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bioinformatics

Performs bioinformatics analyses including pathway enrichment, gene ontology analysis, protein-protein interaction networks, multi-omics integration, and biological sequence database querying; trigger when users discuss gene sets, biological pathways, functional annotation, or omics data integration.

Star
962
分类
科研
许可证
MIT
最近提交
2026年10月8日

skills/bioinformatics/SKILL.md

在 GitHub 上查看

When to Trigger

Activate this skill when the user mentions:

  • Pathway analysis, KEGG, Reactome, WikiPathways
  • Gene Ontology (GO) enrichment, biological process, molecular function
  • Protein-protein interaction (PPI) networks, STRING, BioGRID
  • Multi-omics integration (transcriptomics + proteomics + metabolomics)
  • Gene set enrichment analysis (GSEA), over-representation analysis (ORA)
  • Sequence databases, UniProt, NCBI, Ensembl queries
  • Single-cell RNA-seq analysis, clustering, trajectory inference

Step-by-Step Methodology

  1. Data preparation - Standardize gene/protein identifiers (convert to Entrez, Ensembl, or UniProt IDs as needed). Remove duplicates and handle ambiguous mappings. Verify organism and genome build.
  2. Differential analysis - For transcriptomics: DESeq2 or edgeR (count data), limma-voom (normalized). For proteomics: limma with appropriate normalization. Apply multiple testing correction (BH-FDR). Set thresholds (|log2FC| > 1, padj < 0.05 as defaults, adjustable).
  3. Functional enrichment - Perform GO enrichment (BP, MF, CC) using clusterProfiler, g:Profiler, or DAVID. Run KEGG/Reactome pathway enrichment. Use GSEA for ranked gene lists (no arbitrary cutoff). Report enriched terms with gene ratio, p-value, adjusted p-value, and gene members.
  4. Network analysis - Build PPI networks from STRING (confidence > 0.7 for high confidence). Identify hub genes (degree centrality), bottleneck nodes (betweenness centrality), and functional modules (MCODE, Louvain clustering). Overlay expression data on network.
  5. Multi-omics integration - For paired omics: correlation analysis, canonical correlation (CCA), or MOFA/DIABLO. Map features across omics layers using shared identifiers or known biological connections. Identify convergent pathways.
  6. Single-cell analysis - QC filtering (genes/cell, UMI/cell, mitochondrial %). Normalization (scran, SCTransform). Dimensionality reduction (PCA, UMAP). Clustering (Leiden, Louvain). Cell type annotation (SingleR, scType, marker genes). Trajectory inference (Monocle3, Slingshot).
  7. Visualization - Generate volcano plots, heatmaps (with hierarchical clustering), dot plots (enrichment), network diagrams, UMAP/tSNE plots (single-cell), and circos plots (multi-omics).

Key Databases and Tools

  • Gene Ontology (GO) - Functional annotations
  • KEGG / Reactome / WikiPathways - Pathway databases
  • STRING / BioGRID / IntAct - PPI databases
  • Ensembl / NCBI / UniProt - Sequence and annotation databases
  • clusterProfiler / g:Profiler / DAVID - Enrichment tools
  • Seurat / Scanpy - Single-cell analysis frameworks
  • Cytoscape - Network visualization

Output Format

  • Enrichment results as tables: term, description, gene ratio, p-value, padj, gene list.
  • Volcano plots with labeled significant genes and fold-change thresholds.
  • Network figures with node coloring (expression), size (degree), and module highlighting.
  • UMAP/tSNE plots with cluster labels and cell type annotations.
  • Heatmaps with dendrograms and annotation bars.

Quality Checklist

  • Gene ID mapping verified (conversion losses reported)
  • Background gene set appropriate for enrichment analysis
  • Multiple testing correction applied (BH-FDR or equivalent)
  • Redundant GO terms handled (semantic similarity, REVIGO)
  • Network confidence threshold specified and justified
  • Single-cell QC thresholds documented
  • Batch effects assessed and corrected if present
  • Results cross-validated across databases or methods
  • Biological interpretation grounded in literature

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