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pathway-enricher

Runs gene-set enrichment analysis against Enrichr databases and generates ranked tables, charts, and reports.

1.1k|257|Updated Feb 25, 2026
Search Tags:#bioinformatics#pathway enrichment#gwas#enrichr#kegg#gene set analysis#go ontology
One-click install
npx skills add https://github.com/ClawBio/ClawBio --skill pathway-enricher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pathway-enricher
Source: https://github.com/ClawBio/ClawBio/tree/main/skills/pathway-enricher
Command: npx skills add https://github.com/ClawBio/ClawBio --skill pathway-enricher

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, matplotlib, numpy, pandas.

What problem does it solve?

Researchers with a gene list from GWAS, differential expression, or other omics studies need to know which biological pathways and processes are statistically overrepresented, but manually querying KEGG, GO, Reactome, and WikiPathways one by one is slow and hard to reproduce.

Core Features & Use Cases

  • Multi-database enrichment: Queries six Enrichr libraries (KEGG 2021, GO BP/MF/CC 2023, Reactome 2022, WikiPathways 2023) in a single run and ranks terms by combined score and adjusted p-value.
  • Publication-ready outputs: Produces bubble charts, per-database bar charts, a gene-by-pathway heatmap, CSV tables, a Markdown report, and a machine-readable result.json.
  • Reproducibility pack: Writes commands.sh, environment.yml, and checksums so any run can be repeated exactly.
  • Use Case: Given a list of 25 Alzheimer's GWAS genes (APOE, TREM2, BIN1, ...), run the demo mode to instantly see that Alzheimer disease and microglial phagocytosis pathways are top hits, with figures ready for a presentation.

Quick Start

Ask the agent to run pathway enrichment on your gene list, for example: "Run pathway enrichment on these genes: APOE, TREM2, BIN1, CLU, APP" or invoke the demo with the built-in Alzheimer's gene set.

Frequently Asked Questions about pathway-enricher

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run pathway enrichment analysis on a gene list?▼

Provide a text or CSV file with one HGNC gene symbol per line and run the pathway_enricher.py script with --input and --output flags. It submits the list to Enrichr and returns ranked pathways from KEGG, GO, Reactome, and WikiPathways with charts and a report.

What databases does Enrichr pathway enrichment query?▼

This skill queries six Enrichr libraries: KEGG 2021 Human, GO Biological Process 2023, GO Molecular Function 2023, GO Cellular Component 2023, Reactome 2022, and WikiPathways 2023 Human. You can restrict the run to a subset using the --databases flag.

Can I do gene set enrichment without sending data to an external API?▼

This skill requires internet access because it calls the Enrichr REST API, but only HGNC gene symbols are transmitted, never patient identifiers or genotype data. For fully offline analysis you would need a local tool with downloaded pathway databases instead.

Why does pathway enrichment fail or return no significant results?▼

Failures usually come from network issues reaching the Enrichr API, which the script reports as errors rather than crashing. Empty significant results occur when no term passes the adjusted p-value < 0.05 threshold; the report then falls back to showing top-ranked terms with a warning.

What are the limitations of Enrichr for large gene lists?▼

Enrichr limits input size, so submitting thousands of genes can fail or dilute signal. Filter differential expression results to the top 500-1000 significant genes before running, and remember enrichment shows statistical overrepresentation, not diagnostic proof.

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