names.txt new file mode 100644 index 0000000..881bf4b --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_names.txt @@ -0,0 +1,169 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +samples +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +factors +METABOLITES_END +EXTENDED_MS_METABOLITE_DATA_START +metabolite_name assigned_mass assigned_mass%type assigned_mass%units assignment%type assignment_scores expectation_values raw_mz raw_mz%units raw_mz_sd sample_id +IMF1 [518.13104102, 513.17564587] adduct subtracted daltons ['SMIRFE', 'voted'] [0.99991183, 'NA'] [8.817e-05, 'NA'] 536.16541515 daltons/charge 2.412e-05 CER040_242995_ML_2 +metabolite_name +EXTENDED_MS_METABOLITE_DATA_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_metabolites.json b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolites.json new file mode 100644 index 0000000..e5b3c12 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolites.json @@ -0,0 +1,1215 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "-", + "NUM_GROUPS": "NA" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER340_244448_ML_27", + "Sample ID": "CER340_244448_ML_27", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER346_246320_ML_28", + "Sample ID": "CER346_246320_ML_28", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER356_269662_ML_29", + "Sample ID": "CER356_269662_ML_29", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER368_250104_ML_30", + "Sample ID": "CER368_250104_ML_30", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER369_276355_ML_31", + "Sample ID": "CER369_276355_ML_31", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER384_264971_ML_32", + "Sample ID": "CER384_264971_ML_32", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER445_286527_ML_33", + "Sample ID": "CER445_286527_ML_33", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER452_240972_ML_34", + "Sample ID": "CER452_240972_ML_34", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER463_271249_ML_35", + "Sample ID": "CER463_271249_ML_35", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER465_265004_ML_36", + "Sample ID": "CER465_265004_ML_36", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER483_294606_ML_37", + "Sample ID": "CER483_294606_ML_37", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER488_274343_ML_38", + "Sample ID": "CER488_274343_ML_38", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER530_249229_ML_39", + "Sample ID": "CER530_249229_ML_39", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER540_240346_ML_40", + "Sample ID": "CER540_240346_ML_40", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER552_241945_ML_41", + "Sample ID": "CER552_241945_ML_41", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER555_251239_ML_42", + "Sample ID": "CER555_251239_ML_42", + "Factors": { + "Tissue/Fluid": "Serum" + } + } + ], + "COLLECTION": { + "COLLECTION_SUMMARY": "-" + }, + "TREATMENT": { + "TREATMENT_SUMMARY": "-" + }, + "SAMPLEPREP": { + "SAMPLEPREP_SUMMARY": "Methanol: Water Extraction", + "SAMPLEPREP_PROTOCOL_FILENAME": "NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx", + "PROCESSING_METHOD": "Homogenization and Solvent Removal w/ Speed Vac", + "PROCESSING_STORAGE_CONDITIONS": "On Ice", + "EXTRACTION_METHOD": "1:1 Methanol: Water", + "EXTRACT_STORAGE": "-80C", + "SAMPLE_RESUSPENSION": "150ul CH3OH/H2O", + "ORGAN": "Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid Fetal: Male and female brain, male and female liver" + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted UPLC-MS/MS", + "CHROMATOGRAPHY_TYPE": "Reversed phase", + "INSTRUMENT_NAME": "Waters Acquity", + "COLUMN_NAME": "Waters Acquity HSS T3 (150 x 2.1mm, 1.8um)", + "FLOW_GRADIENT": "0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A", + "FLOW_RATE": "0.15 ml/min", + "SAMPLE_INJECTION": "10ul", + "SOLVENT_A": "Water 0.1% formic acid", + "SOLVENT_B": "CH3CN 0.1 % formic acid", + "ANALYTICAL_TIME": "12 mins" + }, + "ANALYSIS": { + "ANALYSIS_TYPE": "MS", + "LABORATORY_NAME": "Gaikwad Laboratory", + "ACQUISITION_DATE": "41716", + "SOFTWARE_VERSION": "Masslynx", + "OPERATOR_NAME": "Nilesh Gaikwad" + }, + "MS": { + "INSTRUMENT_NAME": "Waters Xevo-TQ", + "INSTRUMENT_TYPE": "Triple quadrupole", + "MS_TYPE": "ESI", + "ION_MODE": "POSITIVE", + "CAPILLARY_VOLTAGE": "3.0 kV", + "COLLISION_GAS": "N2", + "IONIZATION": "Electrospray Ionization", + "SOURCE_TEMPERATURE": "150C", + "DESOLVATION_GAS_FLOW": "600 L/h", + "DESOLVATION_TEMPERATURE": "350C", + "MS_COMMENTS": "UPLC-MS/MS" + }, + "MS_METABOLITE_DATA": { + "Units": "pg/ml", + "Data": [ + { + "Metabolite": "17-hydroxypregnenolone", + "CER030_294717_ML_1": "946.2500", + "CER040_242995_ML_2": "0.0000", + "CER055_249947_ML_3": "676.2500", + "CER062_246153_ML_4": "0.0000", + "CER085_251176_ML_5": "2251.2500", + 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"CER223_264067_ML_20": "13.7500", + "CER226_254303_ML_21": "0.0000", + "CER277_255328_ML_22": "0.0000", + "CER287_248530_ML_23": "0.0000", + "CER303_253023_ML_24": "0.0000", + "CER315_282966_ML_25": "0.0000", + "CER324_285069_ML_26": "0.0000", + "CER340_244448_ML_27": "0.0000", + "CER346_246320_ML_28": "0.0000", + "CER356_269662_ML_29": "0.0000", + "CER368_250104_ML_30": "0.0000", + "CER369_276355_ML_31": "488.5000", + "CER384_264971_ML_32": "0.0000", + "CER445_286527_ML_33": "0.0000", + "CER452_240972_ML_34": "0.0000", + "CER463_271249_ML_35": "0.0000", + "CER465_265004_ML_36": "280.7500", + "CER483_294606_ML_37": "0.0000", + "CER488_274343_ML_38": "0.0000", + "CER530_249229_ML_39": "0.0000", + "CER540_240346_ML_40": "0.0000", + "CER552_241945_ML_41": "0.0000", + "CER555_251239_ML_42": "205.5000" + }, + { + "Metabolite": "Progesterone", + "CER030_294717_ML_1": "28.2500", + "CER040_242995_ML_2": "6.2500", + "CER055_249947_ML_3": "725.2500", + "CER062_246153_ML_4": "57.2500", + "CER085_251176_ML_5": "767.0000", + "CER093_242931_ML_6": "2.7500", + "CER110_238825_ML_7": "388.0000", + "CER120_253690_ML_8": "9.0000", + "CER147_254803_ML_9": "19.5000", + "CER149_266689_ML_10": "242.5000", + "CER158_254231_ML_11": "4.0000", + "CER165_287001_ML_12": "0.0000", + "CER178_295145_ML_13": "94.5000", + "CER181_244392_ML_14": "160.7500", + "CER188_250760_ML_15": "0.0000", + "CER192_254091_ML_16": "3214.5000", + "CER201_244193_ML_17": "218.2500", + "CER216_242490_ML_18": "1.0000", + "CER220_274308_ML_19": "0.0000", + "CER223_264067_ML_20": "20.0000", + "CER226_254303_ML_21": "4.5000", + "CER277_255328_ML_22": "55.7500", + "CER287_248530_ML_23": "24.5000", + "CER303_253023_ML_24": "57.0000", + "CER315_282966_ML_25": "200.5000", + "CER324_285069_ML_26": "138.7500", + "CER340_244448_ML_27": "132.2500", + "CER346_246320_ML_28": "120.5000", + "CER356_269662_ML_29": "80.5000", + "CER368_250104_ML_30": "59.5000", + "CER369_276355_ML_31": "315.7500", + "CER384_264971_ML_32": "247.2500", + "CER445_286527_ML_33": "211.5000", + "CER452_240972_ML_34": "198.5000", + "CER463_271249_ML_35": "232.2500", + "CER465_265004_ML_36": "241.0000", + "CER483_294606_ML_37": "199.5000", + "CER488_274343_ML_38": "282.5000", + "CER530_249229_ML_39": "216.5000", + "CER540_240346_ML_40": "358.5000", + "CER552_241945_ML_41": "289.5000", + "CER555_251239_ML_42": "199.2500" + }, + { + "Metabolite": "Testosterone", + "CER030_294717_ML_1": "75.7500", + "CER040_242995_ML_2": "63.2500", + "CER055_249947_ML_3": "42.7500", + "CER062_246153_ML_4": "98.0000", + "CER085_251176_ML_5": "24.2500", + "CER093_242931_ML_6": "35.0000", + "CER110_238825_ML_7": "165.7500", + "CER120_253690_ML_8": "23.2500", + "CER147_254803_ML_9": "73.7500", + "CER149_266689_ML_10": "52.7500", + "CER158_254231_ML_11": "118.7500", + "CER165_287001_ML_12": "35.7500", + "CER178_295145_ML_13": "65.2500", + "CER181_244392_ML_14": "127.2500", + "CER188_250760_ML_15": "14.2500", + "CER192_254091_ML_16": "202.5000", + "CER201_244193_ML_17": "110.7500", + "CER216_242490_ML_18": "53.5000", + "CER220_274308_ML_19": "54.2500", + "CER223_264067_ML_20": "2.2500", + "CER226_254303_ML_21": "105.2500", + "CER277_255328_ML_22": "182.7500", + "CER287_248530_ML_23": "116.0000", + "CER303_253023_ML_24": "66.2500", + "CER315_282966_ML_25": "52.5000", + "CER324_285069_ML_26": "106.2500", + "CER340_244448_ML_27": "43.2500", + "CER346_246320_ML_28": "57.2500", + "CER356_269662_ML_29": "97.2500", + "CER368_250104_ML_30": "16.0000", + "CER369_276355_ML_31": "192.0000", + "CER384_264971_ML_32": "53.7500", + "CER445_286527_ML_33": "182.5000", + "CER452_240972_ML_34": "0.2500", + "CER463_271249_ML_35": "11.5000", + "CER465_265004_ML_36": "87.2500", + "CER483_294606_ML_37": "33.7500", + "CER488_274343_ML_38": "45.5000", + "CER530_249229_ML_39": "26.2500", + "CER540_240346_ML_40": "96.0000", + "CER552_241945_ML_41": "17.5000", + "CER555_251239_ML_42": "79.7500" + } + ], + "Metabolites": [ + { + "Metabolite": "17-hydroxypregnenolone", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "asdf", + "inchi_key": "", + "kegg_id": "", + "other_id": "2Q4710", + "other_id_type": "", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "17-hydroxyprogesterone", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "6238", + "inchi_key": "", + "kegg_id": "", + "other_id": "6Q3360", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Allodihydrotestosterone", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "10635", + "inchi_key": "", + "kegg_id": "", + "other_id": "14A2570", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Androstenedione", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "6128", + "inchi_key": "", + "kegg_id": "", + "other_id": "12A6030", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Androstenolone (DHEA)", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "5881", + "inchi_key": "", + "kegg_id": "", + "other_id": "3A8500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Cortexolone", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "440707", + "inchi_key": "", + "kegg_id": "", + "other_id": "7Q1610", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Cortexone", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "6166", + "inchi_key": "", + "kegg_id": "", + "other_id": "9Q3460", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Corticosterone, DOC", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "5753", + "inchi_key": "", + "kegg_id": "", + "other_id": "10Q1550", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Cortisol", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "5754", + "inchi_key": "", + "kegg_id": "", + "other_id": "8Q3880", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Estradiol", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "5757", + "inchi_key": "", + "kegg_id": "", + "other_id": "16E0950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Estrone", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "5870", + "inchi_key": "", + "kegg_id": "", + "other_id": "15E2300", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Pregnenolone", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "8955", + "inchi_key": "", + "kegg_id": "", + "other_id": "1Q5500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Progesterone", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "5994", + "inchi_key": "", + "kegg_id": "", + "other_id": "5Q2600", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Testosterone", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "6013", + "inchi_key": "", + "kegg_id": "", + "other_id": "13A6950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "Testosterone", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "6013", + "inchi_key": "", + "kegg_id": "", + "other_id": "13A6950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "pubchem_id/hmdb_id": "" + }, + { + "Metabolite": "", + "moverz_quant": "", + "ri": "", + "qwer": "", + "pubchem_id": "1234", + "inchi_key": "", + "kegg_id": "", + "other_id": "", + "other_id_type": "", + "pubchem_id/hmdb_id": "" + } + ] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_metabolites.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolites.txt new file mode 100644 index 0000000..1e6179d --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_metabolites.txt @@ -0,0 +1,164 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri qwer pubchem_id inchi_key kegg_id other_id other_id_type pubchem_id/hmdb_id +17-hydroxypregnenolone asdf 2Q4710 +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID + 1234 +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_passing.json b/tests/example_data/validation_files/ST000122_AN000204_validate_passing.json new file mode 100644 index 0000000..197df47 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_passing.json @@ -0,0 +1,1122 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "asdf", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "asdf", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "asdf", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "asdf", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "asdf", + "NUM_GROUPS": "2" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER340_244448_ML_27", + "Sample ID": "CER340_244448_ML_27", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER346_246320_ML_28", + "Sample ID": "CER346_246320_ML_28", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER356_269662_ML_29", + "Sample ID": "CER356_269662_ML_29", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER368_250104_ML_30", + "Sample ID": "CER368_250104_ML_30", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER369_276355_ML_31", + "Sample ID": "CER369_276355_ML_31", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER384_264971_ML_32", + "Sample ID": "CER384_264971_ML_32", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER445_286527_ML_33", + "Sample ID": "CER445_286527_ML_33", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER452_240972_ML_34", + "Sample ID": "CER452_240972_ML_34", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER463_271249_ML_35", + "Sample ID": "CER463_271249_ML_35", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER465_265004_ML_36", + "Sample ID": "CER465_265004_ML_36", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER483_294606_ML_37", + "Sample ID": "CER483_294606_ML_37", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER488_274343_ML_38", + "Sample ID": "CER488_274343_ML_38", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER530_249229_ML_39", + "Sample ID": "CER530_249229_ML_39", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER540_240346_ML_40", + "Sample ID": "CER540_240346_ML_40", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER552_241945_ML_41", + "Sample ID": "CER552_241945_ML_41", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER555_251239_ML_42", + "Sample ID": "CER555_251239_ML_42", + "Factors": { + "Tissue/Fluid": "Serum" + } + } + ], + "COLLECTION": { + "COLLECTION_SUMMARY": "asdf" + }, + "TREATMENT": { + "TREATMENT_SUMMARY": "asdf" + }, + "SAMPLEPREP": { + "SAMPLEPREP_SUMMARY": "Methanol: Water Extraction", + "SAMPLEPREP_PROTOCOL_FILENAME": "NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx", + "PROCESSING_METHOD": "Homogenization and Solvent Removal w/ Speed Vac", + "PROCESSING_STORAGE_CONDITIONS": "On Ice", + "EXTRACTION_METHOD": "1:1 Methanol: Water", + "EXTRACT_STORAGE": "-80C", + "SAMPLE_RESUSPENSION": "150ul CH3OH/H2O", + "ORGAN": "Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid Fetal: Male and female brain, male and female liver" + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted UPLC-MS/MS", + "CHROMATOGRAPHY_TYPE": "Reversed phase", + "INSTRUMENT_NAME": "Waters Acquity", + "COLUMN_NAME": "Waters Acquity HSS T3 (150 x 2.1mm, 1.8um)", + "FLOW_GRADIENT": "0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A", + "FLOW_RATE": "0.15 mL/min", + "SAMPLE_INJECTION": "10 uL", + "SOLVENT_A": "Water 0.1% formic acid", + "SOLVENT_B": "CH3CN 0.1 % formic acid", + "ANALYTICAL_TIME": "12 min", + "COLUMN_TEMPERATURE": "150 C" + }, + "ANALYSIS": { + "ANALYSIS_TYPE": "MS", + "LABORATORY_NAME": "Gaikwad Laboratory", + "ACQUISITION_DATE": "41716", + "SOFTWARE_VERSION": "Masslynx", + "OPERATOR_NAME": "Nilesh Gaikwad" + }, + "MS": { + "INSTRUMENT_NAME": "Waters Xevo-TQ", + "INSTRUMENT_TYPE": "Triple quadrupole", + "MS_TYPE": "ESI", + "ION_MODE": "POSITIVE", + "CAPILLARY_VOLTAGE": "3.0 kV", + "COLLISION_GAS": "Nitrogen", + "IONIZATION": "Electrospray Ionization", + "SOURCE_TEMPERATURE": "150 C", + "DESOLVATION_GAS_FLOW": "600 L/hr", + "DESOLVATION_TEMPERATURE": "350 C", + "MS_COMMENTS": "UPLC-MS/MS" + }, + "MS_METABOLITE_DATA": { + "Units": "pg/ml", + "Data": [ + { + "Metabolite": "17-hydroxypregnenolone", + "CER030_294717_ML_1": "946.2500", + "CER040_242995_ML_2": "0.0000", + "CER055_249947_ML_3": "676.2500", + "CER062_246153_ML_4": 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"CER555_251239_ML_42": "19662.5000" + }, + { + "Metabolite": "Estradiol", + "CER030_294717_ML_1": "123992.2500", + "CER040_242995_ML_2": "796595.7500", + "CER055_249947_ML_3": "619110.0000", + "CER062_246153_ML_4": "449415.7500", + "CER085_251176_ML_5": "320835.5000", + "CER093_242931_ML_6": "326124.2500", + "CER110_238825_ML_7": "249087.2500", + "CER120_253690_ML_8": "311589.2500", + "CER147_254803_ML_9": "345598.5000", + "CER149_266689_ML_10": "485857.0000", + "CER158_254231_ML_11": "332055.2500", + "CER165_287001_ML_12": "211831.0000", + "CER178_295145_ML_13": "334929.7500", + "CER181_244392_ML_14": "235466.7500", + "CER188_250760_ML_15": "352555.0000", + "CER192_254091_ML_16": "410500.0000", + "CER201_244193_ML_17": "887955.0000", + "CER216_242490_ML_18": "865791.7500", + "CER220_274308_ML_19": "1648163.5000", + "CER223_264067_ML_20": "856726.7500", + "CER226_254303_ML_21": "579044.2500", + "CER277_255328_ML_22": "254013.2500", + "CER287_248530_ML_23": "326272.7500", + "CER303_253023_ML_24": "239893.7500", + "CER315_282966_ML_25": "329553.2500", + "CER324_285069_ML_26": "438715.5000", + "CER340_244448_ML_27": "248489.0000", + "CER346_246320_ML_28": "380251.0000", + "CER356_269662_ML_29": "338965.5000", + "CER368_250104_ML_30": "337231.2500", + "CER369_276355_ML_31": "342754.5000", + "CER384_264971_ML_32": "370657.2500", + "CER445_286527_ML_33": "2028106.5000", + "CER452_240972_ML_34": "733521.0000", + "CER463_271249_ML_35": "399244.2500", + "CER465_265004_ML_36": "321007.5000", + "CER483_294606_ML_37": "634463.0000", + "CER488_274343_ML_38": "231294.0000", + "CER530_249229_ML_39": "349439.2500", + "CER540_240346_ML_40": "75746.7500", + "CER552_241945_ML_41": "399415.5000", + "CER555_251239_ML_42": "303855.7500" + }, + { + "Metabolite": "Estrone", + "CER030_294717_ML_1": "484.5000", + "CER040_242995_ML_2": "1663.7500", + "CER055_249947_ML_3": "1680.7500", + "CER062_246153_ML_4": "794.5000", + "CER085_251176_ML_5": "557.2500", + "CER093_242931_ML_6": "625.7500", + "CER110_238825_ML_7": "669.7500", + "CER120_253690_ML_8": "885.0000", + "CER147_254803_ML_9": "715.0000", + "CER149_266689_ML_10": "1225.5000", + "CER158_254231_ML_11": "697.7500", + "CER165_287001_ML_12": "478.2500", + "CER178_295145_ML_13": "659.0000", + "CER181_244392_ML_14": "575.5000", + "CER188_250760_ML_15": "871.7500", + "CER192_254091_ML_16": "1089.0000", + "CER201_244193_ML_17": "1726.2500", + "CER216_242490_ML_18": "2325.2500", + "CER220_274308_ML_19": "3286.7500", + "CER223_264067_ML_20": "1955.7500", + "CER226_254303_ML_21": "1094.0000", + "CER277_255328_ML_22": "486.2500", + "CER287_248530_ML_23": "650.5000", + "CER303_253023_ML_24": "574.2500", + "CER315_282966_ML_25": "601.7500", + "CER324_285069_ML_26": "842.7500", + "CER340_244448_ML_27": "757.7500", + "CER346_246320_ML_28": "732.7500", + "CER356_269662_ML_29": "571.7500", + "CER368_250104_ML_30": "693.7500", + "CER369_276355_ML_31": "1004.2500", + "CER384_264971_ML_32": "879.2500", + "CER445_286527_ML_33": "3154.7500", + "CER452_240972_ML_34": "1095.2500", + "CER463_271249_ML_35": "22680.2500", + "CER465_265004_ML_36": "637.2500", + "CER483_294606_ML_37": "1108.2500", + "CER488_274343_ML_38": "474.2500", + "CER530_249229_ML_39": "810.2500", + "CER540_240346_ML_40": "421.2500", + "CER552_241945_ML_41": "680.7500", + "CER555_251239_ML_42": "623.7500" + }, + { + "Metabolite": "Pregnenolone", + "CER030_294717_ML_1": "12.2500", + "CER040_242995_ML_2": "0.0000", + "CER055_249947_ML_3": "0.0000", + "CER062_246153_ML_4": "0.0000", + "CER085_251176_ML_5": "0.0000", + "CER093_242931_ML_6": "144.2500", + "CER110_238825_ML_7": "14.7500", + "CER120_253690_ML_8": "807.2500", + "CER147_254803_ML_9": "0.0000", + "CER149_266689_ML_10": "30.0000", + "CER158_254231_ML_11": "0.0000", + "CER165_287001_ML_12": "0.0000", + "CER178_295145_ML_13": "0.0000", + "CER181_244392_ML_14": "0.0000", + "CER188_250760_ML_15": "16.5000", + "CER192_254091_ML_16": "139.5000", + "CER201_244193_ML_17": 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"CER368_250104_ML_30": "59.5000", + "CER369_276355_ML_31": "315.7500", + "CER384_264971_ML_32": "247.2500", + "CER445_286527_ML_33": "211.5000", + "CER452_240972_ML_34": "198.5000", + "CER463_271249_ML_35": "232.2500", + "CER465_265004_ML_36": "241.0000", + "CER483_294606_ML_37": "199.5000", + "CER488_274343_ML_38": "282.5000", + "CER530_249229_ML_39": "216.5000", + "CER540_240346_ML_40": "358.5000", + "CER552_241945_ML_41": "289.5000", + "CER555_251239_ML_42": "199.2500" + }, + { + "Metabolite": "Testosterone", + "CER030_294717_ML_1": "75.7500", + "CER040_242995_ML_2": "63.2500", + "CER055_249947_ML_3": "42.7500", + "CER062_246153_ML_4": "98.0000", + "CER085_251176_ML_5": "24.2500", + "CER093_242931_ML_6": "35.0000", + "CER110_238825_ML_7": "165.7500", + "CER120_253690_ML_8": "23.2500", + "CER147_254803_ML_9": "73.7500", + "CER149_266689_ML_10": "52.7500", + "CER158_254231_ML_11": "118.7500", + "CER165_287001_ML_12": "35.7500", + "CER178_295145_ML_13": "65.2500", + "CER181_244392_ML_14": "127.2500", + "CER188_250760_ML_15": "14.2500", + "CER192_254091_ML_16": "202.5000", + "CER201_244193_ML_17": "110.7500", + "CER216_242490_ML_18": "53.5000", + "CER220_274308_ML_19": "54.2500", + "CER223_264067_ML_20": "2.2500", + "CER226_254303_ML_21": "105.2500", + "CER277_255328_ML_22": "182.7500", + "CER287_248530_ML_23": "116.0000", + "CER303_253023_ML_24": "66.2500", + "CER315_282966_ML_25": "52.5000", + "CER324_285069_ML_26": "106.2500", + "CER340_244448_ML_27": "43.2500", + "CER346_246320_ML_28": "57.2500", + "CER356_269662_ML_29": "97.2500", + "CER368_250104_ML_30": "16.0000", + "CER369_276355_ML_31": "192.0000", + "CER384_264971_ML_32": "53.7500", + "CER445_286527_ML_33": "182.5000", + "CER452_240972_ML_34": "0.2500", + "CER463_271249_ML_35": "11.5000", + "CER465_265004_ML_36": "87.2500", + "CER483_294606_ML_37": "33.7500", + "CER488_274343_ML_38": "45.5000", + "CER530_249229_ML_39": "26.2500", + "CER540_240346_ML_40": "96.0000", + "CER552_241945_ML_41": "17.5000", + "CER555_251239_ML_42": "79.7500" + } + ], + "Metabolites": [ + { + "Metabolite": "17-hydroxypregnenolone", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "91451" + }, + { + "Metabolite": "17-hydroxyprogesterone", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "6238" + }, + { + "Metabolite": "Allodihydrotestosterone", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "10635" + }, + { + "Metabolite": "Androstenedione", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "6128" + }, + { + "Metabolite": "Androstenolone (DHEA)", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "5881" + }, + { + "Metabolite": "Cortexolone", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "440707" + }, + { + "Metabolite": "Cortexone", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "6166" + }, + { + "Metabolite": "Corticosterone, DOC", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "5753" + }, + { + "Metabolite": "Cortisol", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "5754" + }, + { + "Metabolite": "Estradiol", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "5757" + }, + { + "Metabolite": "Estrone", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "5870" + }, + { + "Metabolite": "Pregnenolone", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "8955" + }, + { + "Metabolite": "Progesterone", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "5994" + }, + { + "Metabolite": "Testosterone", + "moverz_quant": "123", + "retention_index": "345", + "retention_index_type": "asdf", + "pubchem_id": "6013" + } + ] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_passing.