Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Data Analysis
GO/KEGG Enrichment
Performs GO (Gene Ontology) and KEGG pathway enrichment analysis on gene lists. Trigger when: - User provides a list of genes (symbols or IDs) and asks for e...
skill openclawclawhub Free
Performs GO (Gene Ontology) and KEGG pathway enrichment analysis on gene lists. Trigger when: - User provides a list of genes (symbols or IDs) and asks for e...
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- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- requirements.txt, SKILL.md, scripts/main.py, references/example_gene_list.txt, references/GO_KEGG_Reference.md, references/requirements.txt
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
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- Publisher
- AIPOCH-AI
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Documentation
GO/KEGG Enrichment Analysis
Automated pipeline for Gene Ontology and KEGG pathway enrichment analysis with result interpretation and visualization.
Features
GO Enrichment: Biological Process (BP), Molecular Function (MF), Cellular Component (CC) KEGG Pathway: Pathway enrichment with organism-specific mapping Multiple ID Support: Gene symbols, Entrez IDs, Ensembl IDs, RefSeq Statistical Methods: Hypergeometric test, Fisher's exact test, GSEA support Visualizations: Bar plots, dot plots, enrichment maps, cnet plots Result Interpretation: Automatic biological significance summary
Supported Organisms
Common NameScientific NameKEGG CodeOrgDB PackageHumanHomo sapienshsaorg.Hs.eg.dbMouseMus musculusmmuorg.Mm.eg.dbRatRattus norvegicusrnoorg.Rn.eg.dbZebrafishDanio reriodreorg.Dr.eg.dbFlyDrosophila melanogasterdmeorg.Dm.eg.dbYeastSaccharomyces cerevisiaesceorg.Sc.sgd.db
Basic Usage
# Run enrichment analysis with gene list python scripts/main.py --genes gene_list.txt --organism human --output results/
Parameters
ParameterDescriptionDefaultRequired--genesPath to gene list file (one gene per line)-Yes--organismOrganism code (human/mouse/rat/zebrafish/fly/yeast)humanNo--id-typeGene ID type (symbol/entrez/ensembl/refseq)symbolNo--backgroundBackground gene list fileall genesNo--pvalue-cutoffP-value cutoff for significance0.05No--qvalue-cutoffAdjusted p-value (q-value) cutoff0.2No--analysisAnalysis type (go/kegg/all)allNo--outputOutput directory./enrichment_resultsNo--formatOutput format (csv/tsv/excel/all)allNo
Advanced Usage
# GO enrichment only with specific ontology python scripts/main.py \ --genes deg_upregulated.txt \ --organism mouse \ --analysis go \ --go-ontologies BP,MF \ --pvalue-cutoff 0.01 \ --output go_results/ # KEGG enrichment with custom background python scripts/main.py \ --genes treatment_genes.txt \ --background all_expressed_genes.txt \ --organism human \ --analysis kegg \ --qvalue-cutoff 0.05 \ --output kegg_results/
Gene List File
TP53 BRCA1 EGFR MYC KRAS PTEN
With Expression Values (for GSEA)
gene,log2FoldChange TP53,2.5 BRCA1,-1.8 EGFR,3.2
Output Files
output/ โโโ go_enrichment/ โ โโโ GO_BP_results.csv # Biological Process results โ โโโ GO_MF_results.csv # Molecular Function results โ โโโ GO_CC_results.csv # Cellular Component results โ โโโ GO_BP_barplot.pdf # Visualization โ โโโ GO_MF_dotplot.pdf โ โโโ GO_summary.txt # Interpretation summary โโโ kegg_enrichment/ โ โโโ KEGG_results.csv # Pathway results โ โโโ KEGG_barplot.pdf โ โโโ KEGG_dotplot.pdf โ โโโ KEGG_pathview/ # Pathway diagrams โโโ combined_report.html # Interactive report
Result Interpretation
The tool automatically generates biological interpretation including: Top Enriched Terms: Significant GO terms/pathways ranked by enrichment ratio Functional Themes: Clustered biological themes from enriched terms Key Genes: Core genes driving enrichment in significant terms Network Relationships: Gene-term relationship visualization Clinical Relevance: Disease associations (for human genes)
Technical Difficulty: HIGH
โ ๏ธ AI่ชไธป้ชๆถ็ถๆ: ้ไบบๅทฅๆฃๆฅ This skill requires: R/Bioconductor environment with clusterProfiler Multiple annotation databases (org.*.eg.db) KEGG REST API access Complex visualization dependencies
Required R Packages
install.packages(c("BiocManager", "ggplot2", "dplyr", "readr")) BiocManager::install(c( "clusterProfiler", "org.Hs.eg.db", "org.Mm.eg.db", "org.Rn.eg.db", "enrichplot", "pathview", "DOSE" ))
Python Dependencies
pip install pandas numpy matplotlib seaborn rpy2
Example Workflow
Prepare Input: Create gene list from DEG analysis Run Analysis: Execute main.py with appropriate parameters Review Results: Check generated CSV files and visualizations Interpret: Read auto-generated summary for biological insights
References
See references/ for: clusterProfiler documentation KEGG API guide Statistical methods explanation Visualization examples
Limitations
Requires internet connection for KEGG database queries Large gene lists (>5000) may require increased memory Some pathways may not be available for all organisms KEGG API has rate limits (max 3 requests/second)
Risk Assessment
Risk IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow
Security Checklist
No hardcoded credentials or API keys No unauthorized file system access (../) Output does not expose sensitive information Prompt injection protections in place Input file paths validated (no ../ traversal) Output directory restricted to workspace Script execution in sandboxed environment Error messages sanitized (no stack traces exposed) Dependencies audited
Prerequisites
# Python dependencies pip install -r requirements.txt
Success Metrics
Successfully executes main functionality Output meets quality standards Handles edge cases gracefully Performance is acceptable
Test Cases
Basic Functionality: Standard input โ Expected output Edge Case: Invalid input โ Graceful error handling Performance: Large dataset โ Acceptable processing time
Lifecycle Status
Current Stage: Draft Next Review Date: 2026-03-06 Known Issues: None Planned Improvements: Performance optimization Additional feature support
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Files2 Docs1 Scripts
- SKILL.md
- references/GO_KEGG_Reference.md
- scripts/main.py
- references/example_gene_list.txt
- references/requirements.txt
- requirements.txt