MAPA tutorial

Functional Module Identification and Annotation for Pathway Analysis Results Using LLM
Author

Yifei Ge, Xiaotao Shen, Yuchen Shen

Published

June 8, 2025

Preface

MAPA: Functional Module Identification and Annotation for Pathway Analysis Results Using LLM

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.

Aim of This Tutorial

This guide walks you through the two faces of mapa: the R package for command-line (code-based) workflow and the Shiny App/Web version for point-and-click exploration. In the end, you will be able to:

  1. Merge overlapping enriched pathways into informative functional modules.
  2. Generate biological interpretations of each module with large-language models.
  3. Create publication-ready visuals of enrichment results, module networks, and pathway–molecule relationships.
  4. Export a comprehensive report that gathers every table, figure, and LLM-based summary in one place.

Whether you favour the command line or a browser, the tutorial gives you everything you need to apply MAPA confidently in your bioinformatics projects.

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