tooluniverse-gene-enrichment
Perform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ ToolUniverse tools. Supports GO enrichment (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB Hallmark, and 220+ Enrichr libraries. Handles multiple ID types (gene symbols, Ensembl, Entrez, UniProt), multiple organisms (human, mouse, rat, fly, worm, yeast), customizable backgrounds, and multiple testing correction (BH, Bonferroni). Use when users ask about gene enrichment, pathway analysis, GO term enrichment, KEGG pathway analysis, GSEA, over-representation analysis, func
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-gene-enrichment --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Gene Enrichment and Pathway Analysis Perform comprehensive gene enrichment analysis including Gene Ontology (GO), KEGG, Reactome, WikiPathways, and MSigDB enrichment using both Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). Integrates local computation via gseapy with ToolUniverse pathway databases for cross-validated, publication-ready results. **IMPORTANT**: Always use English terms in tool calls (gene names, pathway names, organism names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language. --- ## When to Use This Skill Apply when users: - Ask about gene enrichment analysis (GO, KEGG, Reactome, etc.) - Have a gene list from differential expression, clustering, or any experiment - Want to know which biological processes, molecular functions, or cellular components are enriched - Need KEGG or Reactome pathway enrichment analysis - Ask about GSEA (Gene Set Enrichment Analysis) with ranked gene lists - Want over-representation analysis (ORA) with Fisher's exact test - Need multiple testing correction (Benjamini-Hochberg, Bonferroni) - Ask about enrich
- When to Use This Skill
- Input Parameters
- Core Principles
- Decision Tree: ORA vs GSEA
- Decision Tree: gseapy vs ToolUniverse Tools
- Quick Start Workflow
- Step 1: Create Report File (IMMEDIATE)
- Step 2: ID Conversion and Validation
- Step 3: Primary Enrichment with gseapy
- Step 4: Cross-Validation with ToolUniverse
- Step 5: Report Compilation
- Evidence Grading
- Supported Organisms
- Common Patterns
What does the tooluniverse-gene-enrichment skill do?
Perform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ ToolUniverse tools. Supports GO enrichment (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB Hallmark, and 220+ Enrichr libraries. Handles multiple ID types (gene symbols, Ensembl, Entrez, UniProt), multiple organisms (human, mouse, rat, fly, worm, yeast), customizable backgrounds, and multiple testing correction (BH, Bonferroni). Use when users ask about gene enrichment, pathway analysis, GO term enrichment, KEGG pathway analysis, GSEA, over-representation analysis, func
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-gene-enrichment --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.