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    "sections": [
      {
        "title": "GO/KEGG Enrichment Analysis",
        "body": "Automated pipeline for Gene Ontology and KEGG pathway enrichment analysis with result interpretation and visualization."
      },
      {
        "title": "Features",
        "body": "GO Enrichment: Biological Process (BP), Molecular Function (MF), Cellular Component (CC)\nKEGG Pathway: Pathway enrichment with organism-specific mapping\nMultiple ID Support: Gene symbols, Entrez IDs, Ensembl IDs, RefSeq\nStatistical Methods: Hypergeometric test, Fisher's exact test, GSEA support\nVisualizations: Bar plots, dot plots, enrichment maps, cnet plots\nResult Interpretation: Automatic biological significance summary"
      },
      {
        "title": "Supported Organisms",
        "body": "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"
      },
      {
        "title": "Basic Usage",
        "body": "# Run enrichment analysis with gene list\npython scripts/main.py --genes gene_list.txt --organism human --output results/"
      },
      {
        "title": "Parameters",
        "body": "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"
      },
      {
        "title": "Advanced Usage",
        "body": "# GO enrichment only with specific ontology\npython scripts/main.py \\\n    --genes deg_upregulated.txt \\\n    --organism mouse \\\n    --analysis go \\\n    --go-ontologies BP,MF \\\n    --pvalue-cutoff 0.01 \\\n    --output go_results/\n\n# KEGG enrichment with custom background\npython scripts/main.py \\\n    --genes treatment_genes.txt \\\n    --background all_expressed_genes.txt \\\n    --organism human \\\n    --analysis kegg \\\n    --qvalue-cutoff 0.05 \\\n    --output kegg_results/"
      },
      {
        "title": "Gene List File",
        "body": "TP53\nBRCA1\nEGFR\nMYC\nKRAS\nPTEN"
      },
      {
        "title": "With Expression Values (for GSEA)",
        "body": "gene,log2FoldChange\nTP53,2.5\nBRCA1,-1.8\nEGFR,3.2"
      },
      {
        "title": "Output Files",
        "body": "output/\n├── go_enrichment/\n│   ├── GO_BP_results.csv       # Biological Process results\n│   ├── GO_MF_results.csv       # Molecular Function results\n│   ├── GO_CC_results.csv       # Cellular Component results\n│   ├── GO_BP_barplot.pdf       # Visualization\n│   ├── GO_MF_dotplot.pdf\n│   └── GO_summary.txt          # Interpretation summary\n├── kegg_enrichment/\n│   ├── KEGG_results.csv        # Pathway results\n│   ├── KEGG_barplot.pdf\n│   ├── KEGG_dotplot.pdf\n│   └── KEGG_pathview/          # Pathway diagrams\n└── combined_report.html        # Interactive report"
      },
      {
        "title": "Result Interpretation",
        "body": "The tool automatically generates biological interpretation including:\n\nTop Enriched Terms: Significant GO terms/pathways ranked by enrichment ratio\nFunctional Themes: Clustered biological themes from enriched terms\nKey Genes: Core genes driving enrichment in significant terms\nNetwork Relationships: Gene-term relationship visualization\nClinical Relevance: Disease associations (for human genes)"
      },
      {
        "title": "Technical Difficulty: HIGH",
        "body": "⚠️ AI自主验收状态: 需人工检查\n\nThis skill requires:\n\nR/Bioconductor environment with clusterProfiler\nMultiple annotation databases (org.*.eg.db)\nKEGG REST API access\nComplex visualization dependencies"
      },
      {
        "title": "Required R Packages",
        "body": "install.packages(c(\"BiocManager\", \"ggplot2\", \"dplyr\", \"readr\"))\nBiocManager::install(c(\n    \"clusterProfiler\", \n    \"org.Hs.eg.db\", \"org.Mm.eg.db\", \"org.Rn.eg.db\",\n    \"enrichplot\", \"pathview\", \"DOSE\"\n))"
      },
      {
        "title": "Python Dependencies",
        "body": "pip install pandas numpy matplotlib seaborn rpy2"
      },
      {
        "title": "Example Workflow",
