tooluniverse-gene-enrichment
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
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.
Domain Reasoning: Background Selection
Enrichment results are only as good as your background. The default background (all annotated genes in the genome) inflates enrichment for tissue-specific or context-specific gene lists. Always consider: what is the appropriate background for this experiment? For brain RNA-seq, use brain-expressed genes as background; for a proteomics experiment, use detected proteins. A gene that is never expressed in your system cannot be a true negative control.
LOOK UP DON'T GUESS: adjusted p-values, gene set overlap counts, and which genes from your input list drive each enriched term. Always retrieve the inputGenes field from enrichment results — do not assume which genes caused a term to be significant. When a term looks surprising, verify by checking which genes overlap.
When to Use This Skill
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