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_passing.txt new file mode 100644 index 0000000..ec53e45 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_passing.txt @@ -0,0 +1,163 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY asdf +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS asdf +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY asdf +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS asdf +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE asdf +ST:NUM_GROUPS 2 +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY asdf +#TREATMENT +TR:TREATMENT_SUMMARY asdf +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 mL/min +CH:SAMPLE_INJECTION 10 uL +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 min +CH:COLUMN_TEMPERATURE 150 C +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS Nitrogen +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150 C +MS:DESOLVATION_GAS_FLOW 600 L/hr +MS:DESOLVATION_TEMPERATURE 350 C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone, DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant retention_index retention_index_type pubchem_id +17-hydroxypregnenolone 123 345 asdf 91451 +17-hydroxyprogesterone 123 345 asdf 6238 +Allodihydrotestosterone 123 345 asdf 10635 +Androstenedione 123 345 asdf 6128 +Androstenolone (DHEA) 123 345 asdf 5881 +Cortexolone 123 345 asdf 440707 +Cortexone 123 345 asdf 6166 +Corticosterone, DOC 123 345 asdf 5753 +Cortisol 123 345 asdf 5754 +Estradiol 123 345 asdf 5757 +Estrone 123 345 asdf 5870 +Pregnenolone 123 345 asdf 8955 +Progesterone 123 345 asdf 5994 +Testosterone 123 345 asdf 6013 +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_polarity.json b/tests/example_data/validation_files/ST000122_AN000204_validate_polarity.json new file mode 100644 index 0000000..2cfa996 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_polarity.json @@ -0,0 +1,1191 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "-", + "NUM_GROUPS": "NA" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER340_244448_ML_27", + "Sample ID": "CER340_244448_ML_27", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER346_246320_ML_28", + "Sample ID": "CER346_246320_ML_28", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER356_269662_ML_29", + "Sample ID": "CER356_269662_ML_29", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER368_250104_ML_30", + "Sample ID": "CER368_250104_ML_30", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER369_276355_ML_31", + "Sample ID": "CER369_276355_ML_31", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER384_264971_ML_32", + "Sample ID": "CER384_264971_ML_32", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER445_286527_ML_33", + "Sample ID": "CER445_286527_ML_33", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER452_240972_ML_34", + "Sample ID": "CER452_240972_ML_34", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER463_271249_ML_35", + "Sample ID": "CER463_271249_ML_35", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER465_265004_ML_36", + "Sample ID": "CER465_265004_ML_36", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER483_294606_ML_37", + "Sample ID": "CER483_294606_ML_37", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER488_274343_ML_38", + "Sample ID": "CER488_274343_ML_38", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER530_249229_ML_39", + "Sample ID": "CER530_249229_ML_39", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER540_240346_ML_40", + "Sample ID": "CER540_240346_ML_40", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER552_241945_ML_41", + "Sample ID": "CER552_241945_ML_41", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER555_251239_ML_42", + "Sample ID": "CER555_251239_ML_42", + "Factors": { + "Tissue/Fluid": "Serum" + } + } + ], + "COLLECTION": { + "COLLECTION_SUMMARY": "-" + }, + "TREATMENT": { + 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"Metabolite": "Androstenolone (DHEA)", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5881", + "inchi_key": "", + "kegg_id": "", + "other_id": "3A8500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "polarity": "" + }, + { + "Metabolite": "Cortexolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "440707", + "inchi_key": "", + "kegg_id": "", + "other_id": "7Q1610", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "polarity": "" + }, + { + "Metabolite": "Cortexone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6166", + "inchi_key": "", + "kegg_id": "", + "other_id": "9Q3460", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "polarity": "" + }, + { + "Metabolite": "Corticosterone, DOC", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5753", + "inchi_key": "", + "kegg_id": "", + "other_id": "10Q1550", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "polarity": "" + }, + { + "Metabolite": "Cortisol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5754", + "inchi_key": "", + "kegg_id": "", + "other_id": "8Q3880", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "polarity": "" + }, + { + "Metabolite": "Estradiol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5757", + "inchi_key": "", + "kegg_id": "", + "other_id": "16E0950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "polarity": "" + }, + { + "Metabolite": "Estrone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5870", + "inchi_key": "", + "kegg_id": "", + "other_id": "15E2300", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "polarity": "" + }, + { + "Metabolite": "Pregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "8955", + "inchi_key": "", + "kegg_id": "", + "other_id": "1Q5500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "polarity": "" + }, + { + "Metabolite": "Progesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5994", + "inchi_key": "", + "kegg_id": "", + "other_id": "5Q2600", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "polarity": "" + }, + { + "Metabolite": "Testosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6013", + "inchi_key": "", + "kegg_id": "", + "other_id": "13A6950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "polarity": "" + } + ] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_polarity.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_polarity.txt new file mode 100644 index 0000000..fbdd182 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_polarity.txt @@ -0,0 +1,162 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type polarity +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID positive +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID negative +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_ssf.json b/tests/example_data/validation_files/ST000122_AN000204_validate_ssf.json new file mode 100644 index 0000000..73f7ade --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_ssf.json @@ -0,0 +1,1182 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "-", + "NUM_GROUPS": "NA" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum", + "Tissue/Fluid": "asdf" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + }, + "Additional sample data": { + "key1": "asdf", + "key1": "qewr" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": 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"182.5000", + "CER452_240972_ML_34": "0.2500", + "CER463_271249_ML_35": "11.5000", + "CER465_265004_ML_36": "87.2500", + "CER483_294606_ML_37": "33.7500", + "CER488_274343_ML_38": "45.5000", + "CER530_249229_ML_39": "26.2500", + "CER540_240346_ML_40": "96.0000", + "CER552_241945_ML_41": "17.5000", + "CER555_251239_ML_42": "79.7500" + } + ], + "Metabolites": [ + { + "Metabolite": "17-hydroxypregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "91451", + "inchi_key": "", + "kegg_id": "", + "other_id": "2Q4710", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "17-hydroxyprogesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6238", + "inchi_key": "", + "kegg_id": "", + "other_id": "6Q3360", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Allodihydrotestosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "10635", + "inchi_key": "", + "kegg_id": "", + "other_id": "14A2570", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Androstenedione", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6128", + "inchi_key": "", + "kegg_id": "", + "other_id": "12A6030", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Androstenolone (DHEA)", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5881", + "inchi_key": "", + "kegg_id": "", + "other_id": "3A8500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortexolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "440707", + "inchi_key": "", + "kegg_id": "", + "other_id": "7Q1610", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortexone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6166", + "inchi_key": "", + "kegg_id": "", + "other_id": "9Q3460", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Corticosterone, DOC", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5753", + "inchi_key": "", + "kegg_id": "", + "other_id": "10Q1550", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Cortisol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5754", + "inchi_key": "", + "kegg_id": "", + "other_id": "8Q3880", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Estradiol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5757", + "inchi_key": "", + "kegg_id": "", + "other_id": "16E0950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Estrone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5870", + "inchi_key": "", + "kegg_id": "", + "other_id": "15E2300", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Pregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "8955", + "inchi_key": "", + "kegg_id": "", + "other_id": "1Q5500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Progesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5994", + "inchi_key": "", + "kegg_id": "", + "other_id": "5Q2600", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + }, + { + "Metabolite": "Testosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6013", + "inchi_key": "", + "kegg_id": "", + "other_id": "13A6950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID" + } + ] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_ssf.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_ssf.txt new file mode 100644 index 0000000..cb8f086 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_ssf.txt @@ -0,0 +1,162 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum | Tissue/Fluid:asdf +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum key1=asdf; key1=qewr +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_error_4.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_subsection_uniqueness.txt similarity index 98% rename from tests/example_data/validation_files/ST000122_AN000204_error_4.txt rename to tests/example_data/validation_files/ST000122_AN000204_validate_subsection_uniqueness.txt index 7eda281..b4474d3 100644 --- a/tests/example_data/validation_files/ST000122_AN000204_error_4.txt +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_subsection_uniqueness.txt @@ -1,161 +1,163 @@ -#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 -VERSION 1 -CREATED_ON 2016-09-17 -#PROJECT -PR:PROJECT_TYPE Pilot and Feasibility Projects -PR:PROJECT_SUMMARY - -PR:INSTITUTE University of California, Davis -PR:DEPARTMENT Nutrition -PR:LABORATORY Gaikwad Lab -PR:LAST_NAME Gaikwad -PR:FIRST_NAME Nilesh -PR:ADDRESS - -PR:EMAIL nwgaikwad@ucdavis.edu -PR:PHONE 530-752-2906 -PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL -PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) -#STUDY -ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic -ST:STUDY_TYPE steroid panel -ST:STUDY_SUMMARY - -ST:INSTITUTE University of California, Davis -ST:DEPARTMENT Nutrition -ST:LABORATORY Gaikwad Lab -ST:LAST_NAME Gaikwad -ST:FIRST_NAME Nilesh -ST:ADDRESS - -ST:EMAIL nwgaikwad@ucdavis.edu -ST:PHONE - -ST:NUM_GROUPS NA -#SUBJECT -SU:SUBJECT_TYPE Human -SU:SUBJECT_SPECIES Homo sapiens -SU:TAXONOMY_ID 9606 -#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data -SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum -SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum -#COLLECTION -CO:COLLECTION_SUMMARY - -#TREATMENT -TR:TREATMENT_SUMMARY - -#SAMPLEPREP -SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction -SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx -SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac -SP:PROCESSING_STORAGE_CONDITIONS On Ice -SP:EXTRACTION_METHOD 1:1 Methanol: Water -SP:EXTRACT_STORAGE -80C -SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O -SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid -SP:ORGAN Fetal: Male and female brain, male and female liver -#CHROMATOGRAPHY -CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS -CH:CHROMATOGRAPHY_TYPE Reversed phase -CH:INSTRUMENT_NAME Waters Acquity -CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) -CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min -CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A -CH:FLOW_RATE 0.15 ml/min -CH:SAMPLE_INJECTION 10ul -CH:SOLVENT_A Water 0.1% formic acid -CH:SOLVENT_B CH3CN 0.1 % formic acid -CH:ANALYTICAL_TIME 12 mins -#ANALYSIS -AN:ANALYSIS_TYPE MS -AN:LABORATORY_NAME Gaikwad Laboratory -AN:ACQUISITION_DATE 41716 -AN:SOFTWARE_VERSION Masslynx -AN:OPERATOR_NAME Nilesh Gaikwad -#MS -MS:INSTRUMENT_NAME Waters Xevo-TQ -MS:INSTRUMENT_TYPE Triple quadrupole -MS:MS_TYPE ESI -MS:ION_MODE POSITIVE -MS:CAPILLARY_VOLTAGE 3.0 kV -MS:COLLISION_GAS N2 -MS:IONIZATION Electrospray Ionization -MS:SOURCE_TEMPERATURE 150C -MS:DESOLVATION_GAS_FLOW 600 L/h -MS:DESOLVATION_TEMPERATURE 350C -MS:MS_COMMENTS UPLC-MS/MS -#MS_METABOLITE_DATA -MS_METABOLITE_DATA:UNITS pg/ml -MS_METABOLITE_DATA_START -Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 -Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum -17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 -17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 -Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 -Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 -Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 -Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 -Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 -Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 -Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 -Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 -Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 -Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 -Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 -Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 -MS_METABOLITE_DATA_END -#METABOLITES -METABOLITES_START -metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type -17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID -17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID -Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID -Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID -Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID -Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID -Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID -Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID -Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID -Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID -Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID -Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID -Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID -Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID -METABOLITES_END -#END - - +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone_ DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_table_values.json b/tests/example_data/validation_files/ST000122_AN000204_validate_table_values.json new file mode 100644 index 0000000..cdead48 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_table_values.json @@ -0,0 +1,1248 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "-", + "NUM_GROUPS": "NA" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { 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"Sample ID": "CER488_274343_ML_38", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER530_249229_ML_39", + "Sample ID": "CER530_249229_ML_39", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER540_240346_ML_40", + "Sample ID": "CER540_240346_ML_40", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER552_241945_ML_41", + "Sample ID": "CER552_241945_ML_41", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER555_251239_ML_42", + "Sample ID": "CER555_251239_ML_42", + "Factors": { + "Tissue/Fluid": "Serum" + } + } + ], + "COLLECTION": { + "COLLECTION_SUMMARY": "-" + }, + "TREATMENT": { + "TREATMENT_SUMMARY": "-" + }, + "SAMPLEPREP": { + "SAMPLEPREP_SUMMARY": "Methanol: Water Extraction", + "SAMPLEPREP_PROTOCOL_FILENAME": "NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx", + "PROCESSING_METHOD": "Homogenization and Solvent Removal w/ Speed Vac", + "PROCESSING_STORAGE_CONDITIONS": 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"52.5000", + "CER324_285069_ML_26": "106.2500", + "CER340_244448_ML_27": "43.2500", + "CER346_246320_ML_28": "57.2500", + "CER356_269662_ML_29": "97.2500", + "CER368_250104_ML_30": "16.0000", + "CER369_276355_ML_31": "192.0000", + "CER384_264971_ML_32": "53.7500", + "CER445_286527_ML_33": "182.5000", + "CER452_240972_ML_34": "0.2500", + "CER463_271249_ML_35": "11.5000", + "CER465_265004_ML_36": "87.2500", + "CER483_294606_ML_37": "33.7500", + "CER488_274343_ML_38": "45.5000", + "CER530_249229_ML_39": "26.2500", + "CER540_240346_ML_40": "96.0000", + "CER552_241945_ML_41": "17.5000", + "CER555_251239_ML_42": "79.7500" + } + ], + "Metabolites": [ + { + "Metabolite": "17-hydroxypregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "91451", + "inchi_key": "", + "kegg_id": "", + "other_id": "2Q4710", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "asdf", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "17-hydroxypregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "91451", + "inchi_key": "", + "kegg_id": "", + "other_id": "2Q4710", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "asdf", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "17-hydroxyprogesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6238", + "inchi_key": "", + "kegg_id": "", + "other_id": "6Q3360", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Allodihydrotestosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "10635", + "inchi_key": "", + "kegg_id": "", + "other_id": "14A2570", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Androstenedione", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6128", + "inchi_key": "", + "kegg_id": "", + "other_id": "12A6030", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Androstenolone (DHEA)", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5881", + "inchi_key": "", + "kegg_id": "", + "other_id": "3A8500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Cortexolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "440707", + "inchi_key": "", + "kegg_id": "", + "other_id": "7Q1610", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Cortexone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6166", + "inchi_key": "", + "kegg_id": "", + "other_id": "9Q3460", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Corticosterone, DOC", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5753", + "inchi_key": "", + "kegg_id": "", + "other_id": "10Q1550", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Cortisol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5754", + "inchi_key": "", + "kegg_id": "", + "other_id": "8Q3880", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Estradiol", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5757", + "inchi_key": "", + "kegg_id": "", + "other_id": "16E0950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Estrone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5870", + "inchi_key": "", + "kegg_id": "", + "other_id": "15E2300", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Pregnenolone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "8955", + "inchi_key": "", + "kegg_id": "", + "other_id": "1Q5500", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Progesterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "5994", + "inchi_key": "", + "kegg_id": "", + "other_id": "5Q2600", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "qwer", + "moverz_quant": "" + }, + { + "Metabolite": "Testosterone", + "moverz_quant": "", + "ri": "", + "ri_type": "", + "pubchem_id": "6013", + "inchi_key": "", + "kegg_id": "", + "other_id": "13A6950", + "other_id_type": "UCDavis_Gaikwad_Lab_ID", + "": "", + "null_column": "", + "90_10": "asdf", + "moverz_quant": "" + } + ] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_table_values.txt b/tests/example_data/validation_files/ST000122_AN000204_validate_table_values.txt new file mode 100644 index 0000000..63846b4 --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_table_values.txt @@ -0,0 +1,163 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY - +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS - +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY - +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS - +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE - +ST:NUM_GROUPS NA +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY - +#TREATMENT +TR:TREATMENT_SUMMARY - +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 ml/min +CH:SAMPLE_INJECTION 10ul +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 mins +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS N2 +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150C +MS:DESOLVATION_GAS_FLOW 600 L/h +MS:DESOLVATION_TEMPERATURE 350C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_1 CER040_242995_ML_2 CER055_249947_ML_3 CER062_246153_ML_4 CER085_251176_ML_5 CER093_242931_ML_6 CER110_238825_ML_7 CER120_253690_ML_8 CER147_254803_ML_9 CER149_266689_ML_10 CER158_254231_ML_11 CER165_287001_ML_12 CER178_295145_ML_13 CER181_244392_ML_14 CER188_250760_ML_15 CER192_254091_ML_16 CER201_244193_ML_17 CER216_242490_ML_18 CER220_274308_ML_19 CER223_264067_ML_20 CER226_254303_ML_21 CER277_255328_ML_22 CER287_248530_ML_23 CER303_253023_ML_24 CER315_282966_ML_25 CER324_285069_ML_26 CER340_244448_ML_27 CER346_246320_ML_28 CER356_269662_ML_29 CER368_250104_ML_30 CER369_276355_ML_31 CER384_264971_ML_32 CER445_286527_ML_33 CER452_240972_ML_34 CER463_271249_ML_35 CER465_265004_ML_36 CER483_294606_ML_37 CER488_274343_ML_38 CER530_249229_ML_39 CER540_240346_ML_40 CER552_241945_ML_41 CER555_251239_ML_42 +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 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449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant ri ri_type pubchem_id inchi_key kegg_id other_id other_id_type null_column 90_10 moverz_quant +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID asdf qwer +17-hydroxypregnenolone 91451 2Q4710 UCDavis_Gaikwad_Lab_ID asdf qwer +17-hydroxyprogesterone 6238 6Q3360 UCDavis_Gaikwad_Lab_ID qwer +Allodihydrotestosterone 10635 14A2570 UCDavis_Gaikwad_Lab_ID qwer +Androstenedione 6128 12A6030 UCDavis_Gaikwad_Lab_ID qwer +Androstenolone (DHEA) 5881 3A8500 UCDavis_Gaikwad_Lab_ID qwer +Cortexolone 440707 7Q1610 UCDavis_Gaikwad_Lab_ID qwer +Cortexone 6166 9Q3460 UCDavis_Gaikwad_Lab_ID qwer +Corticosterone, DOC 5753 10Q1550 UCDavis_Gaikwad_Lab_ID qwer +Cortisol 5754 8Q3880 UCDavis_Gaikwad_Lab_ID qwer +Estradiol 5757 16E0950 UCDavis_Gaikwad_Lab_ID qwer +Estrone 5870 15E2300 UCDavis_Gaikwad_Lab_ID qwer +Pregnenolone 8955 1Q5500 UCDavis_Gaikwad_Lab_ID qwer +Progesterone 5994 5Q2600 UCDavis_Gaikwad_Lab_ID qwer +Testosterone 6013 13A6950 UCDavis_Gaikwad_Lab_ID asdf +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/ST000122_AN000204_validate_table_values2.json b/tests/example_data/validation_files/ST000122_AN000204_validate_table_values2.json new file mode 100644 index 0000000..caaf21a --- /dev/null +++ b/tests/example_data/validation_files/ST000122_AN000204_validate_table_values2.json @@ -0,0 +1,1249 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "AN000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "-", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "-", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "-", + "NUM_GROUPS": "NA" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", 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Acquity HSS T3 (150 x 2.1mm, 1.8um)", + "FLOW_GRADIENT": "0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A", + "FLOW_RATE": "0.15 ml/min", + "SAMPLE_INJECTION": "10ul", + "SOLVENT_A": "Water 0.1% formic acid", + "SOLVENT_B": "CH3CN 0.1 % formic acid", + "ANALYTICAL_TIME": "12 mins" + }, + "ANALYSIS": { + "ANALYSIS_TYPE": "MS", + "LABORATORY_NAME": "Gaikwad Laboratory", + "ACQUISITION_DATE": "41716", + "SOFTWARE_VERSION": "Masslynx", + "OPERATOR_NAME": "Nilesh Gaikwad" + }, + "MS": { + "INSTRUMENT_NAME": "Waters Xevo-TQ", + "INSTRUMENT_TYPE": "Triple quadrupole", + "MS_TYPE": "ESI", + "ION_MODE": "POSITIVE", + "CAPILLARY_VOLTAGE": "3.0 kV", + "COLLISION_GAS": "N2", + "IONIZATION": "Electrospray Ionization", + "SOURCE_TEMPERATURE": "150C", + "DESOLVATION_GAS_FLOW": "600 L/h", + "DESOLVATION_TEMPERATURE": "350C", + "MS_COMMENTS": "UPLC-MS/MS" + }, + "MS_METABOLITE_DATA": { + "Units": "pg/ml", + "Data": [ + 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"CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER340_244448_ML_27", + "Sample ID": "CER340_244448_ML_27", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER346_246320_ML_28", + "Sample ID": "CER346_246320_ML_28", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER356_269662_ML_29", + "Sample ID": "CER356_269662_ML_29", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER368_250104_ML_30", + "Sample ID": "CER368_250104_ML_30", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER369_276355_ML_31", + "Sample ID": "CER369_276355_ML_31", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER384_264971_ML_32", + "Sample ID": "CER384_264971_ML_32", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER445_286527_ML_33", + "Sample ID": "CER445_286527_ML_33", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER452_240972_ML_34", + "Sample ID": "CER452_240972_ML_34", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER463_271249_ML_35", + "Sample ID": "CER463_271249_ML_35", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER465_265004_ML_36", + "Sample ID": "CER465_265004_ML_36", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER483_294606_ML_37", + "Sample ID": "CER483_294606_ML_37", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER488_274343_ML_38", + "Sample ID": "CER488_274343_ML_38", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER530_249229_ML_39", + "Sample ID": "CER530_249229_ML_39", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER540_240346_ML_40", + "Sample ID": "CER540_240346_ML_40", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER552_241945_ML_41", + "Sample ID": "CER552_241945_ML_41", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER555_251239_ML_42", + "Sample ID": "CER555_251239_ML_42", + "Factors": { + "Tissue/Fluid": "Serum" + } + } + ], + "COLLECTION": { + "COLLECTION_SUMMARY": "asdf" + }, + "TREATMENT": { + "TREATMENT_SUMMARY": "asdf" + }, + "SAMPLEPREP": { + "SAMPLEPREP_SUMMARY": "Methanol: Water Extraction", + "SAMPLEPREP_PROTOCOL_FILENAME": "NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx", + "PROCESSING_METHOD": "Homogenization and Solvent Removal w/ Speed Vac", + "PROCESSING_STORAGE_CONDITIONS": "On Ice", + "EXTRACTION_METHOD": "1:1 Methanol: Water", + "EXTRACT_STORAGE": "-80C", + "SAMPLE_RESUSPENSION": "150ul CH3OH/H2O", + "ORGAN": "Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid Fetal: Male and female brain, male and female liver" + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted UPLC-MS/MS", + "CHROMATOGRAPHY_TYPE": "Reversed phase", + "INSTRUMENT_NAME": "Waters Acquity", + "COLUMN_NAME": "Waters Acquity HSS T3 (150 x 2.1mm, 1.8um)", + "FLOW_GRADIENT": "0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A", + "FLOW_RATE": "0.15 mL/min", + "SAMPLE_INJECTION": "10 uL", + "SOLVENT_A": "Water 0.1% formic acid", + "SOLVENT_B": "CH3CN 0.1 % formic acid", + "ANALYTICAL_TIME": "12 min", + "COLUMN_TEMPERATURE": "150 C" + }, + "ANALYSIS": { + "ANALYSIS_TYPE": "MS", + "LABORATORY_NAME": "Gaikwad Laboratory", + "ACQUISITION_DATE": "41716", + "SOFTWARE_VERSION": "Masslynx", + "OPERATOR_NAME": "Nilesh Gaikwad" + }, + "MS": { + "INSTRUMENT_NAME": "Waters Xevo-TQ", + "INSTRUMENT_TYPE": "Triple quadrupole", + "MS_TYPE": "ESI", + "ION_MODE": "POSITIVE", + "CAPILLARY_VOLTAGE": "3.0 kV", + "COLLISION_GAS": "Nitrogen", + "IONIZATION": "Electrospray Ionization", + "SOURCE_TEMPERATURE": "150 C", + "DESOLVATION_GAS_FLOW": "600 L/hr", + "DESOLVATION_TEMPERATURE": "350 C", + "MS_COMMENTS": "UPLC-MS/MS" + }, + "MS_METABOLITE_DATA": { + "Units": "pg/ml", + "Data":[], + "Metabolites":[] + } +} \ No newline at end of file diff --git a/tests/example_data/validation_files/complete_coverage2.txt b/tests/example_data/validation_files/complete_coverage2.txt new file mode 100644 index 0000000..6a1995a --- /dev/null +++ b/tests/example_data/validation_files/complete_coverage2.txt @@ -0,0 +1,163 @@ +#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:000204 PROJECT_ID:PR000109 +VERSION 1 +CREATED_ON 2016-09-17 +#PROJECT +PR:PROJECT_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +PR:PROJECT_TYPE Pilot and Feasibility Projects +PR:PROJECT_SUMMARY asdf +PR:INSTITUTE University of California, Davis +PR:DEPARTMENT Nutrition +PR:LABORATORY Gaikwad Lab +PR:LAST_NAME Gaikwad +PR:FIRST_NAME Nilesh +PR:ADDRESS asdf +PR:EMAIL nwgaikwad@ucdavis.edu +PR:PHONE 530-752-2906 +PR:FUNDING_SOURCE NIH 1U24DK097154 ;  PI Fiehn, Oliver  ; UC Davis WEST COAST CENTRAL +PR:FUNDING_SOURCE METABOLOMICS RESOURCE CORE (WC3MRC) +#STUDY +ST:STUDY_TITLE Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic +ST:STUDY_TYPE steroid panel +ST:STUDY_SUMMARY asdf +ST:INSTITUTE University of California, Davis +ST:DEPARTMENT Nutrition +ST:LABORATORY Gaikwad Lab +ST:LAST_NAME Gaikwad +ST:FIRST_NAME Nilesh +ST:ADDRESS asdf +ST:EMAIL nwgaikwad@ucdavis.edu +ST:PHONE asdf +ST:NUM_GROUPS 2 +#SUBJECT +SU:SUBJECT_TYPE Human +SU:SUBJECT_SPECIES Homo sapiens +SU:TAXONOMY_ID 9606 +#SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Additional sample data +SUBJECT_SAMPLE_FACTORS CER030_294717_ML_1 CER030_294717_ML_1 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER040_242995_ML_2 CER040_242995_ML_2 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER055_249947_ML_3 CER055_249947_ML_3 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER062_246153_ML_4 CER062_246153_ML_4 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER085_251176_ML_5 CER085_251176_ML_5 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER093_242931_ML_6 CER093_242931_ML_6 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER110_238825_ML_7 CER110_238825_ML_7 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER120_253690_ML_8 CER120_253690_ML_8 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER147_254803_ML_9 CER147_254803_ML_9 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER149_266689_ML_10 CER149_266689_ML_10 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER158_254231_ML_11 CER158_254231_ML_11 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER165_287001_ML_12 CER165_287001_ML_12 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER178_295145_ML_13 CER178_295145_ML_13 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER181_244392_ML_14 CER181_244392_ML_14 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER188_250760_ML_15 CER188_250760_ML_15 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER192_254091_ML_16 CER192_254091_ML_16 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER201_244193_ML_17 CER201_244193_ML_17 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER216_242490_ML_18 CER216_242490_ML_18 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER220_274308_ML_19 CER220_274308_ML_19 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER223_264067_ML_20 CER223_264067_ML_20 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER226_254303_ML_21 CER226_254303_ML_21 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER277_255328_ML_22 CER277_255328_ML_22 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER287_248530_ML_23 CER287_248530_ML_23 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER303_253023_ML_24 CER303_253023_ML_24 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER315_282966_ML_25 CER315_282966_ML_25 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER324_285069_ML_26 CER324_285069_ML_26 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER340_244448_ML_27 CER340_244448_ML_27 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER346_246320_ML_28 CER346_246320_ML_28 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER356_269662_ML_29 CER356_269662_ML_29 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER368_250104_ML_30 CER368_250104_ML_30 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER369_276355_ML_31 CER369_276355_ML_31 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER384_264971_ML_32 CER384_264971_ML_32 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER445_286527_ML_33 CER445_286527_ML_33 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER452_240972_ML_34 CER452_240972_ML_34 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER463_271249_ML_35 CER463_271249_ML_35 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER465_265004_ML_36 CER465_265004_ML_36 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER483_294606_ML_37 CER483_294606_ML_37 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER488_274343_ML_38 CER488_274343_ML_38 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER530_249229_ML_39 CER530_249229_ML_39 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER540_240346_ML_40 CER540_240346_ML_40 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER552_241945_ML_41 CER552_241945_ML_41 Tissue/Fluid:Serum +SUBJECT_SAMPLE_FACTORS CER555_251239_ML_42 CER555_251239_ML_42 Tissue/Fluid:Serum +#COLLECTION +CO:COLLECTION_SUMMARY asdf +#TREATMENT +TR:TREATMENT_SUMMARY asdf +#SAMPLEPREP +SP:SAMPLEPREP_SUMMARY Methanol: Water Extraction +SP:SAMPLEPREP_PROTOCOL_FILENAME NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx +SP:PROCESSING_METHOD Homogenization and Solvent Removal w/ Speed Vac +SP:PROCESSING_STORAGE_CONDITIONS On Ice +SP:EXTRACTION_METHOD 1:1 Methanol: Water +SP:EXTRACT_STORAGE -80C +SP:SAMPLE_RESUSPENSION 150ul CH3OH/H2O +SP:ORGAN Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid +SP:ORGAN Fetal: Male and female brain, male and female liver +#CHROMATOGRAPHY +CH:CHROMATOGRAPHY_SUMMARY Targeted UPLC-MS/MS +CH:CHROMATOGRAPHY_TYPE Reversed phase +CH:INSTRUMENT_NAME Waters Acquity +CH:COLUMN_NAME Waters Acquity HSS T3 (150 x 2.1mm, 1.8um) +CH:FLOW_GRADIENT 0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min +CH:FLOW_GRADIENT A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A +CH:FLOW_RATE 0.15 mL/min +CH:SAMPLE_INJECTION 10 uL +CH:SOLVENT_A Water 0.1% formic acid +CH:SOLVENT_B CH3CN 0.1 % formic acid +CH:ANALYTICAL_TIME 12 min +CH:COLUMN_TEMPERATURE 150 C +#ANALYSIS +AN:ANALYSIS_TYPE MS +AN:LABORATORY_NAME Gaikwad Laboratory +AN:ACQUISITION_DATE 41716 +AN:SOFTWARE_VERSION Masslynx +AN:OPERATOR_NAME Nilesh Gaikwad +#MS +MS:INSTRUMENT_NAME Waters Xevo-TQ +MS:INSTRUMENT_TYPE Triple quadrupole +MS:MS_TYPE ESI +MS:ION_MODE POSITIVE +MS:CAPILLARY_VOLTAGE 3.0 kV +MS:COLLISION_GAS Nitrogen +MS:IONIZATION Electrospray Ionization +MS:SOURCE_TEMPERATURE 150 C +MS:DESOLVATION_GAS_FLOW 600 L/hr +MS:DESOLVATION_TEMPERATURE 350 C +MS:MS_COMMENTS UPLC-MS/MS +#MS_METABOLITE_DATA +MS_METABOLITE_DATA:UNITS pg/ml +MS_METABOLITE_DATA_START +Samples CER030_294717_ML_ CER040_242995_ML_ CER055_249947_ML_ CER062_246153_ML_ CER085_251176_ML_ CER093_242931_ML_ CER110_238825_ML_ CER120_253690_ML_ CER147_254803_ML_ CER149_266689_ML_ CER158_254231_ML_ CER165_287001_ML_ CER178_295145_ML_ CER181_244392_ML_ CER188_250760_ML_ CER192_254091_ML_ CER201_244193_ML_ CER216_242490_ML_ CER220_274308_ML_ CER223_264067_ML_ CER226_254303_ML_ CER277_255328_ML_ CER287_248530_ML_ CER303_253023_ML_ CER315_282966_ML_ CER324_285069_ML_ CER340_244448_ML_ CER346_246320_ML_ CER356_269662_ML_ CER368_250104_ML_ CER369_276355_ML_ CER384_264971_ML_ CER445_286527_ML_ CER452_240972_ML_ CER463_271249_ML_ CER465_265004_ML_ CER483_294606_ML_ CER488_274343_ML_ CER530_249229_ML_ CER540_240346_ML_ CER552_241945_ML_ CER555_251239_ML_ +Factors Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum Tissue/Fluid:Serum +17-hydroxypregnenolone 946.2500 0.0000 676.2500 0.0000 2251.2500 0.0000 0.0000 1134.7500 0.0000 0.0000 2016.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1919.7500 0.0000 972.7500 0.0000 1542.2500 1687.7500 421.0000 0.0000 373.2500 0.0000 614.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 528.2500 +17-hydroxyprogesterone 0.0000 2.0000 0.0000 0.0000 19.2500 0.0000 0.0000 27.0000 2.0000 120.7500 27.7500 83.0000 0.0000 8.0000 3.5000 274.0000 0.0000 0.0000 3.0000 3.2500 0.0000 43.7500 15.2500 25.7500 4.2500 0.0000 0.0000 49.5000 27.7500 14.0000 9.7500 35.2500 34.7500 4.5000 8.0000 17.2500 0.0000 24.7500 19.0000 0.0000 4.5000 132.0000 +Allodihydrotestosterone 80.0000 1181.0000 0.0000 0.0000 0.0000 112.2500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 288.0000 0.0000 0.0000 374.7500 0.0000 27.5000 112.7500 247.7500 39.0000 0.0000 0.0000 0.0000 0.0000 0.0000 761.0000 245.5000 332.5000 52.