// Perform gene set enrichment analysis using AI research providers. Analyzes lists of genes to identify biological themes, pathways, and functional associations.
| name | gene-set-enrichment |
| description | Perform gene set enrichment analysis using AI research providers. Analyzes lists of genes to identify biological themes, pathways, and functional associations. |
Toolkit for performing gene set enrichment analysis on lists of genes using AI research providers. Identifies biological themes, pathways, diseases, and functional relationships within gene sets.
Use this skill when:
This skill uses HEAVYWEIGHT mode by default:
sonar-deep-researchUsers can override to use comprehensive mode if needed for publication-quality analysis.
# At least one is required:
export PERPLEXITY_API_KEY="your-perplexity-key" # Recommended for speed
export OPENAI_API_KEY="your-openai-key" # For comprehensive analysis
export FUTUREHOUSE_API_KEY="your-futurehouse-key" # For scientific literature
# Quick enrichment analysis
uv run deep-research-client research \
--template .claude/skills/gene-set-enrichment/examples/enrichment_template.md \
--var "geneset=APOH APP COL3A1 VEGFA THBD" \
--var "organism=human" \
--provider perplexity \
--model sonar-deep-research \
--output enrichment_results.md
# Comprehensive analysis (slower, more detailed)
uv run deep-research-client research \
--template .claude/skills/gene-set-enrichment/examples/enrichment_template.md \
--var "geneset=APOH APP COL3A1 VEGFA THBD" \
--var "organism=human" \
--provider openai \
--output enrichment_detailed.md
uv run deep-research-client research \
"Perform gene set enrichment analysis on: APOH APP COL3A1 VEGFA THBD. Identify common pathways, biological processes, and disease associations." \
--provider perplexity \
--model sonar-deep-research \
--output quick_enrichment.md
The enrichment template accepts:
{geneset} - Space-separated list of gene symbols (e.g., "APOH APP VEGFA"){organism} - Organism name (e.g., "human", "mouse", "rat"){context} - Optional: Study context (e.g., "upregulated in heart disease", "differentially expressed in cancer")Results include:
APOH APP CND2 COL3A1 COL5A2 CXCL6 FGFR1 FSTL1 ITGAV JAG1 JAG2 KCNJ8 LPL LRPAP1 LUM MSX1 NRP1 OLR1 PDGFA PF4 PGLYRP1 POSTN PRG2 PTK2 S100A4 SERPINA5 SLCO2A1 SPP1 STC1 THBD TIMP1 TNFRSF21 VAV2 VCAN VEGFA VTN
This set appears enriched for:
TP53 BRCA1 BRCA2 PTEN RB1
Cancer-related tumor suppressors for testing.
uv run deep-research-client research \
--template .claude/skills/gene-set-enrichment/examples/enrichment_template.md \
--var "geneset=GENE1 GENE2 GENE3" \
--var "organism=human" \
--provider perplexity \
--model sonar-pro
uv run deep-research-client research \
--template .claude/skills/gene-set-enrichment/examples/enrichment_template.md \
--var "geneset=GENE1 GENE2 GENE3" \
--var "organism=human" \
--var "context=upregulated in diabetic cardiomyopathy" \
--var "focus=cardiac remodeling pathways"
uv run deep-research-client research \
--template .claude/skills/gene-set-enrichment/examples/enrichment_template.md \
--var "geneset=GENE1 GENE2 GENE3" \
--var "organism=human" \
--provider openai
--no-cache to force re-analysis with updated databases# Python example: extract top genes
import pandas as pd
# Load DEG results
degs = pd.read_csv("deg_results.csv")
top_genes = degs[degs['padj'] < 0.05].head(50)['gene_symbol'].tolist()
geneset = " ".join(top_genes)
# Use with template
print(f"--var \"geneset={geneset}\"")
# Extract gene symbols from analysis output
cut -f1 significant_genes.txt | tr '\n' ' ' > geneset.txt
# Use in template
uv run deep-research-client research \
--template .claude/skills/gene-set-enrichment/examples/enrichment_template.md \
--var "geneset=$(cat geneset.txt)" \
--var "organism=human"
This AI-based approach complements traditional enrichment tools (DAVID, Enrichr, g:Profiler):
Advantages:
Traditional Tools Still Better For:
Best Practice: Use both approaches: