MAPA — Tutorial

Overview

MAPA is a streamlined workflow for pathway-enrichment analysis and enrichment result interpretation that turns large omics datasets into clear biological insight. It is developed by the Shen Lab at Nanyang Technological University, Singapore.

It is able to perform:

1. Pathway analysis: Detects enriched pathways from your data via over-representation analysis (ORA) or gene set enrichment analysis (GSEA).
2. Functional module identification: Clusters overlapping or functional-related pathways into functional modules, so every informative pathway—not just the "top 5 or 10"—contributes to the story.
3. Functional module annotation: Summarises each module with large-language models (LLM) (e.g., ChatGPT), which links the results to the latest findings in literature from PubMed.

This tool is a streamlined, reproducible, and user-friendly workflow that reduces redundancy and delivers biologically meaningful interpretations for enrichment results.

For a step-to-step guide, please visit this link.

Contact

If you have any questions about mapa, please don’t hesitate to email me ([email protected]{.email}) or reach out me via the social medias below.

shenxt1990

[email protected]{.email}

Twitter

M339, Alway Buidling, Cooper Lane, Palo Alto, CA 94304

Citation

If you use mapa in your publications, please cite this paper:

Ge, Y. et al. (2025) ‘Leveraging Large Language Models for Redundancy-Aware Pathway Analysis and Deep Biological Interpretation’. bioRxiv, p. 2025.08.23.671949. Available at: https://doi.org/10.1101/2025.08.23.671949.

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