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 465.7500 159.0000 0.0000 77.0000 315.5000 466.0000 +Androstenedione 76.7500 57.0000 176.2500 399.5000 208.5000 37.0000 281.2500 79.7500 250.7500 420.5000 123.0000 186.2500 34.7500 224.5000 67.7500 335.0000 126.5000 277.0000 50.5000 153.7500 62.2500 107.0000 431.2500 167.5000 134.0000 60.7500 38.5000 42.0000 78.7500 43.0000 60.0000 114.7500 237.7500 53.5000 51.7500 298.0000 220.2500 15.0000 256.5000 172.5000 79.2500 52.5000 +Androstenolone (DHEA) 1779.7500 1409.2500 945.7500 748.2500 2284.0000 2351.0000 2183.7500 1916.5000 5079.5000 1474.0000 1338.5000 1646.0000 2051.7500 2039.7500 2618.0000 306.7500 574.5000 1794.2500 1429.0000 2293.2500 2066.2500 2493.2500 918.0000 1579.2500 2042.2500 2645.7500 2393.7500 1913.0000 1641.5000 853.2500 586.5000 537.2500 562.5000 1887.2500 979.0000 678.5000 1357.2500 1526.2500 2300.7500 129.0000 409.2500 282.2500 +Cortexolone 0.0000 0.0000 0.0000 54.0000 0.0000 0.0000 0.0000 0.0000 215.7500 135.7500 72.7500 53.0000 11.7500 0.0000 0.0000 0.0000 0.0000 101.2500 11.2500 0.0000 0.0000 315.0000 181.2500 0.0000 7.7500 151.2500 0.0000 0.0000 104.0000 0.0000 0.0000 30.7500 94.2500 210.5000 33.2500 126.0000 0.0000 10.0000 17.0000 15.7500 0.0000 0.0000 +Cortexone 108.0000 16.0000 13.0000 117.5000 3.2500 63.2500 42.5000 146.7500 29.5000 204.2500 28.7500 67.0000 30.5000 103.0000 23.0000 416.7500 63.5000 32.5000 32.5000 127.2500 39.0000 84.2500 7.2500 16.2500 68.7500 27.0000 46.5000 21.7500 3.2500 14.7500 28.7500 67.0000 33.0000 40.7500 31.0000 32.2500 40.0000 13.7500 18.7500 0.0000 25.7500 29.0000 +Corticosterone, DOC 0.0000 354.5000 0.0000 0.0000 322.5000 419.7500 420.7500 0.0000 0.0000 0.0000 393.2500 915.5000 0.0000 432.2500 1233.0000 0.0000 525.5000 1700.0000 0.0000 98.7500 285.5000 42.5000 428.2500 0.0000 427.5000 271.7500 254.7500 478.0000 303.5000 462.2500 532.0000 715.0000 1073.0000 836.2500 0.0000 1639.0000 601.7500 287.7500 0.0000 0.0000 435.2500 1602.2500 +Cortisol 7643.0000 39245.7500 11671.5000 20216.0000 14908.7500 14386.5000 16815.2500 7806.2500 27135.5000 7095.0000 12175.2500 36413.0000 2499.2500 15101.7500 22045.0000 24832.0000 13257.0000 19528.5000 4539.7500 7681.7500 9585.2500 19361.0000 24203.7500 5667.0000 19437.2500 10849.2500 11855.7500 7546.5000 3093.7500 19035.7500 18575.0000 14801.5000 22960.7500 22506.5000 8001.5000 31037.5000 18577.2500 15506.2500 8364.7500 2145.7500 5574.7500 19662.5000 +Estradiol 123992.2500 796595.7500 619110.0000 449415.7500 320835.5000 326124.2500 249087.2500 311589.2500 345598.5000 485857.0000 332055.2500 211831.0000 334929.7500 235466.7500 352555.0000 410500.0000 887955.0000 865791.7500 1648163.5000 856726.7500 579044.2500 254013.2500 326272.7500 239893.7500 329553.2500 438715.5000 248489.0000 380251.0000 338965.5000 337231.2500 342754.5000 370657.2500 2028106.5000 733521.0000 399244.2500 321007.5000 634463.0000 231294.0000 349439.2500 75746.7500 399415.5000 303855.7500 +Estrone 484.5000 1663.7500 1680.7500 794.5000 557.2500 625.7500 669.7500 885.0000 715.0000 1225.5000 697.7500 478.2500 659.0000 575.5000 871.7500 1089.0000 1726.2500 2325.2500 3286.7500 1955.7500 1094.0000 486.2500 650.5000 574.2500 601.7500 842.7500 757.7500 732.7500 571.7500 693.7500 1004.2500 879.2500 3154.7500 1095.2500 22680.2500 637.2500 1108.2500 474.2500 810.2500 421.2500 680.7500 623.7500 +Pregnenolone 12.2500 0.0000 0.0000 0.0000 0.0000 144.2500 14.7500 807.2500 0.0000 30.0000 0.0000 0.0000 0.0000 0.0000 16.5000 139.5000 132.5000 0.0000 0.0000 13.7500 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 488.5000 0.0000 0.0000 0.0000 0.0000 280.7500 0.0000 0.0000 0.0000 0.0000 0.0000 205.5000 +Progesterone 28.2500 6.2500 725.2500 57.2500 767.0000 2.7500 388.0000 9.0000 19.5000 242.5000 4.0000 0.0000 94.5000 160.7500 0.0000 3214.5000 218.2500 1.0000 0.0000 20.0000 4.5000 55.7500 24.5000 57.0000 200.5000 138.7500 132.2500 120.5000 80.5000 59.5000 315.7500 247.2500 211.5000 198.5000 232.2500 241.0000 199.5000 282.5000 216.5000 358.5000 289.5000 199.2500 +Testosterone 75.7500 63.2500 42.7500 98.0000 24.2500 35.0000 165.7500 23.2500 73.7500 52.7500 118.7500 35.7500 65.2500 127.2500 14.2500 202.5000 110.7500 53.5000 54.2500 2.2500 105.2500 182.7500 116.0000 66.2500 52.5000 106.2500 43.2500 57.2500 97.2500 16.0000 192.0000 53.7500 182.5000 0.2500 11.5000 87.2500 33.7500 45.5000 26.2500 96.0000 17.5000 79.7500 +MS_METABOLITE_DATA_END +#METABOLITES +METABOLITES_START +metabolite_name moverz_quant retention_index retention_index_type pubchem_id asdf{{{_11_}}} +17-hydroxypregnenolone 123 345 asdf 91451 +17-hydroxyprogesterone 123 345 asdf 6238 +Allodihydrotestosterone 123 345 asdf 10635 +Androstenedione 123 345 asdf 6128 +Androstenolone (DHEA) 123 345 asdf 5881 +Cortexolone 123 345 asdf 440707 +Cortexone 123 345 asdf 6166 +Corticosterone, DOC 123 345 asdf 5753 +Cortisol 123 345 asdf 5754 +Estradiol 123 345 asdf 5757 +Estrone 123 345 asdf 5870 +Pregnenolone 123 345 asdf 8955 +Progesterone 123 345 asdf 5994 +Testosterone 123 345 asdf 6013 +METABOLITES_END +#END + + diff --git a/tests/example_data/validation_files/complete_coverage3.json b/tests/example_data/validation_files/complete_coverage3.json new file mode 100644 index 0000000..e4a6ba4 --- /dev/null +++ b/tests/example_data/validation_files/complete_coverage3.json @@ -0,0 +1,390 @@ +{ + "METABOLOMICS WORKBENCH": { + "STUDY_ID": "ST000122", + "ANALYSIS_ID": "000204", + "PROJECT_ID": "PR000109", + "VERSION": "1", + "CREATED_ON": "2016-09-17" + }, + "PROJECT": { + "PROJECT_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "PROJECT_TYPE": "Pilot and Feasibility Projects", + "PROJECT_SUMMARY": "asdf", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "asdf", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "530-752-2906", + "FUNDING_SOURCE": "NIH 1U24DK097154 ;\u00a0 PI Fiehn, Oliver\u00a0 ; UC Davis WEST COAST CENTRAL METABOLOMICS RESOURCE CORE (WC3MRC)" + }, + "STUDY": { + "STUDY_TITLE": "Perinatal DDT causes dysfunctional lipid metabolism underlying metabolic", + "STUDY_TYPE": "steroid panel", + "STUDY_SUMMARY": "asdf", + "INSTITUTE": "University of California, Davis", + "DEPARTMENT": "Nutrition", + "LABORATORY": "Gaikwad Lab", + "LAST_NAME": "Gaikwad", + "FIRST_NAME": "Nilesh", + "ADDRESS": "asdf", + "EMAIL": "nwgaikwad@ucdavis.edu", + "PHONE": "asdf", + "NUM_GROUPS": "2" + }, + "SUBJECT": { + "SUBJECT_TYPE": "Human", + "SUBJECT_SPECIES": "Homo sapiens", + "TAXONOMY_ID": "9606" + }, + "SUBJECT_SAMPLE_FACTORS": [ + { + "Subject ID": "CER030_294717_ML_1", + "Sample ID": "CER030_294717_ML_1", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER040_242995_ML_2", + "Sample ID": "CER040_242995_ML_2", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER055_249947_ML_3", + "Sample ID": "CER055_249947_ML_3", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER062_246153_ML_4", + "Sample ID": "CER062_246153_ML_4", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER085_251176_ML_5", + "Sample ID": "CER085_251176_ML_5", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER093_242931_ML_6", + "Sample ID": "CER093_242931_ML_6", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER110_238825_ML_7", + "Sample ID": "CER110_238825_ML_7", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER120_253690_ML_8", + "Sample ID": "CER120_253690_ML_8", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER147_254803_ML_9", + "Sample ID": "CER147_254803_ML_9", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER149_266689_ML_10", + "Sample ID": "CER149_266689_ML_10", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER158_254231_ML_11", + "Sample ID": "CER158_254231_ML_11", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER165_287001_ML_12", + "Sample ID": "CER165_287001_ML_12", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER178_295145_ML_13", + "Sample ID": "CER178_295145_ML_13", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER181_244392_ML_14", + "Sample ID": "CER181_244392_ML_14", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER188_250760_ML_15", + "Sample ID": "CER188_250760_ML_15", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER192_254091_ML_16", + "Sample ID": "CER192_254091_ML_16", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER201_244193_ML_17", + "Sample ID": "CER201_244193_ML_17", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER216_242490_ML_18", + "Sample ID": "CER216_242490_ML_18", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER220_274308_ML_19", + "Sample ID": "CER220_274308_ML_19", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER223_264067_ML_20", + "Sample ID": "CER223_264067_ML_20", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER226_254303_ML_21", + "Sample ID": "CER226_254303_ML_21", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER277_255328_ML_22", + "Sample ID": "CER277_255328_ML_22", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER287_248530_ML_23", + "Sample ID": "CER287_248530_ML_23", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER303_253023_ML_24", + "Sample ID": "CER303_253023_ML_24", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER315_282966_ML_25", + "Sample ID": "CER315_282966_ML_25", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER324_285069_ML_26", + "Sample ID": "CER324_285069_ML_26", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER340_244448_ML_27", + "Sample ID": "CER340_244448_ML_27", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER346_246320_ML_28", + "Sample ID": "CER346_246320_ML_28", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER356_269662_ML_29", + "Sample ID": "CER356_269662_ML_29", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER368_250104_ML_30", + "Sample ID": "CER368_250104_ML_30", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER369_276355_ML_31", + "Sample ID": "CER369_276355_ML_31", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER384_264971_ML_32", + "Sample ID": "CER384_264971_ML_32", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER445_286527_ML_33", + "Sample ID": "CER445_286527_ML_33", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER452_240972_ML_34", + "Sample ID": "CER452_240972_ML_34", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER463_271249_ML_35", + "Sample ID": "CER463_271249_ML_35", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER465_265004_ML_36", + "Sample ID": "CER465_265004_ML_36", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER483_294606_ML_37", + "Sample ID": "CER483_294606_ML_37", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER488_274343_ML_38", + "Sample ID": "CER488_274343_ML_38", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER530_249229_ML_39", + "Sample ID": "CER530_249229_ML_39", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER540_240346_ML_40", + "Sample ID": "CER540_240346_ML_40", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER552_241945_ML_41", + "Sample ID": "CER552_241945_ML_41", + "Factors": { + "Tissue/Fluid": "Serum" + } + }, + { + "Subject ID": "CER555_251239_ML_42", + "Sample ID": "CER555_251239_ML_42", + "Factors": { + "Tissue/Fluid": "Serum" + } + } + ], + "COLLECTION": { + "COLLECTION_SUMMARY": "asdf" + }, + "TREATMENT": { + "TREATMENT_SUMMARY": "asdf" + }, + "SAMPLEPREP": { + "SAMPLEPREP_SUMMARY": "Methanol: Water Extraction", + "SAMPLEPREP_PROTOCOL_FILENAME": "NIH_WCMC_LaMerrill_Method_GaikwadLab__SteroidAnalysis_2013-14.docx", + "PROCESSING_METHOD": "Homogenization and Solvent Removal w/ Speed Vac", + "PROCESSING_STORAGE_CONDITIONS": "On Ice", + "EXTRACTION_METHOD": "1:1 Methanol: Water", + "EXTRACT_STORAGE": "-80C", + "SAMPLE_RESUSPENSION": "150ul CH3OH/H2O", + "ORGAN": "Sprague-Dawley Maternal: Adrenal, liver, placenta, amniotic fluid Fetal: Male and female brain, male and female liver" + }, + "CHROMATOGRAPHY": { + "CHROMATOGRAPHY_SUMMARY": "Targeted UPLC-MS/MS", + "CHROMATOGRAPHY_TYPE": "Reversed phase", + "INSTRUMENT_NAME": "Waters Acquity", + "COLUMN_NAME": "Waters Acquity HSS T3 (150 x 2.1mm, 1.8um)", + "FLOW_GRADIENT": "0-2 min 100% A (Water 0.1% formic acid) 0% B (CH3CN 0.1 % formic acid), 2-4 min A, 4-9mins 45% A, 9-11 mins 20% A, 11-12 mins 100% A", + "FLOW_RATE": "0.15 mL/min", + "SAMPLE_INJECTION": "10 uL", + "SOLVENT_A": "Water 0.1% formic acid", + "SOLVENT_B": "CH3CN 0.1 % formic acid", + "ANALYTICAL_TIME": "12 min", + "COLUMN_TEMPERATURE": "150 C" + }, + "ANALYSIS": { + "ANALYSIS_TYPE": "MS", + "LABORATORY_NAME": "Gaikwad Laboratory", + "ACQUISITION_DATE": "41716", + "SOFTWARE_VERSION": "Masslynx", + "OPERATOR_NAME": "Nilesh Gaikwad" + }, + "MS": { + "INSTRUMENT_NAME": "Waters Xevo-TQ", + "INSTRUMENT_TYPE": "Triple quadrupole", + "MS_TYPE": "ESI", + "ION_MODE": "POSITIVE", + "CAPILLARY_VOLTAGE": "3.0 kV", + "COLLISION_GAS": "Nitrogen", + "IONIZATION": "Electrospray Ionization", + "SOURCE_TEMPERATURE": "150 C", + "DESOLVATION_GAS_FLOW": "600 L/hr", + "DESOLVATION_TEMPERATURE": "350 C", + "MS_COMMENTS": "UPLC-MS/MS" + }, + "MS_METABOLITE_DATA": { + "Units": "pg/ml" + } +} \ No newline at end of file diff --git a/tests/fixtures.py b/tests/fixtures.py new file mode 100644 index 0000000..94c3f39 --- /dev/null +++ b/tests/fixtures.py @@ -0,0 +1,70 @@ + +import pathlib +import time +import shutil + +import pytest + +def create_tmp_dir(): + path = pathlib.Path("tests/example_data/tmp/") + path.mkdir(parents=True) + +def delete_tmp_dir(): + path = pathlib.Path("tests/example_data/tmp/") + if path.exists(): + shutil.rmtree(path) + time_to_wait=10 + time_counter = 0 + while path.exists(): + time.sleep(1) + time_counter += 1 + if time_counter > time_to_wait: + raise FileExistsError(path + " was not deleted within " + str(time_to_wait) + " seconds, so it is assumed that it won't be and something went wrong.") + + +@pytest.fixture() +def init_tmp_dir(): + delete_tmp_dir() + create_tmp_dir() + yield + delete_tmp_dir() + +@pytest.fixture(autouse=True) +def init_tmp_dir_auto(): + delete_tmp_dir() + create_tmp_dir() + yield + delete_tmp_dir() + +@pytest.fixture() +def teardown_module(): + delete_tmp_dir() + yield + delete_tmp_dir() + +@pytest.fixture(autouse=True) +def teardown_module_auto(): + delete_tmp_dir() + yield + delete_tmp_dir() + +@pytest.fixture() +def init_tmp_dir_no_clean(): + create_tmp_dir() + +@pytest.fixture() +def cleanup_tmp_dir(): + yield + delete_tmp_dir() + + + + + + + + + + + + diff --git a/tests/test_cli.py b/tests/test_cli.py index e9e5a2c..92880a6 100755 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -4,12 +4,49 @@ import mwtab import os import pytest -import shutil +import time +import pathlib +import subprocess +from fixtures import teardown_module -def teardown_module(module): - if os.path.exists("tests/example_data/tmp/"): - shutil.rmtree("tests/example_data/tmp") +from mwtab import cli + +teardown_module = teardown_module + +def delete_ST000001_cwd(): + path = pathlib.Path(os.path.join(os.getcwd(), 'https%3A%2F%2Fwww_metabolomicsworkbench_org%2Frest%2Fstudy%2Fstudy_id%2FST000001%2Fsummary.txt')) + if path.exists(): + os.remove(path) + time_to_wait=10 + time_counter = 0 + while path.exists(): + time.sleep(1) + time_counter += 1 + if time_counter > time_to_wait: + raise FileExistsError(path + " was not deleted within " + str(time_to_wait) + " seconds, so it is assumed that it won't be and something went wrong.") + +@pytest.fixture() +def teardown_module_cwd(): + delete_ST000001_cwd() + yield + delete_ST000001_cwd() + + +@pytest.fixture() +def disable_network_calls(monkeypatch): + def stunted_get(): + raise RuntimeError("Network access not allowed during testing!") + monkeypatch.setattr("urllib.request.urlopen", lambda *args, **kwargs: stunted_get()) + monkeypatch.setattr("urllib.parse.urlparse", lambda *args, **kwargs: stunted_get()) + monkeypatch.setattr("mwtab.fileio.urlopen", lambda *args, **kwargs: stunted_get()) + monkeypatch.setattr("mwtab.fileio.urlparse", lambda *args, **kwargs: stunted_get()) + +@pytest.fixture() +def disable_sleep(monkeypatch): + def no_sleep(arg): + pass + monkeypatch.setattr('mwtab.cli.time.sleep', no_sleep) @pytest.mark.parametrize("files_source", [ @@ -28,6 +65,27 @@ def test_validate_command(files_source): command = "python -m mwtab validate {}".format(files_source) assert os.system(command) == 0 +@pytest.mark.parametrize("file_sources", [ + ("tests/example_data/mwtab_files/ST000122_AN000204.txt", 'tests/example_data/tmp/validation_errors.json'), + ("tests/example_data/mwtab_files/ST000122_AN000204.txt", 'tests/example_data/tmp') +]) +def test_validate_command_saving(file_sources, teardown_module): + command = "python -m mwtab validate {} --to-path={}".format(*file_sources) + assert os.system(command) == 0 + assert os.path.exists(file_sources[1]) + + +def test_validate_command_error_recovery(): + """Test that the validate command can have an error and move to the next one.""" + command = "python -m mwtab validate tests/example_data/files_to_test_error_recovery" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + assert subp.returncode == 0 + assert "1_no_error.json" in subp.stdout + assert "2_error.json" in subp.stdout + assert "3_no_error.txt" in subp.stdout + assert "ValueError: Blank input string retrieved from source." in subp.stdout + @pytest.mark.parametrize("from_path, to_path, from_format, to_format", [ # one-to-one file conversions @@ -89,13 +147,34 @@ def test_convert_command(from_path, to_path, from_format, to_format): assert os.system(command) == 0 mwtabfile_generator = mwtab.read_files(to_path) - mwtabfiles_list = list(mwtabfile_generator) + mwtabfiles_list = [file for file in mwtabfile_generator] mwtabfiles_study_ids_set = set(mwf.study_id for mwf in mwtabfiles_list) mwtabfiles_analysis_ids_set = set(mwf.analysis_id for mwf in mwtabfiles_list) assert mwtabfiles_study_ids_set.issubset({"ST000122"}) assert mwtabfiles_analysis_ids_set.issubset({"AN000204"}) +def test_convert_command_mwrest_option(teardown_module): + """Test that the --mw-rest option works for the convert command. Test this by setting a bad url and making sure there is an error.""" + command = "python -m mwtab convert 204 tests/example_data/tmp/tmp.json --mw-rest=https://bad_url --from-format=mwtab --to-format=json" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + assert "urlopen error" in subp.stdout + assert subp.returncode == 0 + + +def test_convert_command_error_recovery(teardown_module): + """Test that the convert command can recover from a bad file.""" + command = "python -m mwtab convert tests/example_data/files_to_test_error_recovery tests/example_data/tmp/ --from-format=mwtab --to-format=json" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + assert subp.returncode == 0 + assert "1_no_error.json" in subp.stdout + assert "2_error.json" in subp.stdout + assert "3_no_error.txt" in subp.stdout + assert "ValueError: Blank input string retrieved from source." in subp.stdout + + @pytest.mark.parametrize("command", [ # download by url "python -m mwtab download url https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/summary --to-path=tests/example_data/tmp/tmp.txt", @@ -118,7 +197,7 @@ def test_convert_command(from_path, to_path, from_format, to_format): "python -m mwtab download exactmass \"GlcCer(d42:2)\" M-H --to-path=tests/example_data/tmp/tmp.txt", ]) -def test_download_command(command): +def test_download_command(command, teardown_module): assert os.system(command) == 0 file_str = "" @@ -130,12 +209,277 @@ def test_download_command(command): assert file_str +def test_download_command_cwd(teardown_module_cwd): + assert os.system('python -m mwtab download url https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/summary') == 0 + file_path = os.path.join(os.getcwd(), 'https%3A%2F%2Fwww_metabolomicsworkbench_org%2Frest%2Fstudy%2Fstudy_id%2FST000001%2Fsummary.txt') + + file_str = "" + with open(file_path, "r") as fh: + file_str = fh.read() + fh.close() + with open(file_path, "w") as fh: + fh.close() + assert file_str + + +def test_download_all_analysis_ids(teardown_module, disable_network_calls, disable_sleep, capsys, mocker): + cmdargs = { + '--verbose': True, + '--force': False, + '--silent': False, + '--mw-rest': 'https://www.metabolomicsworkbench.org/rest/', + 'convert': False, + 'validate': False, + 'download': True, + '': None, + 'study': True, + 'all': True, + '--input-item': 'analysis_id' + } + mocker.patch('mwtab.cli.mwrest.analysis_ids', side_effect = [['AN000001', 'AN000002']]) + mocker.patch('mwtab.cli.download_and_save_mwrest_file', side_effect = [None, None]) + + cli.cli(cmdargs) + captured = capsys.readouterr() + assert 'AN000001' in captured.out + assert 'AN000002' in captured.out + +def test_download_all_analysis_ids2(teardown_module, disable_network_calls, disable_sleep, capsys, mocker): + """The same as the previous test, but with --input-item being null to test the default.""" + cmdargs = { + '--verbose': True, + '--force': False, + '--silent': False, + '--mw-rest': 'https://www.metabolomicsworkbench.org/rest/', + 'convert': False, + 'validate': False, + 'download': True, + '': None, + 'study': True, + 'all': True, + '--input-item': 'analysis_id' + } + mocker.patch('mwtab.cli.mwrest.analysis_ids', side_effect = [['AN000001', 'AN000002']]) + mocker.patch('mwtab.cli.download_and_save_mwrest_file', side_effect = [None, None]) + + cli.cli(cmdargs) + captured = capsys.readouterr() + assert 'AN000001' in captured.out + assert 'AN000002' in captured.out + +def test_download_all_study_ids(teardown_module, disable_network_calls, disable_sleep, capsys, mocker): + cmdargs = { + '--verbose': True, + '--force': False, + '--silent': False, + '--mw-rest': 'https://www.metabolomicsworkbench.org/rest/', + 'convert': False, + 'validate': False, + 'download': True, + '': None, + 'study': True, + 'all': True, + '--input-item': 'study_id' + } + mocker.patch('mwtab.cli.mwrest.study_ids', side_effect = [['ST000001', 'ST000002']]) + mocker.patch('mwtab.cli.download_and_save_mwrest_file', side_effect = [None, None]) + + cli.cli(cmdargs) + captured = capsys.readouterr() + assert 'ST000001' in captured.out + assert 'ST000002' in captured.out + +def test_download_all_download_error(teardown_module, disable_network_calls, disable_sleep, capsys, mocker): + cmdargs = { + '--verbose': True, + '--force': False, + '--silent': False, + '--mw-rest': 'https://www.metabolomicsworkbench.org/rest/', + 'convert': False, + 'validate': False, + 'download': True, + '': None, + 'study': True, + 'all': True, + '--input-item': 'analysis_id' + } + mocker.patch('mwtab.cli.mwrest.analysis_ids', side_effect = [['AN000001', 'AN000002']]) + mocker.patch('mwtab.cli.download_and_save_mwrest_file', side_effect = [Exception, None]) + + cli.cli(cmdargs) + captured = capsys.readouterr() + assert 'AN000001' in captured.out + assert 'AN000002' in captured.out + assert 'Something went wrong and AN000001 could not be downloaded.' in captured.out + +def test_download_all_bad_input_item(teardown_module, disable_network_calls, disable_sleep): + cmdargs = { + '--verbose': True, + '--force': False, + '--silent': False, + '--mw-rest': 'https://www.metabolomicsworkbench.org/rest/', + 'convert': False, + 'validate': False, + 'download': True, + '': None, + 'study': True, + 'all': True, + '--input-item': 'asdf' + } + + with pytest.raises(ValueError, match = r'Unknown "--input-item" asdf'): + cli.cli(cmdargs) + +def test_download_study_file_list_analysis(teardown_module, disable_network_calls, disable_sleep, capsys, mocker): + cmdargs = { + '--verbose': True, + '--force': False, + '--silent': False, + '--mw-rest': 'https://www.metabolomicsworkbench.org/rest/', + 'convert': False, + 'validate': False, + 'download': True, + '': None, + 'study': True, + 'all': None, + '--input-item': 'analysis_id', + '': None, + '': 'tests/example_data/other_test_data/download_studies_an_ids.json' + } + mocker.patch('mwtab.cli.download_and_save_mwrest_file', side_effect = [None, None]) + + cli.cli(cmdargs) + captured = capsys.readouterr() + assert 'Found 2 Files to be Downloaded' in captured.out + assert 'AN000001' in