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
- 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).
- Data ingestion and QC - Check raw data quality (FastQC metrics, read depth, coverage). Flag low-quality samples before proceeding.
- 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).
- Variant calling / Expression quantification - Use GATK HaplotypeCaller or DeepVariant for variants; featureCounts or Salmon for transcript quantification.
- Statistical analysis - Apply appropriate multiple-testing correction (Bonferroni, BH-FDR). For GWAS, use mixed models (BOLT-LMM, SAIGE) to handle population structure.
- Annotation and interpretation - Annotate variants with VEP/ANNOVAR; enrich gene lists with GO, KEGG, Reactome pathways.
- 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
