genome-analysis
Performs genomics analyses including gene expression profiling, BLAST sequence alignment, GWAS interpretation, variant calling, and genome assembly tasks; trigger when the user mentions DNA/RNA sequences, SNPs, gene panels, or comparative genomics.
Best use case
genome-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Performs genomics analyses including gene expression profiling, BLAST sequence alignment, GWAS interpretation, variant calling, and genome assembly tasks; trigger when the user mentions DNA/RNA sequences, SNPs, gene panels, or comparative genomics.
Teams using genome-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/genome-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How genome-analysis Compares
| Feature / Agent | genome-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?
Performs genomics analyses including gene expression profiling, BLAST sequence alignment, GWAS interpretation, variant calling, and genome assembly tasks; trigger when the user mentions DNA/RNA sequences, SNPs, gene panels, or comparative genomics.
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
## When to Trigger Activate this skill when the user mentions any of the following: - BLAST, sequence alignment, homology search - Gene expression, RNA-seq, differential expression, DESeq2, edgeR - GWAS, SNP, variant calling, VCF files - Genome assembly, annotation, scaffolding - Phylogenomics, comparative genomics, synteny - Genotyping, haplotype analysis, linkage disequilibrium ## Step-by-Step Methodology 1. **Clarify the organism and genome build** - Confirm species, reference genome version (e.g., GRCh38 for human, GRCm39 for mouse), and data type (WGS, WES, RNA-seq, microarray). 2. **Data ingestion and QC** - Check raw data quality (FastQC metrics, read depth, coverage). Flag low-quality samples before proceeding. 3. **Alignment / Assembly** - For alignment tasks, specify the aligner (BWA-MEM2, STAR for RNA-seq, minimap2 for long reads). For de novo assembly, recommend assemblers (SPAdes, Flye, hifiasm). 4. **Variant calling / Expression quantification** - Use GATK HaplotypeCaller or DeepVariant for variants; featureCounts or Salmon for transcript quantification. 5. **Statistical analysis** - Apply appropriate multiple-testing correction (Bonferroni, BH-FDR). For GWAS, use mixed models (BOLT-LMM, SAIGE) to handle population structure. 6. **Annotation and interpretation** - Annotate variants with VEP/ANNOVAR; enrich gene lists with GO, KEGG, Reactome pathways. 7. **Visualization** - Generate Manhattan plots (GWAS), volcano plots (DE), circos plots (structural variants), or heatmaps (expression clusters). ## Key Databases and Tools - **NCBI GenBank / RefSeq** - Reference sequences and annotations - **Ensembl / UCSC Genome Browser** - Genome browsing and tracks - **BLAST (NCBI)** - Sequence similarity search - **UniProt** - Protein function annotation - **ClinVar / gnomAD** - Clinical variant interpretation - **KEGG / Reactome / Gene Ontology** - Pathway and functional enrichment - **GEO / ArrayExpress** - Public expression datasets ## Output Format - Provide results in structured tables (gene, log2FC, p-value, adjusted p-value). - Include publication-quality figure descriptions with axis labels and legends. - Report genome coordinates in standard notation (chr:start-end, 1-based). - Always state the reference genome build used. ## Quality Checklist - [ ] Reference genome build explicitly stated - [ ] Multiple-testing correction applied and method named - [ ] Sample sizes and statistical power discussed - [ ] QC metrics reported (mapping rate, duplication rate, coverage) - [ ] Biological vs. statistical significance distinguished - [ ] All gene identifiers use standard nomenclature (HGNC symbols for human) - [ ] Effect sizes reported alongside p-values - [ ] Reproducibility: exact tool versions and parameters documented
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