captured.out + assert 'AN000002' in captured.out + +def test_download_study_file_list_study(teardown_module, disable_network_calls, disable_sleep, capsys, mocker): + """Same as previous test, but the list is study IDs instead of AN IDs.""" + cmdargs = { + '--verbose': True, + '--force': False, + '--silent': False, + '--mw-rest': 'https://www.metabolomicsworkbench.org/rest/', + 'convert': False, + 'validate': False, + 'download': True, + '': None, + 'study': True, + 'all': None, + '--input-item': 'study_id', + '': None, + '': 'tests/example_data/other_test_data/download_studies_study_ids.json' + } + mocker.patch('mwtab.cli.download_and_save_mwrest_file', side_effect = [None, None]) + + cli.cli(cmdargs) + captured = capsys.readouterr() + assert 'Found 2 Files to be Downloaded' in captured.out + assert 'ST000001' in captured.out + assert 'ST000002' in captured.out + + +def test_download_study_file_list_mixed(teardown_module, disable_network_calls, disable_sleep, capsys, mocker): + """Same as previous test, but the list values are mixed.""" + cmdargs = { + '--verbose': True, + '--force': False, + '--silent': False, + '--mw-rest': 'https://www.metabolomicsworkbench.org/rest/', + 'convert': False, + 'validate': False, + 'download': True, + '': None, + 'study': True, + 'all': None, + '--input-item': None, + '': None, + '': 'tests/example_data/other_test_data/download_studies_mixed_ids.json' + } + mocker.patch('mwtab.cli.download_and_save_mwrest_file', side_effect = [None, None, None, Exception]) + + cli.cli(cmdargs) + captured = capsys.readouterr() + assert 'Found 4 Files to be Downloaded' in captured.out + assert '000001' in captured.out + assert 'AN000002' in captured.out + assert 'ST000003' in captured.out + assert 'Something went wrong and asdf could not be downloaded.' in captured.out + + +def test_download_study_file_list_bad_input_item(teardown_module, disable_network_calls, disable_sleep): + cmdargs = { + '--verbose': True, + '--force': False, + '--silent': False, + '--mw-rest': 'https://www.metabolomicsworkbench.org/rest/', + 'convert': False, + 'validate': False, + 'download': True, + '': None, + 'study': True, + 'all': None, + '--input-item': 'asdf', + '': None, + '': 'tests/example_data/other_test_data/download_studies_mixed_ids.json' + } + + with pytest.raises(ValueError, match = r'Unknown "--input-item" asdf'): + cli.cli(cmdargs) + + +def test_download_study_file_list_bad_to_path(teardown_module, disable_network_calls, disable_sleep, capsys): + cmdargs = { + '--verbose': True, + '--force': False, + '--silent': False, + '--mw-rest': 'https://www.metabolomicsworkbench.org/rest/', + 'convert': False, + 'validate': False, + 'download': True, + '': None, + 'study': True, + 'all': None, + '--to-path': 'filename.txt', + '': None, + '': 'tests/example_data/other_test_data/download_studies_mixed_ids.json' + } + + with pytest.raises(SystemExit): + cli.cli(cmdargs) + captured = capsys.readouterr() + assert ('Error: The given "--to-path" option is a path to a file, ' + 'but the given list of IDs to download is greater ' + 'than 1. Please specify a directory to save the files to.') in captured.out + + +def test_download_url_command_error(teardown_module): + """Test that the download url command can have an error and print appropriately.""" + command = "python -m mwtab download url https://bad_url --to-path=tests/example_data/tmp/tmp.txt" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + assert subp.returncode == 1 + + +def test_download_study_error_recovery(teardown_module): + """Test that the download study command can recover from an error.""" + command = "python -m mwtab download study tests/example_data/other_test_data/download_studies_with_error.json --to-path=tests/example_data/tmp/" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + assert "AN9999999 could not be downloaded" in subp.stdout + assert ('When trying to download a file for the value, "AN000206", ' + 'a blank file or an error was returned, so no file was created for it.') in subp.stdout + assert "ValueError: Invalid Metabolomics Workbench analysis ID for a study (AN<6-digit integer>)" in subp.stdout + assert subp.returncode == 0 + + @pytest.mark.parametrize("from_path, to_path, key, to_format, no_header", [ ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metadata", "SUBJECT_TYPE", "csv", " --no-header"), ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metadata", "SUBJECT_TYPE", "csv", ""), - ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metadata", "SUBJECT_TYPE", "json", "") + ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metadata", "SUBJECT_TYPE", "json", ""), ]) -def test_extract_metadata_command(from_path, to_path, key, to_format, no_header): +def test_extract_metadata_command(from_path, to_path, key, to_format, no_header, teardown_module): command = "python -m mwtab extract metadata {} {} {} --to-format={}{}".format( from_path, to_path, key, to_format, no_header ) @@ -157,43 +501,126 @@ def test_extract_metadata_command(from_path, to_path, key, to_format, no_header) assert False -# @pytest.mark.parametrize("from_path, to_path, key, value, to_format, no_header", [ -# ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "Plant", "csv", " --no-header"), -# ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "Plant", "csv", ""), -# ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "Plant", "json", ""), -# ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites.csv", "SU:SUBJECT_TYPE", "Plant", "csv", ""), -# ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites.json", "SU:SUBJECT_TYPE", "Plant", "json", ""), -# ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "\"r'(Plant)'\"", "csv", " --no-header"), -# ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "\"r'(Plant)'\"", "csv", ""), -# ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "\"r'(Plant)'\"", "json", "") -# ]) -# def test_extract_metabolites_command(from_path, to_path, key, value, to_format, no_header): -# command = "python -m mwtab extract metabolites {} {} {} {} --to-format={}{}".format( -# from_path, to_path, key, value, to_format, no_header -# ) -# assert os.system(command) == 0 -# -# if to_format == "csv": -# filepath = to_path -# if not os.path.splitext(filepath)[1]: -# filepath += ".csv" -# with open(filepath, "r") as fh: -# data = list(csv.reader(fh)) -# if bool(no_header): -# assert set(data[0]) == {"1,2,4-benzenetriol", "1", "1", "24"} -# assert len(data) == 191 -# else: -# assert set(data[0]) == {"metabolite_name", "num-studies", "num_analyses", "num_samples"} -# assert set(data[1]) == {"1,2,4-benzenetriol", "1", "1", "24"} -# assert len(data) == 192 -# fh.close() -# elif to_format == 'json': -# filepath = to_path -# if not os.path.splitext(filepath)[1]: -# filepath += ".json" -# with open(filepath, "r") as fh: -# text = fh.read() -# fh.close() -# assert text -# else: -# assert False +def test_extract_metadata_stdout(): + command = "python -m mwtab extract metadata tests/example_data/mwtab_files/ - SUBJECT_TYPE --to-format=json" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + assert subp.returncode == 0 + assert "{'SUBJECT_TYPE': {'Human'}}" == subp.stdout[:-1] + + +def test_extract_metadata_error_recovery(teardown_module): + """Test that the extract metadata command can recover from an error.""" + command = "python -m mwtab extract metadata tests/example_data/files_to_test_error_recovery tests/example_data/tmp/ SUBJECT_TYPE" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + assert subp.returncode == 0 + assert "1_no_error.json" in subp.stdout + assert "2_error.json" in subp.stdout + assert "3_no_error.txt" in subp.stdout + assert "ValueError: Blank input string retrieved from source." in subp.stdout + + +def test_extract_metadata_no_data_extracted(teardown_module): + """Test that the extract metadata command prints a message when no data is extracted and no file is made.""" + command = "python -m mwtab extract metadata tests/example_data/mwtab_files/ tests/example_data/tmp/ asdf" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + assert not os.path.exists("tests/example_data/tmp/") + assert subp.returncode == 0 + assert "No metadata extracted. No file was saved." in subp.stdout + + +@pytest.mark.parametrize("from_path, to_path, key, value, to_format, no_header", [ + ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "Human", "csv", " --no-header"), + ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "Human", "csv", ""), + ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "Human", "json", ""), + ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites.csv", "SU:SUBJECT_TYPE", "Human", "csv", ""), + ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites.json", "SU:SUBJECT_TYPE", "Human", "json", ""), + ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "\"r'(Human)'\"", "csv", " --no-header"), + ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "\"r'(Human)'\"", "csv", ""), + ("tests/example_data/mwtab_files/", "tests/example_data/tmp/test_extract_metabolites", "SU:SUBJECT_TYPE", "\"r'(Human)'\"", "json", "") +]) +def test_extract_metabolites_command(from_path, to_path, key, value, to_format, no_header, teardown_module): + command = "python -m mwtab extract metabolites {} {} {} {} --to-format={}{}".format( + from_path, to_path, key, value, to_format, no_header + ) + assert os.system(command) == 0 + + expected_data = [['17-hydroxypregnenolone', '1', '1', '13'], + ['17-hydroxyprogesterone', '1', '1', '29'], + ['Allodihydrotestosterone', '1', '1', '18'], + ['Androstenedione', '1', '1', '42'], + ['Androstenolone (DHEA)', '1', '1', '42'], + ['Cortexolone', '1', '1', '21'], + ['Cortexone', '1', '1', '41'], + ['Corticosterone_ DOC', '1', '1', '29'], + ['Cortisol', '1', '1', '42'], + ['Estradiol', '1', '1', '42'], + ['Estrone', '1', '1', '42'], + ['Pregnenolone', '1', '1', '12'], + ['Progesterone', '1', '1', '39'], + ['Testosterone', '1', '1', '42']] + + if to_format == "csv": + filepath = to_path + if not os.path.splitext(filepath)[1]: + filepath += ".csv" + with open(filepath, "r") as fh: + data = list(csv.reader(fh)) + if bool(no_header): + assert data == expected_data + else: + assert data[0] == ["metabolite_name", "num-studies", "num_analyses", "num_samples"] + assert data[1:] == expected_data + fh.close() + elif to_format == 'json': + filepath = to_path + if not os.path.splitext(filepath)[1]: + filepath += ".json" + with open(filepath, "r") as fh: + text = fh.read() + fh.close() + assert text + else: + assert False + + +def test_extract_metabolites_stdout(): + command = "python -m mwtab extract metabolites tests/example_data/mwtab_files/ - SU:SUBJECT_TYPE Human --to-format=json" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + assert subp.returncode == 0 + assert '{\n "17-hydroxypregnenolone": {\n "ST000122": {' in subp.stdout + + +def test_extract_metabolites_error_recovery(teardown_module): + """Test that the extract metabolites command can recover from an error.""" + command = "python -m mwtab extract metabolites tests/example_data/files_to_test_error_recovery tests/example_data/tmp/ SU:SUBJECT_TYPE Human" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + # print() + # print("Output:") + # print(subp.stdout) + # print() + # print("Error:") + # print(subp.stderr) + assert subp.returncode == 0 + assert "1_no_error.json" in subp.stdout + assert "2_error.json" in subp.stdout + assert "3_no_error.txt" in subp.stdout + assert "ValueError: Blank input string retrieved from source." in subp.stdout + + +def test_extract_metabolites_no_data_extracted(teardown_module): + """Test that the extract metabolites command prints a message when no data is extracted and no file is made.""" + command = "python -m mwtab extract metabolites tests/example_data/mwtab_files/ tests/example_data/tmp/ SU:SUBJECT_TYPE Plant" + command = command.split(" ") + subp = subprocess.run(command, capture_output=True, encoding="UTF-8") + assert not os.path.exists("tests/example_data/tmp/") + assert subp.returncode == 0 + assert ("No metabolites extracted. No file was saved. " + "This is likely due to key value pairs filtering all of the studies out. " + "Check your key value pairs and data.") in subp.stdout + + diff --git a/tests/test_converter.py b/tests/test_converter.py index b7867ee..e400065 100755 --- a/tests/test_converter.py +++ b/tests/test_converter.py @@ -1,11 +1,15 @@ import os -import shutil import pytest import mwtab from json import loads + +from fixtures import teardown_module + from mwtab.converter import Converter +teardown_module = teardown_module + ITEM_SECTIONS = { # "METABOLOMICS WORKBENCH", "PROJECT", @@ -21,10 +25,6 @@ } -def teardown_module(module): - if os.path.exists("tests/example_data/tmp"): - shutil.rmtree("tests/example_data/tmp") - def compare_item_sections(dict1, dict2): """ @@ -44,13 +44,14 @@ def compare_item_sections(dict1, dict2): assert not keys1 ^ keys2 for key in keys1 & keys2: + print(key) assert dict1[key] == dict2[key] @pytest.mark.parametrize("mwtab_file_path, json_file_path", [ ("tests/example_data/mwtab_files/ST000122_AN000204.txt", "tests/example_data/mwtab_files/ST000122_AN000204.json") ]) -def test_convert_mwtab_to_json(mwtab_file_path, json_file_path): +def test_convert_mwtab_to_json(mwtab_file_path, json_file_path, teardown_module): """ """ @@ -58,14 +59,14 @@ def test_convert_mwtab_to_json(mwtab_file_path, json_file_path): mwfile = next(mwtab.read_files(mwtab_file_path)) if not os.path.exists("tests/example_data/tmp/"): os.makedirs("tests/example_data/tmp/") - with open("tests/example_data/tmp/tmp.json", "w") as f: + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: mwfile.write(f, file_format="json") f.close() # open files - with open("tests/example_data/tmp/tmp.json", "r") as f: + with open("tests/example_data/tmp/tmp.json", "r", encoding="utf-8") as f: mwtab_file = loads(f.read()) - with open(json_file_path, "r") as f: + with open(json_file_path, "r", encoding="utf-8") as f: json_file = loads(f.read()) # assert both files contain the same sections @@ -73,6 +74,7 @@ def test_convert_mwtab_to_json(mwtab_file_path, json_file_path): # Assert item sections are equal for section_key in ITEM_SECTIONS: + print(section_key) if section_key in set(mwtab_file.keys()) & set(json_file.keys()): compare_item_sections(mwtab_file[section_key], json_file[section_key]) @@ -145,3 +147,146 @@ def test_converter_module(from_path, to_path, from_format, to_format): mwtabfiles_analysis_ids_set = set(mwf.analysis_id for mwf in mwtabfiles_list) assert mwtabfiles_study_ids_set.issubset({"ST000122"}) assert mwtabfiles_analysis_ids_set.issubset({"AN000204"}) + + +@pytest.mark.parametrize("from_path, to_path, from_format, to_format, error, message", [ + ("nonexistent_file.txt", "tests/example_data/tmp/json/ST000122_AN000204.json", "mwtab", "json", FileNotFoundError, r'No such file or directory: "nonexistent_file.txt"'), + ("tests/example_data/other_test_data/unknown_file_format.asdf", "tests/example_data/tmp/json/ST000122_AN000204.json", "mwtab", "json", TypeError, r'Unknown input file format: "tests/example_data/other_test_data/unknown_file_format.asdf"'), +]) +def test_convert_exceptions(teardown_module, from_path, to_path, from_format, to_format, error, message, mocker): + converter = Converter(from_path=from_path, + to_path=to_path, + from_format=from_format, + to_format=to_format) + + # I can't find a way to trigger the last else without mocking, but I don't want to remove the line either. + if error is TypeError: + mocker.patch('mwtab.converter.os.path.isfile', side_effect = [False]) + + with pytest.raises(error, match = message): + converter.convert() + + +@pytest.mark.parametrize("from_path, to_path, from_format, to_format, error, message", [ + ("tests/example_data/mwtab_files/ST000122_AN000204.txt", "bad_path.gz", "mwtab", "json", TypeError, r'Many-to-one conversion, cannot convert "tests/example_data/mwtab_files/ST000122_AN000204.txt" into "bad_path.gz"'), + ("tests/example_data/mwtab_files/ST000122_AN000204.txt", "tests/example_data/other_test_data/unknown_file_format.asdf", "mwtab", "json", TypeError, r'Unknown output file format: "tests/example_data/other_test_data/unknown_file_format.asdf"'), +]) +def test_many_to_many_exceptions(teardown_module, from_path, to_path, from_format, to_format, error, message): + converter = Converter(from_path=from_path, + to_path=to_path, + from_format=from_format, + to_format=to_format) + + # Can't get the last else line to trigger any other way than to do something like this. + if 'unknown_file_format' in to_path: + converter.file_generator.to_path_compression = '.asdf' + + with pytest.raises(error, match = message): + converter._many_to_many() + + +@pytest.mark.parametrize("from_path, to_path, from_format, to_format, error, message", [ + ("tests/example_data/mwtab_files/ST000122_AN000204.txt", "bad_path.tar", "mwtab", "json", TypeError, r'One-to-many conversion, cannot convert "tests/example_data/mwtab_files/ST000122_AN000204.txt" into "bad_path.tar"'), + ("tests/example_data/mwtab_files/ST000122_AN000204.txt", "tests/example_data/other_test_data/unknown_file_format.asdf", "mwtab", "json", TypeError, r'Unknown output file format: "tests/example_data/other_test_data/unknown_file_format.asdf"'), +]) +def test_one_to_one_exceptions(teardown_module, from_path, to_path, from_format, to_format, error, message): + converter = Converter(from_path=from_path, + to_path=to_path, + from_format=from_format, + to_format=to_format) + + # Can't get the last else line to trigger any other way than to do something like this. + if 'unknown_file_format' in to_path: + converter.file_generator.to_path_compression = '.asdf' + + with pytest.raises(error, match = message): + converter._one_to_one() + + +def test_to_dir_exceptions(teardown_module, capsys, mocker): + converter = Converter("tests/example_data/mwtab_files/ST000122_AN000204.txt", "tests/example_data/tmp/json/ST000122_AN000204.json", "mwtab", "json") + + # Simulate an exception with this mock. + mocker.patch('mwtab.converter.os.path.exists', side_effect = [True, False]) + converter._to_dir(converter.file_generator) + captured = capsys.readouterr() + assert 'Something went wrong when trying to convert' in captured.out + + +def test_to_zipfile_exceptions(teardown_module, capsys, mocker): + converter = Converter("tests/example_data/mwtab_files/ST000122_AN000204.txt", "tests/example_data/tmp/json.zip", "mwtab", "json") + + # Simulate an exception with this mock. + mocker.patch('mwtab.converter.Converter._output_path', side_effect = [TypeError]) + + # None of the private methods check for this, since convert() does it, so it must be created here. + if not os.path.exists('tests/example_data/tmp'): + os.makedirs('tests/example_data/tmp') + + converter._to_zipfile(converter.file_generator) + captured = capsys.readouterr() + assert 'Something went wrong when trying to convert' in captured.out + + +def test_to_tarfile_exceptions(teardown_module, capsys, mocker): + converter = Converter("tests/example_data/mwtab_files/ST000122_AN000204.txt", "tests/example_data/tmp/json.tar", "mwtab", "json") + + # This is not necessary for the exception, but tests the last else line. + converter.file_generator.to_path_compression = 'asdf' + + # Simulate an exception with this mock. + mocker.patch('mwtab.converter.Converter._output_path', side_effect = [TypeError]) + + # None of the private methods check for this, since convert() does it, so it must be created here. + if not os.path.exists('tests/example_data/tmp'): + os.makedirs('tests/example_data/tmp') + + converter._to_tarfile(converter.file_generator) + captured = capsys.readouterr() + assert 'Something went wrong when trying to convert' in captured.out + + +def test_to_bz2file_exceptions(teardown_module, capsys): + converter = Converter("tests/example_data/mwtab_files/ST000122_AN000204.txt", "tests/example_data/tmp/json.bz2", "mwtab", "json") + + converter.file_generator.to_format = 0 + + # None of the private methods check for this, since convert() does it, so it must be created here. + if not os.path.exists('tests/example_data/tmp'): + os.makedirs('tests/example_data/tmp') + + converter._to_bz2file(converter.file_generator) + captured = capsys.readouterr() + assert 'Something went wrong when trying to convert' in captured.out + + +def test_to_gzipfile_exceptions(teardown_module, capsys): + converter = Converter("tests/example_data/mwtab_files/ST000122_AN000204.txt", "tests/example_data/tmp/json.gz", "mwtab", "json") + + converter.file_generator.to_format = 0 + + # None of the private methods check for this, since convert() does it, so it must be created here. + if not os.path.exists('tests/example_data/tmp'): + os.makedirs('tests/example_data/tmp') + + converter._to_gzipfile(converter.file_generator) + captured = capsys.readouterr() + assert 'Something went wrong when trying to convert' in captured.out + + +def test_to_textfile_exceptions(teardown_module, capsys, mocker): + converter = Converter("tests/example_data/mwtab_files/ST000122_AN000204.txt", "tests/example_data/tmp/txt.txt", "mwtab", "mwtab") + + mocker.patch('mwtab.fileio.mwtab.MWTabFile.writestr', side_effect = [TypeError]) + + # None of the private methods check for this, since convert() does it, so it must be created here. + if not os.path.exists('tests/example_data/tmp'): + os.makedirs('tests/example_data/tmp') + + converter._to_textfile(converter.file_generator) + captured = capsys.readouterr() + assert 'Something went wrong when trying to convert' in captured.out + + + + diff --git a/tests/test_duplicates_dict.py b/tests/test_duplicates_dict.py