        "body": "Prepare Input: Create gene list from DEG analysis\nRun Analysis: Execute main.py with appropriate parameters\nReview Results: Check generated CSV files and visualizations\nInterpret: Read auto-generated summary for biological insights"
      },
      {
        "title": "References",
        "body": "See references/ for:\n\nclusterProfiler documentation\nKEGG API guide\nStatistical methods explanation\nVisualization examples"
      },
      {
        "title": "Limitations",
        "body": "Requires internet connection for KEGG database queries\nLarge gene lists (>5000) may require increased memory\nSome pathways may not be available for all organisms\nKEGG API has rate limits (max 3 requests/second)"
      },
      {
        "title": "Risk Assessment",
        "body": "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"
      },
      {
        "title": "Security Checklist",
        "body": "No hardcoded credentials or API keys\n No unauthorized file system access (../)\n Output does not expose sensitive information\n Prompt injection protections in place\n Input file paths validated (no ../ traversal)\n Output directory restricted to workspace\n Script execution in sandboxed environment\n Error messages sanitized (no stack traces exposed)\n Dependencies audited"
      },
      {
        "title": "Prerequisites",
        "body": "# Python dependencies\npip install -r requirements.txt"
      },
      {
        "title": "Success Metrics",
        "body": "Successfully executes main functionality\n Output meets quality standards\n Handles edge cases gracefully\n Performance is acceptable"
      },
      {
        "title": "Test Cases",
        "body": "Basic Functionality: Standard input → Expected output\nEdge Case: Invalid input → Graceful error handling\nPerformance: Large dataset → Acceptable processing time"
      },
      {
        "title": "Lifecycle Status",
        "body": "Current Stage: Draft\nNext Review Date: 2026-03-06\nKnown Issues: None\nPlanned Improvements:\n\nPerformance optimization\nAdditional feature support"
      }
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    "body": "GO/KEGG Enrichment Analysis\n\nAutomated pipeline for Gene Ontology and KEGG pathway enrichment analysis with result interpretation and visualization.\n\nFeatures\nGO Enrichment: Biological Process (BP), Molecular Function (MF), Cellular Component (CC)\nKEGG Pathway: Pathway enrichment with organism-specific mapping\nMultiple ID Support: Gene symbols, Entrez IDs, Ensembl IDs, RefSeq\nStatistical Methods: Hypergeometric test, Fisher's exact test, GSEA support\nVisualizations: Bar plots, dot plots, enrichment maps, cnet plots\nResult Interpretation: Automatic biological significance summary\nSupported Organisms\nCommon Name\tScientific Name\tKEGG Code\tOrgDB Package\nHuman\tHomo sapiens\thsa\torg.Hs.eg.db\nMouse\tMus musculus\tmmu\torg.Mm.eg.db\nRat\tRattus norvegicus\trno\torg.Rn.eg.db\nZebrafish\tDanio rerio\tdre\torg.Dr.eg.db\nFly\tDrosophila melanogaster\tdme\torg.Dm.eg.db\nYeast\tSaccharomyces cerevisiae\tsce\torg.Sc.sgd.db\nUsage\nBasic Usage\n# Run enrichment analysis with gene list\npython scripts/main.py --genes gene_list.txt --organism human --output results/\n\nParameters\nParameter\tDescription\tDefault\tRequired\n--genes\tPath to gene list file (one gene per line)\t-\tYes\n--organism\tOrganism code (human/mouse/rat/zebrafish/fly/yeast)\thuman\tNo\n--id-type\tGene ID type (symbol/entrez/ensembl/refseq)\tsymbol\tNo\n--background\tBackground gene list file\tall genes\tNo\n--pvalue-cutoff\tP-value cutoff for significance\t0.05\tNo\n--qvalue-cutoff\tAdjusted p-value (q-value) cutoff\t0.2\tNo\n--analysis\tAnalysis type (go/kegg/all)\tall\tNo\n--output\tOutput directory\t./enrichment_results\tNo\n--format\tOutput format (csv/tsv/excel/all)\tall\tNo\nAdvanced Usage\n# GO enrichment only with specific ontology\npython scripts/main.py \\\n    --genes deg_upregulated.txt \\\n    --organism mouse \\\n    --analysis go \\\n    --go-ontologies BP,MF \\\n    --pvalue-cutoff 0.01 \\\n    --output go_results/\n\n# KEGG