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).