new file mode 100644 index 0000000..649289b --- /dev/null +++ b/tests/test_duplicates_dict.py @@ -0,0 +1,59 @@ +# -*- coding: utf-8 -*- + +import copy + +from mwtab.duplicates_dict import DuplicatesDict + + +""" +Most functionality is tested through its utilization throughout the package, so these tests are just to get 100% coverage. +""" + +def test_init_from_dd(): + test_dict = DuplicatesDict() + test_dict['a'] = 1 + test_dict2 = DuplicatesDict(test_dict) + assert test_dict == test_dict2 + +def test_len(): + test_dict = DuplicatesDict() + test_dict['a'] = 1 + assert len(test_dict) == 1 + +def test_delitem(): + test_dict = DuplicatesDict() + test_dict['a'] = 1 + del test_dict['a'] + assert 'a' not in test_dict + +def test_raw_keys(): + test_dict = DuplicatesDict() + test_dict['a'] = 1 + test_dict['a'] = 2 + raw_keys = test_dict.raw_keys() + assert 'a' in raw_keys + assert 'a{{{_1_}}}' in raw_keys + +def test_update(): + test_dict = DuplicatesDict() + test_dict.update({'a':1}) + assert 'a' in test_dict + assert test_dict['a'] == 1 + +def test_copy(): + test_dict = DuplicatesDict() + test_dict['a'] = 1 + test_dict2 = copy.copy(test_dict) + assert test_dict == test_dict2 + assert id(test_dict) != id(test_dict2) + +def test_not_equal(): + test_dict = DuplicatesDict() + test_dict['a'] = 1 + test_dict2 = DuplicatesDict() + test_dict2['b'] = 2 + assert test_dict != test_dict2 + + + + diff --git a/tests/test_fileio.py b/tests/test_fileio.py new file mode 100644 index 0000000..a3ecd22 --- /dev/null +++ b/tests/test_fileio.py @@ -0,0 +1,98 @@ +# -*- coding: utf-8 -*- + +import pytest + +from mwtab import fileio + + +""" +Most functionality is tested through its utilization throughout the package, so these tests are just to get 100% coverage. +""" + +def test_return_correct_yield(): + with pytest.raises(TypeError): + fileio._return_correct_yield({}, TypeError(), False) + + +def test_generate_filenames_exception(mocker): + mocker.patch('mwtab.fileio.os.path.isdir', side_effect = [Exception]) + with pytest.raises(Exception): + next(fileio._generate_filenames(['tests/example_data/mwtab_files/'])) + + +def test_generate_handles_exception(): + generator = fileio._generate_handles([('name1', Exception()), ('name2', None)]) + with pytest.raises(Exception): + next(generator) + with pytest.raises(StopIteration): + next(generator) + +def test_generate_handles_exception2(): + generator = fileio._generate_handles([('name1', Exception()), ('name2', None)], True) + value1 = next(generator) + assert value1[0] is None + assert value1[1] == 'name1' + assert isinstance(value1[2], Exception) + + value2 = next(generator) + assert value2[0] is None + assert value2[1] == 'name2' + assert isinstance(value2[2], FileNotFoundError) + + +def test_read_files_exceptions(mocker): + with pytest.raises(TypeError, match = r'Unknown file source.'): + next(fileio.read_files(['some_path'])) + + mocker.patch('mwtab.fileio._generate_filenames', side_effect = [Exception]) + with pytest.raises(Exception): + next(fileio.read_files(['some_path'])) + +def test_read_files_exceptions2(mocker, capsys): + fileio.VERBOSE = True + mocker.patch('mwtab.mwtab.MWTabFile.read', side_effect = [Exception]) + with pytest.raises(Exception): + next(fileio.read_files(['tests/example_data/mwtab_files/ST000122_AN000204.txt'])) + captured = capsys.readouterr() + assert 'Error processing file:' in captured.out + + +def test_GenericFilePath(): + web_address = 'https://github.com/MoseleyBioinformaticsLab/mwtab/archive/refs/heads/main.zip' + filehandle, source = next(fileio.GenericFilePath(web_address).open()) + filehandle.read() + assert web_address in source + +def test_GenericFilePath_is_url(mocker): + mocker.patch('mwtab.fileio.urlparse', side_effect = [ValueError]) + assert fileio.GenericFilePath.is_url('asdf') == False + + +def test_read_lines(mocker, capsys): + """We are really testing the ReadLines class, but doing it through the read_lines function.""" + fileio.VERBOSE = True + lines = next(fileio.read_lines('tests/example_data/mwtab_files/ST000122_AN000204.txt')).lines + captured = capsys.readouterr() + assert 'Processed file: ' in captured.out + assert lines[0].startswith('#METABOLOMICS WORKBENCH') + + mocker.patch('mwtab.fileio._generate_handles', side_effect = [((open('tests/example_data/mwtab_files/ST000122_AN000204.txt', 'rb'), 'asdf', None) for a in [1])]) + lines = next(fileio.read_lines('tests/example_data/mwtab_files/ST000122_AN000204.txt')).lines + assert lines[0].startswith('#METABOLOMICS WORKBENCH') + +def test_read_lines_exceptions(mocker, capsys): + """We are really testing the ReadLines class, but doing it through the read_lines function.""" + fileio.VERBOSE = True + class mock_class: + def __init__(self, *args, **kwargs): + pass + def read(self): + return 1 + def close(self): + pass + mocker.patch('mwtab.fileio._generate_handles', side_effect = [((mock_class(), 'asdf', None) for a in [1])]) + with pytest.raises(TypeError, match = r"Expecting or ,"): + next(fileio.read_lines(['tests/example_data/mwtab_files/ST000122_AN000204.txt'])) + captured = capsys.readouterr() + assert 'Error processing file:' in captured.out + diff --git a/tests/test_metadata_column_matching.py b/tests/test_metadata_column_matching.py new file mode 100644 index 0000000..caccf5d --- /dev/null +++ b/tests/test_metadata_column_matching.py @@ -0,0 +1,43 @@ +# -*- coding: utf-8 -*- + +import pandas + +from mwtab import metadata_column_matching + + +""" +Most functionality is tested through its utilization throughout the package, so these tests are just to get 100% coverage. +""" + + +def test_ValueMatcher(): + vm1 = metadata_column_matching.ValueMatcher('integer', None, None) + series = pandas.Series(['a', '1', '2.3', '4']) + series_match = vm1.series_match(series, None) + expected_match = pandas.Series([False, True, False, True]) + assert series_match.equals(expected_match) + + # These conflict, so nothing should match. + vm1 = metadata_column_matching.ValueMatcher('integer', r'a', None) + series = pandas.Series(['a', '1', '2.3', '4']) + series_match = vm1.series_match(series, None) + expected_match = pandas.Series([False, False, False, False]) + assert series_match.equals(expected_match) + + vm1 = metadata_column_matching.ValueMatcher('numeric', None, None) + series = pandas.Series(['a', '1', '2.3', '4']) + series_match = vm1.series_match(series, None) + expected_match = pandas.Series([False, True, True, True]) + assert series_match.equals(expected_match) + + + + + + + + + + + + diff --git a/tests/test_mwextract.py b/tests/test_mwextract.py index b140f44..ab3f79a 100644 --- a/tests/test_mwextract.py +++ b/tests/test_mwextract.py @@ -1,3 +1,30 @@ import pytest + +from mwtab import mwextract import mwtab + +""" +Most functionality is tested through its utilization throughout the package, so these tests are just to get 100% coverage. +""" + + + +def test_SetEncoder(): + with pytest.raises(TypeError, match = r'Object of type str is not JSON serializable'): + mwextract.SetEncoder().default('a') + +def test_extract_metabolites_exception(): + mwtabfile = mwtab.mwtab.MWTabFile('asdf') + mwtabfile['MS_METABOLITE_DATA'] = {'Data':[{'key1':'asdf'}]} + assert mwextract.extract_metabolites([(mwtabfile, None)], [lambda x: True]) == {} + + + + + + + + + + diff --git a/tests/test_mwrest.py b/tests/test_mwrest.py index a64b589..044b916 100755 --- a/tests/test_mwrest.py +++ b/tests/test_mwrest.py @@ -1,5 +1,5 @@ import pytest -from mwtab.mwrest import BASE_URL, GenericMWURL, analysis_ids, study_ids +from mwtab.mwrest import GenericMWURL, analysis_ids, study_ids, generate_mwtab_urls, MWRESTFile def test_study_analysis(): @@ -56,7 +56,318 @@ def test_mwrest(kwds): ]) def test_fail_mwrest(kwds): try: - test_mwurl = GenericMWURL(kwds) + GenericMWURL(kwds) assert False except Exception as e: assert type(e) == ValueError + + +def test_generate_mwtab_urls(): + assert next(generate_mwtab_urls(['ST000001'])) == 'https://www.metabolomicsworkbench.org/rest/study/study_id/ST000001/mwtab/txt' + +def test_generate_mwtab_urls_exception(mocker): + mocker.patch('mwtab.mwrest.fileio._return_correct_yield', side_effect = [Exception]) + with pytest.raises(Exception): + next(generate_mwtab_urls(['ST000001'])) + +def test_GenericMWURL_missing_context(): + with pytest.raises(KeyError, match = r'context'): + GenericMWURL({"input_item": "analysis_id", + "input_value": "AN000001", + "output_item": "mwtab", + 'output_format': "txt"}) + +def test_GenericMWURL_bad_context(): + with pytest.raises(ValueError, match = r'Error: Invalid/missing context'): + GenericMWURL({"context": "asdf", + "input_item": "analysis_id", + "input_value": "AN000001", + "output_item": "mwtab", + 'output_format': "txt"}) + +def test_GenericMWURL_missing_input_item(): + with pytest.raises(KeyError, match = r"Missing input item\(s\): \{'input_item'\}"): + GenericMWURL({"context": "study", + "input_value": "AN000001", + "output_item": "mwtab", + 'output_format': "txt"}) + +def test_GenericMWURL_bad_input_item(): + with pytest.raises(ValueError, match = r"Invalid input item"): + GenericMWURL({"context": "study", + "input_item": "asdf", + "input_value": "AN000001", + "output_item": "mwtab", + 'output_format': "txt"}) + +@pytest.mark.parametrize("kwds, message", [ + ({"context": "study", + "input_item": "analysis_id", + "input_value": "AN000001", + "output_item": ['asdf'], + 'output_format': "txt"}, + r'Invalid output items. Study only takes a single output item.'), + + ({"context": "compound", + "input_item": "formula", + "input_value": "C3H6O2", + "output_item": ['asdf'], + 'output_format': "txt"}, + r"Invalid output item\(s\): \{'asdf'\}"), + + ({"context": "study", + "input_item": "analysis_id", + "input_value": "AN000001", + "output_item": "asdf", + 'output_format': "txt"}, + r'Invalid output item'), +]) +def test_GenericMWURL_bad_output_item(kwds, message): + with pytest.raises(ValueError, match = message): + GenericMWURL(kwds) + + +@pytest.mark.parametrize("kwds, error, message", [ + ({"context": "moverz", + "input_item": "LIPIDS", + "ion_type_value": 'M+H', + 'm/z_tolerance_value': ".01"}, + KeyError, + r"Missing input item\(s\): \{'m/z_value'\}"), + + ({"context": "moverz", + "input_item": "asdf", + "m/z_value": "58", + "ion_type_value": 'M+H', + 'm/z_tolerance_value': ".01"}, + ValueError, + r"Invalid input item"), + + ({"context": "moverz", + "input_item": "LIPIDS", + "m/z_value": "58", + "ion_type_value": 'asdf', + 'm/z_tolerance_value': "txt"}, + ValueError, + r'Invalid ion type value'), + + ({"context": "moverz", + "input_item": "LIPIDS", + "m/z_value": "58", + "ion_type_value": 'M+H', + 'm/z_tolerance_value': "2"}, + ValueError, + r'm/z tolerance value outside of range: 0.0001-1'), +]) +def test_GenericMWURL_moverz_validate(kwds, error, message): + with pytest.raises(error, match = message): + GenericMWURL(kwds) + + +@pytest.mark.parametrize("kwds, error, message", [ + ({"context": "exactmass", + "LIPID_abbreviation": "LIPIDS"}, + KeyError, + r"Missing input item\(s\): \{'ion_type_value'\}"), + + ({"context": "exactmass", + "LIPID_abbreviation": "LIPIDS", + "ion_type_value": 'asdf'}, + ValueError, + r"Invalid ion type value"), +]) +def test_GenericMWURL_exactmass_validate(kwds, error, message): + with pytest.raises(error, match = message): + GenericMWURL(kwds) + + +@pytest.mark.parametrize("kwds, error, message", [ + ({"context": "study", + "input_item": "study_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid Metabolomics Workbench \(MW\) study ID \(ST<6-digit integer>\)"), + + ({"context": "study", + "input_item": "study_title", + "input_value": 1, + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid study title \(\)"), + + ({"context": "study", + "input_item": "metabolite_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid Metabolomics Workbench metabolite ID for a study \(ME<6-digit integer>\)"), + + ({"context": "compound", + "input_item": "regno", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid Metabolomics Workbench Metabolite database ID \(\)"), + + ({"context": "compound", + "input_item": "inchi_key", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid InChIKey \(27-character string\)"), + + ({"context": "compound", + "input_item": "lm_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid LIPID MAPS ID \(LM<2-character LIPID MAPS category><8-10 character string>\)"), + + ({"context": "compound", + "input_item": "pubchem_cid", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid PubChem Compound ID \(\)"), + + ({"context": "compound", + "input_item": "hmdb_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid Human Metabolome Database ID \(HMDB\)"), + + ({"context": "compound", + "input_item": "kegg_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid KEGG compound ID \(CO\)"), + + ({"context": "compound", + "input_item": "chebi_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid ChEBI compound id \(\)"), + + ({"context": "compound", + "input_item": "metacyc_id", + "input_value": 1, + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid METACYC compound ID \(\)"), + + ({"context": "compound", + "input_item": "abbrev", + "input_value": 1, + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid : Lipid bulk abbreviation \(\)"), + + ({"context": "protein", + "input_item": "mgp_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid Human Metabolome Gene/Protein \(MGP\) database gene ID \(MGP<6-digit integer>\)"), + + ({"context": "protein", + "input_item": "gene_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid Entrez gene ID \(\)"), + + ({"context": "protein", + "input_item": "taxid", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid NCBI taxonomy ID \(\)"), + + ({"context": "protein", + "input_item": "mrna_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid mRNA ID \(NM_\)"), + + ({"context": "protein", + "input_item": "refseq_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid mRNA ID \(NP_\)"), + + ({"context": "protein", + "input_item": "protein_gi", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid NCBI protein GI \(\)"), + + ({"context": "protein", + "input_item": "uniprot_id", + "input_value": "AN000001", + "output_item": 'summary', + 'output_format': "txt"}, + ValueError, + r"Invalid UniProt ID \(see uniprot.org/help/accession_numbers\)"), +]) +def test_GenericMWURL_validate_input(kwds, error, message): + with pytest.raises(error, match = message): + GenericMWURL(kwds) + + +def test_MWRESTFile_write(): + class IOError_maker(): + def write(self, text): + raise IOError + + mwfile = MWRESTFile('asdf') + with pytest.raises(IOError, match = r'"filehandle" parameter must be writable\.'): + mwfile.write(IOError_maker()) + + +def test_MWRESTFile_is_json(): + mwfile = MWRESTFile('asdf') + + assert mwfile._is_json(b'{"key1":1}') == {'key1':1} + + with pytest.raises(TypeError, match = r"Expecting or , but was passed"): + mwfile._is_json([]) + + assert mwfile._is_json('[]]') == False + + + + + + + + + + + + + diff --git a/tests/test_mwschema.py b/tests/test_mwschema.py new file mode 100644 index 0000000..b02a70d --- /dev/null +++ b/tests/test_mwschema.py @@ -0,0 +1,13 @@ + +from mwtab import mwschema + + +""" +Most functionality is tested through its utilization throughout the package, so these tests are just to get 100% coverage. +""" + +def test_create_unit_error_message(): + assert ' should be a unitless integer. Ignore this when more complicated descriptions are required.' == mwschema.create_unit_error_message(integer=True, no_units=True) + + + diff --git a/tests/test_mwtabfile.py b/tests/test_mwtabfile.py new file mode 100644 index 0000000..7619730 --- /dev/null +++ b/tests/test_mwtabfile.py @@ -0,0 +1,653 @@ +# -*- coding: utf-8 -*- + +import mwtab +import os +from json import loads +import copy +import io +import json + +import pandas +import pytest +# This is an autouse module, so it is being used by every test simply by importing without calling it directly. +from fixtures import teardown_module_auto, init_tmp_dir + +init_tmp_dir = init_tmp_dir + + + +def test_read(): + # mwtab + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt") + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + assert mwtabfile['MS_METABOLITE_DATA']['Data'][0]['Metabolite'] == '17-hydroxypregnenolone' + assert mwtabfile._input_format == 'mwtab' + + # json + mwtabfile2 = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.json") + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.json", "r", encoding="utf-8") as f: + mwtabfile2.read(f) + + assert mwtabfile2['MS_METABOLITE_DATA']['Data'][0]['Metabolite'] == '17-hydroxypregnenolone' + assert mwtabfile2._input_format == 'json' + + # bytes + mwtabfile2 = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.json") + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.json", "rb") as f: + mwtabfile2.read(f) + + assert mwtabfile2['MS_METABOLITE_DATA']['Data'][0]['Metabolite'] == '17-hydroxypregnenolone' + assert mwtabfile2._input_format == 'json' + + # binned json + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_binned.json") + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_binned.json", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + assert mwtabfile['NMR_BINNED_DATA']['Data'][0]['Metabolite'] == '17-hydroxypregnenolone' + + # binned mwtab + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + assert mwtabfile['NMR_BINNED_DATA']['Data'][0]['Metabolite'] == '0.4...0.46' + +def test_read_errors(): + with pytest.raises(TypeError, match = r'^Unknown file format'): + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/bad_file.txt") + with open("tests/example_data/other_mwtab_files/bad_file.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + with pytest.raises(KeyError, match = r'^\'Given JSON contains duplicate keys at the highest level\.'): + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys_highest_level.json", duplicate_keys=True) + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys_highest_level.json", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + with pytest.raises(TypeError, match = r'^Given JSON contains non-dictionary values in \["MS_METABOLITE_DATA"\]\["Data"\]\.'): + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_bad_table_type.json") + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_bad_table_type.json", "r", encoding="utf-8") as f: + mwtabfile.read(f) + +def test_read_with_force(): + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_bad_table_type.json", force=True) + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_bad_table_type.json", "r", encoding="utf-8") as f: + mwtabfile.read(f) + assert mwtabfile['MS_METABOLITE_DATA']['Data'][0] != 'asdf' + + +def test_reading_results_file(): + results_file_dict = {'filename': 'ST000071_AN000111_Results.txt', + 'UNITS': 'Peak area', + 'Has m/z': 'Yes', + 'Has RT': 'Yes', + 'RT units': 'Minutes'} + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041_results_file.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041_results_file.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + assert mwtabfile['NM']['NMR_RESULTS_FILE'] == results_file_dict + + + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_results_file.txt") + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_results_file.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + assert mwtabfile['MS']['MS_RESULTS_FILE'] == results_file_dict + + + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041_results_file_bad_location.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041_results_file_bad_location.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + assert mwtabfile['NM']['NMR_RESULTS_FILE'] == results_file_dict + + + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_results_file_bad_location.txt") + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_results_file_bad_location.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + assert mwtabfile['MS']['MS_RESULTS_FILE'] == results_file_dict + + +def test_reading_duplicate_subsections(): + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_subsections.txt") + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_subsections.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + assert mwtabfile['METABOLOMICS WORKBENCH']['STUDY_ID'] == 'ST000123' + assert mwtabfile['STUDY']['NUM_GROUPS'] == 'NA NA' + + + +def test_write(): + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt") + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + if not os.path.exists(os.path.dirname("tests/example_data/tmp/tmp.json")): + dirname = os.path.dirname("tests/example_data/tmp/tmp.json") + if dirname: + os.makedirs(dirname) + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="json") + + mwtabfile2 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.json") + with open("tests/example_data/tmp/tmp.json", "r", encoding="utf-8") as f: + mwtabfile2.read(f) + assert mwtabfile['MS_METABOLITE_DATA']['Data'][0]['Metabolite'] == '17-hydroxypregnenolone' + + + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="json") + + mwtabfile2 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.json") + with open("tests/example_data/tmp/tmp.json", "r", encoding="utf-8") as f: + mwtabfile2.read(f) + assert mwtabfile['NMR_BINNED_DATA']['Data'][0]['Metabolite'] == '0.4...0.46' + + + with pytest.raises(IOError, match = r'^"filehandle" parameter must be writable'): + with open("tests/example_data/tmp/tmp.json", "r", encoding="utf-8") as f: + mwtabfile.write(f, file_format="json") + + with pytest.raises(TypeError, match = r'^Unknown file format'): + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="asdf") + + with pytest.raises(TypeError, match = r'^Unknown file format'): + mwtabfile.writestr(file_format="asdf") + + +def test_writing_results_file(): + results_file_dict = {'filename': 'ST000071_AN000111_Results.txt', + 'UNITS': 'Peak area', + 'Has m/z': 'Yes', + 'Has RT': 'Yes', + 'RT units': 'Minutes'} + + if not os.path.exists(os.path.dirname("tests/example_data/tmp/tmp.json")): + dirname = os.path.dirname("tests/example_data/tmp/tmp.json") + if dirname: + os.makedirs(dirname) + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041_results_file.