go-kegg-enrichment

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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 enrichment analysis - User mentions "GO enrichment", "KEGG enrichment", "pathway analysis" - User wants to understand biological functions of gene sets - User provides differentially expressed genes (DEGs) and asks for interpretation - Input: gene list (file or inline), organism (human/mouse/rat), background gene set (optional) - Output: enriched terms, statistics, visualizations (barplot, dotplot, enrichment map)

modbender By modbender schedule Updated 3/6/2026
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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/

├── go_enrichment/

│   ├── GO_BP_results.csv       # Biological Process results

│   ├── GO_MF_results.csv       # Molecular Function results

│   ├── GO_CC_results.csv       # Cellular Component results

│   ├── GO_BP_barplot.pdf       # Visualization

│   ├── GO_MF_dotplot.pdf

│   └── GO_summary.txt          # Interpretation summary

├── kegg_enrichment/

│   ├── KEGG_results.csv        # Pathway results

│   ├── KEGG_barplot.pdf

│   ├── KEGG_dotplot.pdf

│   └── 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

⚠️ AI自主验收状态: 需人工检查

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 → Expected output

  2. Edge Case: Invalid input → Graceful error handling

  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft

  • Next Review Date: 2026-03-06

  • Known Issues: None

  • Planned Improvements:

    • Performance optimization

    • Additional feature support

Install via CLI
npx skills add https://github.com/modbender/skill-library-mcp --skill go-kegg-enrichment
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