MAPA tutorial

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

Yifei Ge and Xiaotao 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:

  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), linking the results to the latest findings in literature from PubMed.

The outcome is a fast, reproducible, and user-friendly pipeline 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 workflows and the Shiny app for point-and-click exploration. By the end, with your gene/metabolite list as input, 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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