Genome Analysis
- 16 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
genome-analysis is a Claude skill that guides genomics analyses including BLAST alignment, RNA-seq expression, GWAS, variant calling, and genome assembly.
About
This skill runs genomics analyses including gene expression profiling, BLAST alignment, GWAS interpretation, variant calling, and genome assembly. A developer or researcher uses it to structure a bioinformatics workflow from data QC through alignment, statistics, annotation, and visualization. It names specific tools and enforces a reproducibility checklist.
- Covers BLAST, RNA-seq DE, GWAS, variant calling, and genome assembly
- Names concrete tools (BWA-MEM2, STAR, GATK, DeepVariant, Salmon)
- Includes an 8-item quality checklist for reproducible genomics
Genome Analysis by the numbers
- 16 all-time installs (skills.sh)
- Ranked #1,318 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
genome-analysis capabilities & compatibility
- Capabilities
- data analysis
- Use cases
- data analysis · research
What genome-analysis says it does
Performs genomics analyses including gene expression profiling, BLAST sequence alignment, GWAS interpretation, variant calling, and genome assembly tasks
Use GATK HaplotypeCaller or DeepVariant for variants; featureCounts or Salmon for transcript quantification.
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| Installs | 16 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Structure and run a genomics analysis workflow from QC through variant calling and interpretation.
Who is it for?
BLAST, RNA-seq differential expression, GWAS, variant calling, and de novo genome assembly.
Skip if: Pure protein structure modeling or drug-target interaction analysis.
When should I use this skill?
The user mentions DNA/RNA sequences, SNPs, gene panels, VCF files, or comparative genomics.
What you get
- variant tables
- differential-expression results
- Manhattan and volcano plots
By the numbers
- 7-step methodology
- 8-item quality checklist
Files
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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