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041_results_file.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + # write json + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="json") + + mwtabfile2 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.json") + with open("tests/example_data/tmp/tmp.json", "r", encoding="utf-8") as f: + mwtabfile2.read(f) + assert mwtabfile2['NM']['NMR_RESULTS_FILE'] == results_file_dict + + # write mwtab + with open("tests/example_data/tmp/tmp.txt", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="mwtab") + + mwtabfile3 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.txt") + with open("tests/example_data/tmp/tmp.txt", "r", encoding="utf-8") as f: + mwtabfile3.read(f) + assert mwtabfile3['NM']['NMR_RESULTS_FILE'] == results_file_dict + + # write results file line missing filename + del mwtabfile['NM']['NMR_RESULTS_FILE']['filename'] + del results_file_dict['filename'] + with open("tests/example_data/tmp/tmp.txt", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="mwtab") + + mwtabfile4 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.txt") + with open("tests/example_data/tmp/tmp.txt", "r", encoding="utf-8") as f: + mwtabfile4.read(f) + assert mwtabfile4['NM']['NMR_RESULTS_FILE'] == results_file_dict + + +def test_write_extended_section(): + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + if not os.path.exists(os.path.dirname("tests/example_data/tmp/tmp.json")): + dirname = os.path.dirname("tests/example_data/tmp/tmp.json") + if dirname: + os.makedirs(dirname) + + mwtabfile['NMR_BINNED_DATA']['Extended'] = [] + df = pandas.DataFrame([['some name', 'some value'], ['some name 2', 'some value 2']], columns = ['Metabolite', 'column1']) + mwtabfile.set_extended_from_pandas(df, True) + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="json") + + mwtabfile2 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.json") + with open("tests/example_data/tmp/tmp.json", "r", encoding="utf-8") as f: + mwtabfile2.read(f) + assert mwtabfile2['NMR_BINNED_DATA']['Extended'] == [{'Metabolite': 'some name', 'column1': 'some value'}, + {'Metabolite': 'some name 2', 'column1': 'some value 2'}] + + + with open("tests/example_data/tmp/tmp.txt", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="mwtab") + + mwtabfile3 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.txt") + with open("tests/example_data/tmp/tmp.txt", "r", encoding="utf-8") as f: + mwtabfile3.read(f) + assert mwtabfile3['NMR_BINNED_DATA']['Extended'] == [{'Metabolite': 'some name', 'column1': 'some value'}, + {'Metabolite': 'some name 2', 'column1': 'some value 2'}] + + + df = pandas.DataFrame([['some name', 'some value'], ['some name 3', 'some value 3']], columns = ['Metabolite', 'column2']) + mwtabfile.set_extended_from_pandas(df) + with open("tests/example_data/tmp/tmp.txt", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="mwtab") + + mwtabfile3 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.txt") + with open("tests/example_data/tmp/tmp.txt", "r", encoding="utf-8") as f: + mwtabfile3.read(f) + assert mwtabfile3['NMR_BINNED_DATA']['Extended'] == [{'Metabolite': 'some name', 'column2': 'some value'}, + {'Metabolite': 'some name 3', 'column2': 'some value 3'}] + + + mwtabfile['NMR_BINNED_DATA']['Extended'] = [] + mwtabfile._extended_metabolite_header = None + with open("tests/example_data/tmp/tmp.txt", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="mwtab") + + mwtabfile3 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.txt") + with open("tests/example_data/tmp/tmp.txt", "r", encoding="utf-8") as f: + mwtabfile3.read(f) + assert mwtabfile3['NMR_BINNED_DATA']['Extended'] == [] + + +def test_write_edge_cases(capsys): + """Just hitting a few lines that are somewhat edge cases.""" + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + if not os.path.exists(os.path.dirname("tests/example_data/tmp/tmp.json")): + dirname = os.path.dirname("tests/example_data/tmp/tmp.json") + if dirname: + os.makedirs(dirname) + + save_binned_header = copy.deepcopy(mwtabfile._binned_header) + mwtabfile._binned_header = None + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="json") + + mwtabfile2 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.json") + with open("tests/example_data/tmp/tmp.json", "r", encoding="utf-8") as f: + mwtabfile2.read(f) + assert mwtabfile2._binned_header == save_binned_header + + + with open("tests/example_data/tmp/tmp.txt", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="mwtab") + + mwtabfile3 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.txt") + with open("tests/example_data/tmp/tmp.txt", "r", encoding="utf-8") as f: + mwtabfile3.read(f) + assert mwtabfile3._binned_header == save_binned_header + + + mwtabfile['NMR_BINNED_DATA']['Data'][0]['Bin range(ppm)'] = 'asdf' + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="json") + captured = capsys.readouterr() + assert captured.out[:-1] == ("Warning: The \"Metabolite\" key and \"Bin range(ppm)\" " + "key in ['NMR_BINNED_DATA']['Data'][0] are different " + "values. Only the value in \"Bin range(ppm)\" will be written out.") + + + mwtabfile4 = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_extra_sample.txt") + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_extra_sample.txt", "r", encoding="utf-8") as f: + mwtabfile4.read(f) + assert mwtabfile4._factors is not None + + save_samples = copy.deepcopy(mwtabfile4._samples) + mwtabfile4._samples = None + mwtabfile4._factors = None + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: + mwtabfile4.write(f, file_format="json") + + mwtabfile5 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.json") + with open("tests/example_data/tmp/tmp.json", "r", encoding="utf-8") as f: + mwtabfile5.read(f) + assert mwtabfile5._samples == save_samples + assert mwtabfile5._factors is None + + + with open("tests/example_data/tmp/tmp.txt", "w", encoding="utf-8") as f: + mwtabfile4.write(f, file_format="mwtab") + + mwtabfile6 = mwtab.mwtab.MWTabFile("tests/example_data/tmp/tmp.txt") + with open("tests/example_data/tmp/tmp.txt", "r", encoding="utf-8") as f: + mwtabfile6.read(f) + assert mwtabfile6._samples == save_samples + assert mwtabfile6._factors is None + +def test_write_error(init_tmp_dir): + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_bad_table_type.json", force=True) + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_bad_table_type.json", "r", encoding="utf-8") as f: + mwtabfile.read(f) + mwtabfile['PROJECT'] = [] + with pytest.raises(TypeError, match = r'^Key/section "PROJECT" is not a dictionary\. It cannot be translated to the mwTab format\.'): + with open("tests/example_data/tmp/tmp.txt", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="mwtab") + + +def test_keys_reorder(): + """Test that the keys are reordered to match what the Workbench wants.""" + test_dict = {"UNKNOWN_KEY": {'asdf': 'qwer'}, + "MS_METABOLITE_DATA": {"Data":[{"attribute1":"qwer", "attribute2":"zxcv", "Metabolite":"asdf"}], "Units":"some unit"}, + "ANALYSIS":{"asdf":"asdf"}, + "SUBJECT_SAMPLE_FACTORS":[ + { + "Factors":{"Feeeding":"Ad lib","Running Capacity":"High"}, + "Subject ID":"-", + "Sample ID":"S00009477" + }, + { + "Subject ID":"-", + "Sample ID":"S00009478", + "Factors":{"Feeeding":"Ad lib","Running Capacity":"High"} + }]} + + expected_dict = {'ANALYSIS': {'asdf': 'asdf'}, + 'SUBJECT_SAMPLE_FACTORS': [ + {'Subject ID': '-', + 'Sample ID': 'S00009477', + 'Factors': + {'Feeeding': 'Ad lib', + 'Running Capacity': 'High'}}, + {'Subject ID': '-', + 'Sample ID': 'S00009478', + 'Factors': + {'Feeeding': 'Ad lib', + 'Running Capacity': 'High'}}], + 'MS_METABOLITE_DATA':{ + 'Units': 'some unit', + 'Data':[ + {'Metabolite': 'asdf', + 'attribute1': 'qwer', + 'attribute2': 'zxcv'}]}, + "UNKNOWN_KEY": {'asdf': 'qwer'}} + + mwtabfile = mwtab.mwtab.MWTabFile("some file path") + + for key, value in test_dict.items(): + mwtabfile[key] = value + + if not os.path.exists("tests/example_data/tmp/"): + os.makedirs("tests/example_data/tmp/") + + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="json") + + with open("tests/example_data/tmp/tmp.json", "r", encoding="utf-8") as f: + json_file = loads(f.read()) + + assert expected_dict == json_file + + +def test_read_in_and_reorder_keys(): + """Test that a file with incorrect key order gets reordered.""" + + if not os.path.exists("tests/example_data/tmp/"): + os.makedirs("tests/example_data/tmp/") + + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/incorrect_section_order.json") + + with open("tests/example_data/other_mwtab_files/incorrect_section_order.json", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + with open("tests/example_data/tmp/tmp.json", "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format="json") + + with open("tests/example_data/tmp/tmp.json", "r", encoding="utf-8") as f: + json_file = loads(f.read()) + + with open("tests/example_data/other_mwtab_files/corrected_section_order.json", "r", encoding="utf-8") as f: + expected_dict = loads(f.read()) + + assert expected_dict == json_file + + +@pytest.mark.parametrize("file_source", [ + "tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt", + "tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.json" +]) +def test_read_in_duplicate_keys(file_source): + """Test that a file with duplicate keys is handled correctly.""" + + if not os.path.exists("tests/example_data/tmp/"): + os.makedirs("tests/example_data/tmp/") + + mwtabfile = mwtab.mwtab.MWTabFile(file_source, duplicate_keys=True) + with open(file_source, "r", encoding="utf-8") as f: + mwtabfile.read(f) + + assert 'key_1{{{_1_}}}' in mwtabfile["SUBJECT_SAMPLE_FACTORS"][0]["Additional sample data"] + + if file_source.endswith('.txt'): + outpath = "tests/example_data/tmp/tmp.txt" + file_format = 'mwtab' + else: + outpath = "tests/example_data/tmp/tmp.json" + file_format = 'json' + with open(outpath, "w", encoding="utf-8") as f: + mwtabfile.write(f, file_format=file_format) + + new_mwtabfile = mwtab.mwtab.MWTabFile(outpath, duplicate_keys=True) + with open(outpath, "r", encoding="utf-8") as f: + new_mwtabfile.read(f) + + assert 'key_1{{{_1_}}}' in new_mwtabfile["SUBJECT_SAMPLE_FACTORS"][0]["Additional sample data"] + + + +def test_validate(): + """Test that the validate method validates the object.""" + + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt", duplicate_keys=True) + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + error_log, _ = mwtabfile.validate(verbose=False) + + assert "duplicate keys" in error_log + + +def test_from_dict(): + """Test that the from_dict method works to create a new MWTabFile object.""" + + with open("tests/example_data/other_mwtab_files/incorrect_section_order.json", "r", encoding="utf-8") as f: + json_file = loads(f.read()) + + mwtabfile = mwtab.mwtab.MWTabFile.from_dict(json_file) + + assert mwtabfile.study_id == "ST000000" + + + +def test_properties(): + """Test that the study_id, analysis_id, and header properties behave as expected.""" + + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt") + + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + assert mwtabfile.study_id == "ST000122" + assert mwtabfile.analysis_id == "AN000204" + assert mwtabfile.header == "#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109" + + temp = mwtabfile["METABOLOMICS WORKBENCH"] + del mwtabfile["METABOLOMICS WORKBENCH"] + + assert mwtabfile.study_id is None + assert mwtabfile.analysis_id is None + assert mwtabfile.header is None + + mwtabfile["METABOLOMICS WORKBENCH"] = temp + + assert mwtabfile.study_id == "ST000122" + assert mwtabfile.analysis_id == "AN000204" + assert mwtabfile.header == "#METABOLOMICS WORKBENCH STUDY_ID:ST000122 ANALYSIS_ID:AN000204 PROJECT_ID:PR000109" + + mwtabfile.study_id = "asdf" + mwtabfile.analysis_id = "qwer" + + + assert mwtabfile.study_id == "asdf" + assert mwtabfile.analysis_id == "qwer" + + with pytest.raises(ValueError, match=r'^Header cannot be set because it is not of the form'): + mwtabfile.header = "zxcv" + + mwtabfile.header = "#METABOLOMICS WORKBENCH STUDY_ID:ST000234 ANALYSIS_ID:AN000234 PROJECT_ID:PR000234" + + assert mwtabfile.header == "#METABOLOMICS WORKBENCH STUDY_ID:ST000234 ANALYSIS_ID:AN000234 PROJECT_ID:PR000234" + + with pytest.raises(TypeError, match=r'^The value for study_id must be a string.'): + mwtabfile.study_id = 1 + + mwtabfile['METABOLOMICS WORKBENCH'] = 'asdf' + with pytest.raises(TypeError, match=r'^The "METABOLOMICS WORKBENCH" key is not a dictionary, so study_id cannot be set.'): + mwtabfile.study_id = 'asdf' + + del mwtabfile['METABOLOMICS WORKBENCH'] + mwtabfile.study_id = 'opui' + assert mwtabfile.study_id == 'opui' + assert mwtabfile['METABOLOMICS WORKBENCH'] == {'STUDY_ID': 'opui'} + + del mwtabfile['METABOLOMICS WORKBENCH'] + mwtabfile.header = "#METABOLOMICS WORKBENCH STUDY_ID:ST000234 ANALYSIS_ID:AN000234 PROJECT_ID:PR000234" + assert mwtabfile.header == "#METABOLOMICS WORKBENCH STUDY_ID:ST000234 ANALYSIS_ID:AN000234 PROJECT_ID:PR000234" + assert mwtabfile['METABOLOMICS WORKBENCH'] == {'STUDY_ID': 'ST000234', 'ANALYSIS_ID': 'AN000234', 'PROJECT_ID': 'PR000234'} + + with pytest.raises(AttributeError): + del mwtabfile.study_id + + +def test_get_and_set_table_from_pandas(): + """Test that METABOLITES DATA, METABOLITES, and EXTENDED can be set from a pandas dataframe and gotten as one.""" + + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt") + + with open("tests/example_data/other_mwtab_files/ST000122_AN000204_duplicate_keys.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + df = pandas.DataFrame([['some name', 'some value'], ['some name 2', 'some value 2']], columns = ['Metabolite', 'column1']) + mwtabfile.set_table_from_pandas(df, 'Metabolites') + assert mwtabfile['MS_METABOLITE_DATA']['Metabolites'] == [{'Metabolite': 'some name', 'column1': 'some value'}, + {'Metabolite': 'some name 2', 'column1': 'some value 2'}] + assert mwtabfile.get_table_as_pandas('Metabolites').equals(df) + assert mwtabfile.get_metabolites_as_pandas().equals(df) + assert mwtabfile._metabolite_header == ['column1'] + + mwtabfile.set_table_from_pandas(df, 'Extended') + assert mwtabfile['MS_METABOLITE_DATA']['Extended'] == [{'Metabolite': 'some name', 'column1': 'some value'}, + {'Metabolite': 'some name 2', 'column1': 'some value 2'}] + assert mwtabfile.get_table_as_pandas('Extended').equals(df) + assert mwtabfile.get_extended_as_pandas().equals(df) + assert mwtabfile._extended_metabolite_header == ['column1'] + + mwtabfile.set_table_from_pandas(df, 'Data') + assert mwtabfile['MS_METABOLITE_DATA']['Data'] == [{'Metabolite': 'some name', 'column1': 'some value'}, + {'Metabolite': 'some name 2', 'column1': 'some value 2'}] + assert mwtabfile.get_table_as_pandas('Data').equals(df) + assert mwtabfile.get_metabolites_data_as_pandas().equals(df) + assert mwtabfile._samples == ['column1'] + + df2 = pandas.DataFrame([['some name3', 'some value3'], ['some name 4', 'some value 4']], columns = ['Metabolite', 'column2']) + mwtabfile.set_metabolites_from_pandas(df2, True) + assert mwtabfile['MS_METABOLITE_DATA']['Metabolites'] == [{'Metabolite': 'some name3', 'column2': 'some value3'}, + {'Metabolite': 'some name 4', 'column2': 'some value 4'}] + assert mwtabfile._metabolite_header == None + + mwtabfile.set_extended_from_pandas(df2, True) + assert mwtabfile['MS_METABOLITE_DATA']['Extended'] == [{'Metabolite': 'some name3', 'column2': 'some value3'}, + {'Metabolite': 'some name 4', 'column2': 'some value 4'}] + assert mwtabfile._extended_metabolite_header == None + + mwtabfile.set_metabolites_data_from_pandas(df2, True) + assert mwtabfile['MS_METABOLITE_DATA']['Data'] == [{'Metabolite': 'some name3', 'column2': 'some value3'}, + {'Metabolite': 'some name 4', 'column2': 'some value 4'}] + assert mwtabfile._samples == None + + del mwtabfile['MS_METABOLITE_DATA'] + mwtabfile['NMR_BINNED_DATA'] = {'Data':[{'Metabolite': 'asdf', 'sample1': '1234.45'}]} + mwtabfile.set_metabolites_data_from_pandas(df2) + assert mwtabfile['NMR_BINNED_DATA']['Data'] == [{'Metabolite': 'some name3', 'column2': 'some value3'}, + {'Metabolite': 'some name 4', 'column2': 'some value 4'}] + assert mwtabfile._samples == ['column2'] + assert mwtabfile._binned_header == ['column2'] + + mwtabfile.set_metabolites_data_from_pandas(df, True) + assert mwtabfile['NMR_BINNED_DATA']['Data'] == [{'Metabolite': 'some name', 'column1': 'some value'}, + {'Metabolite': 'some name 2', 'column1': 'some value 2'}] + assert mwtabfile._samples == None + assert mwtabfile._binned_header == None + + +def test_print_file(): + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + mwtab_io = io.StringIO() + mwtabfile.print_file(mwtab_io, 'json') + mwtab_str = mwtab_io.getvalue() + assert mwtabfile.writestr('json') == mwtab_str[:-1] + + +def test_print_block(): + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + mwtab_io = io.StringIO() + mwtabfile.print_block('PROJECT', mwtab_io, 'json') + mwtab_str = mwtab_io.getvalue() + check_str = json.dumps(mwtabfile['PROJECT'], sort_keys=mwtab.mwtab.SORT_KEYS, indent=mwtab.mwtab.INDENT) + assert check_str == mwtab_str[:-1] + + +def test_copy(): + """Copy and deepcopy dunders were overweritten, so check them.""" + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + mwtabfile2 = copy.copy(mwtabfile) + assert mwtabfile == mwtabfile2 + assert mwtabfile._samples == mwtabfile2._samples + assert mwtabfile._duplicate_keys == mwtabfile2._duplicate_keys + + mwtabfile3 = copy.deepcopy(mwtabfile) + assert mwtabfile == mwtabfile3 + assert mwtabfile._samples == mwtabfile3._samples + assert mwtabfile._duplicate_keys == mwtabfile3._duplicate_keys + + +def test_misc_coverage(): + """Testing some lines that are basic error checking that don't really belong in another test.""" + mwtabfile = mwtab.mwtab.MWTabFile("tests/example_data/other_mwtab_files/ST000022_AN000041.txt") + with open("tests/example_data/other_mwtab_files/ST000022_AN000041.txt", "r", encoding="utf-8") as f: + mwtabfile.read(f) + + with pytest.raises(TypeError, match = r"^Expecting or , but was passed"): + mwtabfile._is_mwtab({}) + + with pytest.raises(TypeError, match = r"^Expecting or , but was passed"): + mwtabfile._is_json({}) + + + diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py new file mode 100644 index 0000000..590d89b --- /dev/null +++ b/tests/test_tokenizer.py @@ -0,0 +1,64 @@ +# -*- coding: utf-8 -*- + + +import pytest + +from mwtab import tokenizer + + + +def test_errors(): + with open("tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_factor.txt", 'r') as f: + text = f.read() + + with pytest.raises(ValueError, match = r".*Either a bar \('\| '\) separating 2 items.*") as exc_info: + for token in tokenizer.tokenizer(text): + pass + + err = exc_info.value + assert hasattr(err, '__cause__') + assert isinstance(err.__cause__, ValueError) + assert err.__cause__.args[0] == ("Either a bar ('| ') separating 2 items is missing or " + "there is an extra colon (':') in the factor " + "key value pair, 'Disease Status::Cases'") + + + with open("tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_additional_data.txt", 'r') as f: + text = f.read() + + with pytest.raises(ValueError, match = r".*Either a semicolon \('; '\) separating 2 items.*") as exc_info: + for token in tokenizer.tokenizer(text): + pass + + err = exc_info.value + assert hasattr(err, '__cause__') + assert isinstance(err.__cause__, ValueError) + assert err.__cause__.args[0] == ("Either a semicolon ('; ') separating 2 items " + "is missing or there is an extra equal sign " + "('=') in the additional data key value pair, 'key1==value1'") + + + with open("tests/example_data/other_mwtab_files/ST000022_AN000041_missing_tab.txt", 'r') as f: + text = f.read() + + with pytest.raises(ValueError, match = r".*Expected a tab in the line.*") as exc_info: + for token in tokenizer.tokenizer(text): + pass + + err = exc_info.value + assert hasattr(err, '__cause__') + assert isinstance(err.__cause__, ValueError) + assert err.__cause__.args[0] == "Expected a tab in the line." + + + with open("tests/example_data/other_mwtab_files/ST000022_AN000041_malformed_SSF_line.txt", 'r') as f: + text = f.read() + + with pytest.raises(IndexError, match = r".