Genomic Variant Impact Assessment & Interpretation
Assess functional impact and clinical significance of genomic variants
What You Can Do
You can analyze annotated variants from VCF files and prediction tools (VEP, SnpEff, InterVar) to assess pathogenicity, predict functional consequences, and cross-reference multiple annotation sources including ACMG guidelines, CADD scores, and splicing predictions. Claude helps you generate reproducible variant interpretation reports that bridge computational outputs with biological and clinical significance for disease studies, pharmacogenomics, and regulatory submissions.
Features
rank variants by predicted impact (loss-of-function, missense, regulatory) using SIFT, PolyPhen-2, and conservation metrics
cross-reference ACMG pathogenicity criteria, ClinVar, gnomAD population frequencies, and disease-specific databases in a unified framework
assess splicing effects (SpliceAI), protein folding (AlphaFold), and domain disruption for novel variants
connect variants to published evidence linking genotype to phenotype for genes in your panel
generate structured reports with criteria, evidence trails, and confidence assessments for wet lab collaboration and publication
evaluate drug-metabolizer phenotypes and variant-drug interaction significance
build and apply custom criteria for targeted gene panels, cancer hotspots, or monogenic disease genes
Example Output
Example 1: Missense Variant in TP53
- Variant: chr17:7577121 C>T (p.R248Q)
- Predicted Impact: Loss of function (missense in DNA-binding domain)
- ACMG Classification: Likely Pathogenic (PVS1, PS1, PM2_supporting)
- Evidence: Known hotspot in IARC TP53 database; 47 ClinVar submissions as pathogenic; gnomAD frequency <0.0001
- Functional Prediction: PolyPhen-2 = 0.95 (probably damaging); SIFT score = deleterious
- Recommendation: HIGH PRIORITY — strong evidence for functional disruption in cancer context
Example 2: Synonymous Variant in BRCA1 Exon 5
- Variant: chr17:41196312 G>A (p.Asp222=)
- Predicted Impact: Potential splicing disruption
- SpliceAI Score: 0.82 (high confidence for exon skipping)
- Functional Prediction: MaxEntScan indicates 65% reduction in splice site strength
- gnomAD Frequency: 0.0008 (present in population)
- Recommendation: MODERATE PRIORITY — investigate RNA-level consequences; consider functional validation
Example 3: Intergenic Variant Near PTEN Promoter
- Variant: chr10:89654321 A>G
- Predicted Impact: Potential regulatory disruption
- Conservation: phyloP score = 4.2 (moderately conserved across mammals)
- TF Binding Prediction: TRANSFAC indicates altered TP53 binding site
- ClinVar: No submissions; gnomAD frequency = 0.001
- Recommendation: LOW-MODERATE PRIORITY — functional validation recommended before clinical classification
What's Included
- SKILL.md: Core instruction file for variant interpretation workflow and ACMG criteria integration
- Variant Interpretation Template: Structured markdown template for documenting variant assessment with evidence fields
- ACMG Pathogenicity Checklist: Interactive checklist for applying American College of Medical Genetics criteria (PVS, PS, PM, BP, BS categories)
- Annotation Source Reference Guide: Quick-reference table mapping prediction tools (CADD, SpliceAI, PolyPhen-2, SIFT, AlphaFold) to interpretive thresholds
- Literature Integration Workflow: Framework for cross-referencing ClinVar, IARC, dbSNP, and PubMed to support pathogenicity calls
Who It's For
- Bioinformatics scientists — systematize variant interpretation pipelines and standardize reporting across projects
- Computational biologists — prioritize variants for functional validation and reduce time spent on literature manual review
- Clinical geneticists — document evidence trails for rare disease diagnoses and regulatory submissions
- Pharmacogenomics specialists — assess drug-metabolizer phenotypes and predict variant-drug interactions
- Research scientists in oncology — interpret cancer-associated variants and identify driver mutations in tumor panels
Best For
- Variant prioritization from whole-exome or whole-genome sequencing studies
- Pathogenicity assessment for novel variants in monogenic disease genes
- Pharmacogenomics interpretation and drug-metabolizer phenotyping
- Cancer variant annotation and driver mutation identification
- Preparation of variant interpretation reports for publication, patents, or regulatory submissions
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