Genome Analysis

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