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BytesAgain β€Ί Skills β€Ί GO/KEGG Enrichment
πŸ¦€ ClawHub

GO/KEGG Enrichment

by @aipoch-ai

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...

Versionv1.0.0
Downloads775
Installs2
#clawhub
TERMINAL
clawhub install go-kegg-enrichment

πŸ“– About This Skill

name: go-kegg-enrichment description: "Performs GO (Gene Ontology) and KEGG pathway enrichment analysis on\ \ gene lists.\nTrigger when: \n- User provides a list of genes (symbols or IDs)\ \ and asks for enrichment analysis\n- User mentions \"GO enrichment\", \"KEGG enrichment\"\ , \"pathway analysis\"\n- User wants to understand biological functions of gene\ \ sets\n- User provides differentially expressed genes (DEGs) and asks for interpretation\n\ - Input: gene list (file or inline), organism (human/mouse/rat), background gene\ \ set (optional)\n- Output: enriched terms, statistics, visualizations (barplot,\ \ dotplot, enrichment map)" version: 1.0.0 category: Bioinfo tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

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 Name | Scientific Name | KEGG Code | OrgDB Package | |-------------|-----------------|-----------|---------------| | Human | Homo sapiens | hsa | org.Hs.eg.db | | Mouse | Mus musculus | mmu | org.Mm.eg.db | | Rat | Rattus norvegicus | rno | org.Rn.eg.db | | Zebrafish | Danio rerio | dre | org.Dr.eg.db | | Fly | Drosophila melanogaster | dme | org.Dm.eg.db | | Yeast | Saccharomyces cerevisiae | sce | org.Sc.sgd.db |

    Usage

    Basic Usage

    # Run enrichment analysis with gene list
python scripts/main.py --genes gene_list.txt --organism human --output results/

    Parameters

    | Parameter | Description | Default | Required | |-----------|-------------|---------|----------| | --genes | Path to gene list file (one gene per line) | - | Yes | | --organism | Organism code (human/mouse/rat/zebrafish/fly/yeast) | human | No | | --id-type | Gene ID type (symbol/entrez/ensembl/refseq) | symbol | No | | --background | Background gene list file | all genes | No | | --pvalue-cutoff | P-value cutoff for significance | 0.05 | No | | --qvalue-cutoff | Adjusted p-value (q-value) cutoff | 0.2 | No | | --analysis | Analysis type (go/kegg/all) | all | No | | --output | Output directory | ./enrichment_results | No | | --format | Output format (csv/tsv/excel/all) | all | No |

    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/

    Input Format

    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/
β”œβ”€β”€ go_enrichment/
β”‚   β”œβ”€β”€ GO_BP_results.csv       # Biological Process results
β”‚   β”œβ”€β”€ GO_MF_results.csv       # Molecular Function results
β”‚   β”œβ”€β”€ GO_CC_results.csv       # Cellular Component results
β”‚   β”œβ”€β”€ GO_BP_barplot.pdf       # Visualization
β”‚   β”œβ”€β”€ GO_MF_dotplot.pdf
β”‚   └── GO_summary.txt          # Interpretation summary
β”œβ”€β”€ kegg_enrichment/
β”‚   β”œβ”€β”€ KEGG_results.csv        # Pathway results
β”‚   β”œβ”€β”€ KEGG_barplot.pdf
β”‚   β”œβ”€β”€ KEGG_dotplot.pdf
β”‚   └── KEGG_pathview/          # Pathway diagrams
└── combined_report.html        # Interactive report

    Result Interpretation

    The tool automatically generates biological interpretation including:

    1. Top Enriched Terms: Significant GO terms/pathways ranked by enrichment ratio 2. Functional Themes: Clustered biological themes from enriched terms 3. Key Genes: Core genes driving enrichment in significant terms 4. Network Relationships: Gene-term relationship visualization 5. Clinical Relevance: Disease associations (for human genes)

    Technical Difficulty: HIGH

    ⚠️ AIθ‡ͺδΈ»ιͺŒζ”ΆηŠΆζ€: ιœ€δΊΊε·₯ζ£€ζŸ₯

    This skill requires:

  • R/Bioconductor environment with clusterProfiler
  • Multiple annotation databases (org.*.eg.db)
  • KEGG REST API access
  • Complex visualization dependencies

    • 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

    1. Prepare Input: Create gene list from DEG analysis 2. Run Analysis: Execute main.py with appropriate parameters 3. Review Results: Check generated CSV files and visualizations 4. 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 Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |

    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

    Evaluation Criteria

    Success Metrics

  • [ ] Successfully executes main functionality
  • [ ] Output meets quality standards
  • [ ] Handles edge cases gracefully
  • [ ] Performance is acceptable

    • Test Cases

    1. Basic Functionality: Standard input β†’ Expected output 2. Edge Case: Invalid input β†’ Graceful error handling 3. Performance: Large dataset β†’ Acceptable processing time

    Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
    • - Performance optimization - Additional feature support

    πŸ’‘ Examples

    Basic Usage

    # Run enrichment analysis with gene list
python scripts/main.py --genes gene_list.txt --organism human --output results/

    Parameters

    | Parameter | Description | Default | Required | |-----------|-------------|---------|----------| | --genes | Path to gene list file (one gene per line) | - | Yes | | --organism | Organism code (human/mouse/rat/zebrafish/fly/yeast) | human | No | | --id-type | Gene ID type (symbol/entrez/ensembl/refseq) | symbol | No | | --background | Background gene list file | all genes | No | | --pvalue-cutoff | P-value cutoff for significance | 0.05 | No | | --qvalue-cutoff | Adjusted p-value (q-value) cutoff | 0.2 | No | | --analysis | Analysis type (go/kegg/all) | all | No | | --output | Output directory | ./enrichment_results | No | | --format | Output format (csv/tsv/excel/all) | all | No |

    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/

    βš™οΈ Configuration

    # Python dependencies
pip install -r requirements.txt