genomics-analysis
Orchestrates a genomics analysis workflow from gene query through expression analysis to pathway enrichment. Use when investigating gene function, analyzing expression data, or performing pathway-level interpretation. NOT for pure protein structure modeling or drug-target interaction analysis.
Best use case
genomics-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Orchestrates a genomics analysis workflow from gene query through expression analysis to pathway enrichment. Use when investigating gene function, analyzing expression data, or performing pathway-level interpretation. NOT for pure protein structure modeling or drug-target interaction analysis.
Teams using genomics-analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/genomics-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How genomics-analysis Compares
| Feature / Agent | genomics-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Orchestrates a genomics analysis workflow from gene query through expression analysis to pathway enrichment. Use when investigating gene function, analyzing expression data, or performing pathway-level interpretation. NOT for pure protein structure modeling or drug-target interaction analysis.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Genomics Analysis (Meta Skill) This meta-skill coordinates a complete genomics analysis pipeline by integrating gene database queries, sequence analysis, expression profiling, and pathway enrichment into a unified workflow. It combines three specialized skills to deliver comprehensive gene-level and systems-level biological insights. ## Workflow ### Step 1: Gene Information Retrieval Query NCBI Entrez for comprehensive gene details including official nomenclature, genomic coordinates, transcript variants, and functional annotations. Retrieve orthologs across model organisms for evolutionary context. Pull known variants from ClinVar and dbSNP, noting pathogenic or pharmacogenomic associations. Collect linked references from PubMed for recent literature context. ### Step 2: Sequence Analysis Use BioPython to perform sequence-level analyses on retrieved gene and protein sequences: - Multiple sequence alignment of orthologs to identify conserved regions - Motif discovery in promoter regions or protein domains - Domain architecture mapping against Pfam/InterPro signatures - Codon usage analysis for expression optimization studies - Variant impact prediction based on conservation scores ### Step 3: Expression Analysis Apply scanpy for expression data analysis, supporting both single-cell and bulk RNA-seq workflows: - For single-cell: quality control, normalization, clustering, marker gene identification, cell type annotation - For bulk: differential expression analysis, volcano plots, heatmaps - Cross-dataset comparison when multiple conditions are available - Identification of co-expressed gene modules ### Step 4: Pathway Enrichment and Functional Annotation Map differentially expressed or co-expressed genes to biological pathways: - KEGG pathway mapping for metabolic and signaling context - Gene Ontology enrichment (biological process, molecular function, cellular component) - Reactome pathway analysis for detailed mechanistic understanding - Network-based enrichment to identify hub genes and regulatory modules ### Step 5: Integrated Report Generation Compile findings into a structured report with: - Gene summary card with key identifiers and annotations - Sequence conservation highlights and domain maps - Expression analysis results with statistical summaries - Enriched pathways ranked by significance - Key findings synthesis connecting sequence, expression, and pathway data - Publication-ready figures and supplementary tables ## Integration Points - **ncbi-entrez** -- Gene records, variant data, orthologs, literature links - **biopython-bio** -- Sequence alignment, motif search, domain analysis, format conversion - **scanpy-singlecell** -- Expression quantification, clustering, differential expression, visualization ## Output Formats - **Gene card**: Symbol, aliases, genomic location, function summary, disease associations - **Alignment view**: Conserved regions highlighted across orthologs - **Expression summary**: DE gene lists with fold change, p-values, FDR - **Pathway table**: Enriched pathways with gene counts, p-values, leading-edge genes - **Figures**: Heatmaps, volcano plots, UMAP embeddings, pathway diagrams ## Best Practices 1. Start with gene identifiers from a reliable source (NCBI Gene ID or HGNC symbol) 2. Verify gene nomenclature across databases to avoid confusion from aliases 3. Use appropriate normalization for the expression data type (TPM, CPM, SCTransform) 4. Apply multiple testing correction (Benjamini-Hochberg) for all enrichment analyses 5. Set biologically meaningful fold-change thresholds alongside statistical cutoffs 6. Include both up- and down-regulated gene sets in pathway analysis 7. Cross-reference pathway results with known biology to filter spurious enrichments 8. Report effect sizes and confidence intervals, not just p-values 9. Note species differences when translating findings from model organisms 10. Archive intermediate results for reproducibility and downstream re-analysis
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