*LINE WITH ERROR:\n\t'SUBJECT_SAMPLE_FACTORS \\t-C0559Disease Status:Cases\\t'") as exc_info: + for token in tokenizer.tokenizer(text): + pass + + + + + diff --git a/tests/test_validator.py b/tests/test_validator.py index b6b2590..61a66d6 100755 --- a/tests/test_validator.py +++ b/tests/test_validator.py @@ -1,62 +1,347 @@ import pytest import mwtab +import copy -@pytest.mark.parametrize("files_source", [ - "tests/example_data/mwtab_files/ST000122_AN000204.json", - "tests/example_data/mwtab_files/ST000122_AN000204.txt" +@pytest.mark.parametrize("file_source", [ + "tests/example_data/validation_files/ST000122_AN000204_validate_passing.txt", + "tests/example_data/validation_files/ST000122_AN000204_validate_passing.json" ]) -def test_validate(files_source): - """Test method for validating passing mwTab and JSON files from Metabolomics Workbench. - :param files_source: File path to Metabolomics Workbench file to be validated. - :type files_source: :py:class:`str` or - """ - mwfile = next(mwtab.read_files(files_source)) - _, validation_log = mwtab.validate_file(mwfile, metabolites=False) +def test_validate_passing(file_source): + mwfile = next(mwtab.read_files(file_source)) + validation_log, _ = mwtab.validate_file(mwfile) assert len(validation_log.split('\n')) == 9 + assert 'Status: Passing' in validation_log + +@pytest.mark.parametrize("file_source", [ + "tests/example_data/validation_files/ST000122_AN000204_validate_polarity.txt", + "tests/example_data/validation_files/ST000122_AN000204_validate_polarity.json" +]) +def test_validate_polarity(file_source): + mwfile = next(mwtab.read_files(file_source)) + validation_log, _ = mwtab.validate_file(mwfile) + assert 'Error: The "polarity" column in the ' in validation_log + assert ('indicates multiple polarities in a single analysis, and ' + 'this should not be. A single mwTab file is supposed to be ' + 'restricted to a single analysis. This means multiple MS ' + 'runs under different settings should each be in their own file.') in validation_log + @pytest.mark.parametrize("file_source", [ - "tests/example_data/validation_files/ST000122_AN000204_error_1.txt", - "tests/example_data/validation_files/ST000122_AN000204_error_1.json" + "tests/example_data/validation_files/ST000122_AN000204_validate_table_values.txt", + "tests/example_data/validation_files/ST000122_AN000204_validate_table_values.json" ]) -def test_validate_subject_sample_factors(file_source): +def test_validate_table_values(file_source): mwfile = next(mwtab.read_files(file_source)) - _, validation_log = mwtab.validate_file(mwfile, metabolites=False) - assert "missing Subject ID" in validation_log - assert "missing Sample ID" in validation_log - assert "missing value for Factor" in validation_log + validation_log, _ = mwtab.validate_file(mwfile) + assert 'Error: Column(s) with no name were found in the' in validation_log + + assert 'Warning: The "null_column" column at position' in validation_log + assert 'table has all null values.' in validation_log + + assert 'Warning: The "90_10" column at position' in validation_log + assert ('table may have incorrect values. 90% or more of the values are the ' + 'same, but 10% or less are different.') in validation_log + + assert 'Warning: There are duplicate rows in the' in validation_log + + assert 'Warning: There are duplicate column names in the' in validation_log + +def test_validate_table_values2(): + """Can only be tested for JSON files.""" + mwfile = next(mwtab.read_files("tests/example_data/validation_files/ST000122_AN000204_validate_table_values2.json")) + validation_log, _ = mwtab.validate_file(mwfile) + assert ' table does not have the same columns for every row.' in validation_log + @pytest.mark.parametrize("file_source", [ - "tests/example_data/validation_files/ST000122_AN000204_error_2.txt", - "tests/example_data/validation_files/ST000122_AN000204_error_2.json" + "tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_names.txt", + "tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_names.json" ]) -def test_validate_subject_sample_factors(file_source): +def test_validate_metabolite_names(file_source): mwfile = next(mwtab.read_files(file_source)) - _, validation_log = mwtab.validate_file(mwfile, metabolites=False) - # assert "Section missing data entry for sample(s):" in validation_log - assert "SUBJECT_SAMPLE_FACTORS: Section missing sample ID(s)" in validation_log + validation_log, _ = mwtab.validate_file(mwfile) + # This one should bein the Data table. + assert 'Warning: There is a metabolite name, "samples"' in validation_log + # This one should be in the Metabolites table. + assert 'Warning: There is a metabolite name, "factors"' in validation_log + # This one should be in the Extended table. + assert 'Warning: There is a metabolite name, "metabolite_name"' in validation_log + + # All 3 will have this message, but we only need to confirm this part once. + assert (' that is probably wrong. It is close to a header name and ' + 'is likely due to a badly constructed Tab file.') in validation_log + @pytest.mark.parametrize("file_source", [ - "tests/example_data/validation_files/ST000122_AN000204_error_3.txt", - "tests/example_data/validation_files/ST000122_AN000204_error_3.json" + "tests/example_data/validation_files/ST000122_AN000204_validate_extended.txt", + "tests/example_data/validation_files/ST000122_AN000204_validate_extended.json" ]) -def test_validate_metabolites(file_source): +def test_validate_extended(file_source): + mwfile = next(mwtab.read_files(file_source)) + validation_log, _ = mwtab.validate_file(mwfile) + assert ' table has Sample IDs that were not found' in validation_log + assert 'Those IDs are:\n\t"16_A0_Lung_naive_0days_170427_UKy_GCH_rep1-lipid"' in validation_log + + +@pytest.mark.parametrize("file_source", [ + "tests/example_data/validation_files/ST000122_AN000204_validate_extended2.txt", + "tests/example_data/validation_files/ST000122_AN000204_validate_extended2.json" +]) +def test_validate_extended2(file_source): + """Cannot be tested at the same time as the other test for extended.""" mwfile = next(mwtab.read_files(file_source)) - _, validation_log = mwtab.validate_file(mwfile) - assert "which matches a commonly used field name" in validation_log + validation_log, _ = mwtab.validate_file(mwfile) + assert ' table does not have a column for "sample_id".' in validation_log + + @pytest.mark.parametrize("file_source", [ - "tests/example_data/validation_files/ST000122_AN000204_error_4.txt", - "tests/example_data/validation_files/ST000122_AN000204_error_4.json" + "tests/example_data/validation_files/ST000122_AN000204_validate_ssf.txt", + "tests/example_data/validation_files/ST000122_AN000204_validate_ssf.json" ]) -def test_validate_schema(file_source): +def test_validate_subject_samples_factors(file_source): mwfile = next(mwtab.read_files(file_source)) - _, validation_log = mwtab.validate_file(mwfile) - assert "does not match the allowed schema" in validation_log + validation_log, _ = mwtab.validate_file(mwfile) + assert ' has a duplicate Sample ID.' in validation_log + + assert 'has the following duplicate keys in its Factors:\n\t"Tissue/Fluid"' in validation_log + + assert 'has the following duplicate keys in its Additional sample data:\n\t"key1"' in validation_log + + +def test_validate_factors(): + """This can only be detected for mwTab, not JSON.""" + mwfile = next(mwtab.read_files("tests/example_data/validation_files/ST000122_AN000204_validate_factors.txt")) + validation_log, _ = mwtab.validate_file(mwfile) + assert ("Error: The factors in the METABOLITE_DATA section " + "and SUBJECT_SAMPLE_FACTORS section do not match.") in validation_log + + + + + +def test_validate_header_lengths(): + """This can only be detected for mwTab, not JSON.""" + mwfile = next(mwtab.read_files("tests/example_data/validation_files/ST000122_AN000204_validate_header_lengths.txt")) + validation_log, _ = mwtab.validate_file(mwfile) + assert ('Error: The section, METABOLITES, has a mismatch between the ' + 'number of headers and the number of elements in each line. Either ' + 'a line(s) has more values than headers or there are too few headers.') in validation_log + assert ('Error: The section, MS_METABOLITE_DATA, has a mismatch between ' + 'the number of headers and the number of elements in each line. ' + 'Either a line(s) has more values than headers or there are too few headers.') in validation_log + + +def test_validate_sub_section_uniqueness(): + """This can only be detected for mwTab, not JSON.""" + mwfile = next(mwtab.read_files("tests/example_data/validation_files/ST000122_AN000204_validate_subsection_uniqueness.txt")) + validation_log, _ = mwtab.validate_file(mwfile) + assert "Error: The section, STUDY, has a sub-section, FIRST_NAME, that is duplicated." in validation_log + + +@pytest.mark.parametrize("file_source", [ + "tests/example_data/validation_files/ST000122_AN000204_validate_metabolites.txt", + "tests/example_data/validation_files/ST000122_AN000204_validate_metabolites.json" +]) +def test_validate_metabolites(file_source): + mwfile = next(mwtab.read_files(file_source)) + validation_log, _ = mwtab.validate_file(mwfile) + assert 'Warning: The following metabolites in the' in validation_log + assert 'were not found in the' in validation_log + assert 'table:\n\t"Corticosterone, DOC"' in validation_log + + assert 'Error: A metabolite without a name was found in the' in validation_log + + assert 'Warning: The following metabolites in the' in validation_log + assert 'table appear more than once in the table:\n\t"Testosterone"' in validation_log + + assert 'Warning: The "ri" column at position ' in validation_log + assert ' table, matches a standard column name, "retention_index"' in validation_log + + assert 'Warning: The "pubchem_id" column at position ' in validation_log + assert 'and some of the values in the column do not match the expected type or format for that column.' in validation_log + + assert 'Warning: The column "ri" was found in the' in validation_log + assert 'but this column implies that another column, "retention_index_type",' in validation_log + + assert 'Warning: The column pair, "other_id" and "other_id_type",' in validation_log + assert ('in the METABOLITES table should have data in the ' + 'same rows, but at least one row has data in one ' + 'column and nothing in the other.') in validation_log + + assert 'Warning: The standard column, "other_id", was' in validation_log + assert ('If this column contains database IDs for standard databases such ' + 'as KEGG, PubChem, HMDB, etc., it is recommended to make individual ' + 'columns for these and not lump them together into a less descriptive ' + '"other_id" column.') in validation_log + + assert 'Warning: The column, "pubchem_id/hmdb_id", in the' in validation_log + assert ('was matched to multiple standard names, [\'pubchem_id\', \'hmdb_id\']. This is a good indication ' + 'that the values in that column should be split into the appropriate ' + 'individual columns.') in validation_log + + + +@pytest.mark.parametrize("file_source", [ + "tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_data.txt", + "tests/example_data/validation_files/ST000122_AN000204_validate_metabolite_data.json" +]) +def test_validate_data(file_source): + mwfile = next(mwtab.read_files(file_source)) + validation_log, _ = mwtab.validate_file(mwfile) + assert 'Error: SUBJECT_SAMPLE_FACTORS section missing sample ID(s).' in validation_log + assert 'The following IDs were found in the' in validation_log + assert 'section but not in the SUBJECT_SAMPLE_FACTORS:\n\t"CER040_242995_ML_02"' in validation_log + + assert 'Warning: There are duplicate samples in the' in validation_log + + assert 'Warning: The following metabolites in the' in validation_log + assert 'were not found in the' in validation_log + assert 'table:\n\t"Corticosterone_ DOC"' in validation_log + + assert 'Error: A metabolite without a name was found in the' in validation_log + + assert 'Warning: The following metabolites in the' in validation_log + assert 'table appear more than once in the table:\n\t"Testosterone"' in validation_log + + + +@pytest.mark.parametrize("file_source", [ + "mwtab", + "json" +]) +class TestMWTabSchemaErrors: + validation_logs = {} + mwfile = next(mwtab.read_files("tests/example_data/validation_files/ST000122_AN000204_schema_errors.txt")) + validation_log, _ = mwtab.validate_file(mwfile) + validation_logs['mwtab'] = validation_log + mwfile = next(mwtab.read_files("tests/example_data/validation_files/ST000122_AN000204_schema_errors.json")) + validation_log, _ = mwtab.validate_file(mwfile) + validation_logs['json'] = validation_log + + + def test_ionization(self, file_source): + """IONIZATION should not have a value of positive or negative, there is a special check for this.""" + assert '"ION_MODE" is where that should be indicated.' in self.validation_logs[file_source] + + def test_custom_message(self, file_source): + """Some schemas can have custom messages, such as COLLISON_GAS.""" + assert 'should be one of "Nitrogen" or "Argon".' in self.validation_logs[file_source] + + def test_message(self, file_source): + """Some schemas can have the entire message inserted. This is different from custom message.""" + assert ('Error: There must be either a "MS_METABOLITE_DATA" ' + 'section or a "MS_RESULTS_FILE" subsection in the ' + '"MS" section. Neither were found.') in self.validation_logs[file_source] + + def test_additionalProperties_errors(self, file_source): + """There are a few different places an additionalProperties can occur.""" + assert 'Unknown or invalid section, "BADSECTION".' in self.validation_logs[file_source] + assert 'Unknown or invalid subsections, "BADSUBSECTION", "BADSUBSECTION2", ' in self.validation_logs[file_source] + assert 'Unknown or invalid subsection, "BADSUBSECTION", ' in self.validation_logs[file_source] + + def test_required_error(self, file_source): + """Simply test that an error is printed wwhen COLLECTION is not present.""" + assert 'The required property, "COLLECTION", ' in self.validation_logs[file_source] + assert ' is missing.' in self.validation_logs[file_source] + + def test_email_format_error(self, file_source): + """Test that badly formatted emails are caught.""" + assert ' is not a valid email.' in self.validation_logs[file_source] + + def test_NA_value_error(self, file_source): + """Test that messages are printed about NA values where they should not be.""" + assert 'An empty value or a null value was detected' in self.validation_logs[file_source] + # required properties have a slightly different message than non-required ones. + assert 'A legitimate value should be provided for this required' in self.validation_logs[file_source] + assert 'Either a legitimate value should be provided for this' in self.validation_logs[file_source] + + + +class TestMWTabSchemaErrorsRare: + """None of what is tested here is expected to show up during normal operation. + There are more custom validations in create_better_error_messages than what are + acutally in the schema used to validate an mwTab file. This test is to cover + those lines. + """ + ms_schema = copy.deepcopy(mwtab.mwschema.ms_required_schema) + ms_schema['properties']['FOR_TEST'] = {'type':'object', 'properties':{}} + ms_schema['properties']['FOR_TEST']['properties']['MIN_PROPERTIES'] = {'type':'object', 'minProperties':1} + del ms_schema['properties']['CHROMATOGRAPHY']['properties']['COLUMN_NAME']['not'] + ms_schema['properties']['CHROMATOGRAPHY']['properties']['COLUMN_NAME']['minLength'] = 1 + ms_schema['properties']['CHROMATOGRAPHY']['properties']['FLOW_GRADIENT']['minLength'] = 1000 + ms_schema['properties']['CHROMATOGRAPHY']['properties']['FLOW_RATE']['maxLength'] = 3 + ms_schema['properties']['FOR_TEST']['properties']['MIN_ITEMS'] = {'type':'array', 'minItems':1} + ms_schema['properties']['FOR_TEST']['properties']['MIN_ITEMS2'] = {'type':'array', 'minItems':2} + ms_schema['properties']['CHROMATOGRAPHY']['properties']['CHROMATOGRAPHY_SUMMARY']['type'] = ['string', 'null'] + ms_schema['properties']['FOR_TEST']['properties']['ENUM'] = {'type':'string', 'enum':['asdf', 'qwer']} + ms_schema['properties']['FOR_TEST']['properties']['PATTERN'] = {'type':'string', 'pattern':r'^asdf$'} + ms_schema['properties']['FOR_TEST']['properties']['MIN'] = {'type':'integer', 'minimum':15} + ms_schema['properties']['FOR_TEST']['properties']['MAX'] = {'type':'integer', 'maximum':5} + ms_schema['properties']['FOR_TEST']['properties']['UNIQUE_ITEMS'] = {'type':'array', 'uniqueItems':True} + ms_schema['properties']['FOR_TEST']['dependentRequired'] = {'MIN':['asdf']} + ms_schema['properties']['FOR_TEST']['properties']['NOT_ONEOF'] = {'type':'string', 'not':{'oneOf':[{'enum':['qwer']}, {'pattern':r'asdf'}]}} + + mwfile = next(mwtab.read_files("tests/example_data/validation_files/ST000122_AN000204_schema_errors.txt")) + mwfile['FOR_TEST'] = {} + mwfile['FOR_TEST']['MIN_PROPERTIES'] = {} + mwfile['CHROMATOGRAPHY']['CHROMATOGRAPHY_SUMMARY'] = 1 + mwfile['CHROMATOGRAPHY']['CHROMATOGRAPHY_TYPE'] = 1 + mwfile['CHROMATOGRAPHY']['COLUMN_NAME'] = '' + mwfile['FOR_TEST']['MIN_ITEMS'] = [] + mwfile['FOR_TEST']['MIN_ITEMS2'] = ['a'] + mwfile['FOR_TEST']['ENUM'] = 'zxcv' + mwfile['FOR_TEST']['PATTERN'] = 'zxcv' + mwfile['FOR_TEST']['MIN'] = 10 + mwfile['FOR_TEST']['MAX'] = 10 + mwfile['FOR_TEST']['UNIQUE_ITEMS'] = ['a', 'a'] + mwfile['FOR_TEST']['NOT_ONEOF'] = 'asdf' + validation_log, _ = mwtab.validate_file(mwfile, ms_schema=ms_schema) + + + def test_minProperties_error(self): + assert ' "MIN_PROPERTIES", in the "FOR_TEST" section cannot be empty' in self.validation_log + + def test_minLength_error(self): + assert 'cannot be an empty string.' in self.validation_log + assert 'is too short' in self.validation_log + + def test_maxLength_error(self): + assert 'is too long' in self.validation_log + + def test_minItems_error(self): + assert ' "MIN_ITEMS", in the "FOR_TEST" section cannot be empty' in self.validation_log + assert 'must have at least 2 items' in self.validation_log + + def test_type_error(self): + assert 'is not any of the allowed types:' in self.validation_log + assert 'is not of type' in self.validation_log + + def test_enum_error(self): + assert '"ENUM", in the "FOR_TEST" section is not one of [\'asdf\', \'qwer\'].' in self.validation_log + + def test_pattern_error(self): + assert 'does not match the regular expression pattern' in self.validation_log + + def test_minimum_error(self): + assert 'must be greater than or equal to' in self.validation_log + + def test_maximum_error(self): + assert 'must be less than or equal to' in self.validation_log + + def test_uniqueItems_error(self): + assert 'has non-unique elements.' in self.validation_log + + def test_not_oneOf_error(self): + assert '"NOT_ONEOF"' in self.validation_log + assert ("does not match the regular expression pattern " + "{'oneOf': [{'enum': ['qwer']}, {'pattern': 'asdf'}]}") in self.validation_log + @pytest.mark.parametrize("file_source", [ @@ -64,7 +349,7 @@ def test_validate_schema(file_source): ]) def test_validation_log_local(file_source): mwfile = next(mwtab.read_files(file_source)) - _, validation_log = mwtab.validate_file(mwfile) + validation_log, _ = mwtab.validate_file(mwfile) # assert "mwtab version: {}".format(mwtab.__version__) in validation_log assert "Source: {}".format(file_source) in validation_log assert "Study ID: {}".format("ST000122") in validation_log @@ -77,10 +362,55 @@ def test_validation_log_local(file_source): ]) def test_validation_log_web(file_source): mwfile = next(mwtab.read_files(file_source)) - _, validation_log = mwtab.validate_file(mwfile, metabolites=False) + validation_log, _ = mwtab.validate_file(mwfile) # assert "mwtab version: {}".format(mwtab.__version__) in validation_log assert "Source: {}".format("https://www.metabolomicsworkbench.org/rest/study/analysis_id/AN000002/mwtab/txt")\ - in validation_log + in validation_log assert "Study ID: {}".format("ST000002") in validation_log assert "Analysis ID: {}".format("AN000002") in validation_log - assert "File format: {}".format("txt") in validation_log \ No newline at end of file + assert "File format: {}".format("txt") in validation_log + + +def test_base_schema_error_extra_key(): + """Test that if extra keys are included there is an error.""" + mwfile = next(mwtab.read_files("tests/example_data/mwtab_files/ST000122_AN000204.json")) + mwfile["NM"] = {} + validation_log, _ = mwtab.validate_file(mwfile) + + assert 'Error: Unknown or invalid sections, "CHROMATOGRAPHY", "MS", "MS_METABOLITE_DATA".' in validation_log + assert ('Error: There must be either a "NMR_METABOLITE_DATA" section, a ' + '"NMR_BINNED_DATA" section or a "NMR_RESULTS_FILE" subsection in the "NM" section. Neither were found.') in validation_log + + +@pytest.mark.parametrize("file_source", [ + "tests/example_data/validation_files/ST000122_AN000204_validate_file.txt", + "tests/example_data/validation_files/ST000122_AN000204_validate_file.json" +]) +def test_validate_file(file_source): + mwfile = next(mwtab.read_files(file_source)) + validation_log, _ = mwtab.validate_file(mwfile) + assert ('Error: No "MS" or "NM" section was found, ' + 'so analysis type could not be determined. ' + 'Mass spec will be assumed.') in validation_log + + if file_source.endswith('.txt'): + assert 'Warning: Missing METABOLITES section.' in validation_log + else: + assert 'Warning: Missing ["MS_METABOLITE_DATA"]["Metabolites"] section.' in validation_log + + +def test_validation_complete_coverage(): + """This is just to hit some lines that aren't covered, but also aren't terribly important to test.""" + mwfile = next(mwtab.read_files("tests/example_data/validation_files/complete_coverage.json")) + validation_log, _ = mwtab.validate_file(mwfile, verbose=True) + assert validation_log is None + +def test_validation_complete_coverage2(): + """This is just to hit some lines that aren't covered, but also aren't terribly important to test.""" + mwfile = next(mwtab.read_files("tests/example_data/validation_files/complete_coverage2.txt")) + validation_log, _ = mwtab.validate_file(mwfile) + +def test_validation_complete_coverage3(): + """This is just to hit some lines that aren't covered, but also aren't terribly important to test.""" + mwfile = next(mwtab.read_files("tests/example_data/validation_files/complete_coverage3.json")) + validation_log, _ = mwtab.validate_file(mwfile)