enrichment with custom background\npython scripts/main.py \\\n    --genes treatment_genes.txt \\\n    --background all_expressed_genes.txt \\\n    --organism human \\\n    --analysis kegg \\\n    --qvalue-cutoff 0.05 \\\n    --output kegg_results/\n\nInput Format\nGene List File\nTP53\nBRCA1\nEGFR\nMYC\nKRAS\nPTEN\n\nWith Expression Values (for GSEA)\ngene,log2FoldChange\nTP53,2.5\nBRCA1,-1.8\nEGFR,3.2\n\nOutput Files\noutput/\n├── go_enrichment/\n│   ├── GO_BP_results.csv       # Biological Process results\n│   ├── GO_MF_results.csv       # Molecular Function results\n│   ├── GO_CC_results.csv       # Cellular Component results\n│   ├── GO_BP_barplot.pdf       # Visualization\n│   ├── GO_MF_dotplot.pdf\n│   └── GO_summary.txt          # Interpretation summary\n├── kegg_enrichment/\n│   ├── KEGG_results.csv        # Pathway results\n│   ├── KEGG_barplot.pdf\n│   ├── KEGG_dotplot.pdf\n│   └── KEGG_pathview/          # Pathway diagrams\n└── combined_report.html        # Interactive report\n\nResult Interpretation\n\nThe tool automatically generates biological interpretation including:\n\nTop Enriched Terms: Significant GO terms/pathways ranked by enrichment ratio\nFunctional Themes: Clustered biological themes from enriched terms\nKey Genes: Core genes driving enrichment in significant terms\nNetwork Relationships: Gene-term relationship visualization\nClinical Relevance: Disease associations (for human genes)\nTechnical Difficulty: HIGH\n\n⚠️ AI自主验收状态: 需人工检查\n\nThis skill requires:\n\nR/Bioconductor environment with clusterProfiler\nMultiple annotation databases (org.*.eg.db)\nKEGG REST API access\nComplex visualization dependencies\nDependencies\nRequired R Packages\ninstall.packages(c(\"BiocManager\", \"ggplot2\", \"dplyr\", \"readr\"))\nBiocManager::install(c(\n    \"clusterProfiler\", \n    \"org.Hs.eg.db\", \"org.Mm.eg.db\", \"org.Rn.eg.db\",\n    \"enrichplot\", \"pathview\", \"DOSE\"\n))\n\nPython Dependencies\npip install pandas numpy matplotlib seaborn rpy2\n\nExample Workflow\nPrepare Input: Create gene list from DEG analysis\nRun Analysis: Execute main.py with appropriate parameters\nReview Results: Check generated CSV files and visualizations\nInterpret: Read auto-generated summary for biological insights\nReferences\n\nSee references/ for:\n\nclusterProfiler documentation\nKEGG API guide\nStatistical methods explanation\nVisualization examples\nLimitations\nRequires internet connection for KEGG database queries\nLarge gene lists (>5000) may require increased memory\nSome pathways may not be available for all organisms\nKEGG API has rate limits (max 3 requests/second)\nRisk Assessment\nRisk Indicator\tAssessment\tLevel\nCode Execution\tPython/R scripts executed locally\tMedium\nNetwork Access\tNo external API calls\tLow\nFile System Access\tRead input files, write output files\tMedium\nInstruction Tampering\tStandard prompt guidelines\tLow\nData Exposure\tOutput files saved to workspace\tLow\nSecurity Checklist\n No hardcoded credentials or API keys\n No unauthorized file system access (../)\n Output does not expose sensitive information\n Prompt injection protections in place\n Input file paths validated (no ../ traversal)\n Output directory restricted to workspace\n Script execution in sandboxed environment\n Error messages sanitized (no stack traces exposed)\n Dependencies audited\nPrerequisites\n# Python dependencies\npip install -r requirements.txt\n\nEvaluation Criteria\nSuccess Metrics\n Successfully executes main functionality\n Output meets quality standards\n Handles edge cases gracefully\n Performance is acceptable\nTest Cases\nBasic Functionality: Standard input → Expected output\nEdge Case: Invalid input → Graceful error handling\nPerformance: Large dataset → Acceptable processing time\nLifecycle Status\nCurrent Stage: Draft\nNext Review Date: 2026-03-06\nKnown Issues: None\nPlanned Improvements:\nPerformance optimization\nAdditional feature support"
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