mapa tutorial

Functional Module Annotation for Pathway Analysis Results Using LLM

Overview

mapa: Functional Module 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:

1. Detects enriched pathways from your data via over-representation analysis (ORA) or gene set enrichment analysis (GSEA).
2. 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. 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.