modbender 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:
- 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
⚠️ 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
- 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 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
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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