domains / families / science / variant-annotation

variant-annotation

combined

Annotates VCF variants with functional consequences, population frequencies, and pathogenicity scores using bcftools annotate/csq, Ensembl VEP, SnpEff, and ANNOVAR. Use when deciding which annotation engine and version to pin, which transcript set to report on (RefSeq vs Ensembl vs MANE Select/Plus Clinical, and why VEP --pick is dangerous clinically), how to reconcile HGVS 3'-shifting with VCF left-alignment, which consequence plus NMD status governs PVS1 eligibility, which single calibrated predictor to use for PP3/BP4 (REVEL, AlphaMissense, CADD, SpliceAI deltas), or how to read gnomAD v2/v3/v4 grpmax filtering allele frequency instead of one global AF cutoff. Not for ACMG combining rules or final classification (see variant-calling/clinical-interpretation). Query and annotate gene variants from ClinVar and dbSNP databases. \n\.

When to use

• Use this skill when the task needs Query and annotate gene variants from ClinVar and dbSNP databases.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.
• Clinical significance classification (Pathogenic, Likely Pathogenic, VUS, Likely Benign, Benign)
• ACMG guideline-based pathogenicity assessment
• Population allele frequencies (gnomAD, ExAC, 1000 Genomes)
• Disease and phenotype associations
• Functional predictions (SIFT, PolyPhen, CADD)

When not to use

• Requires internet connection for database queries
• NCBI API rate limits: 3 requests/second (API key increases to 10/sec)
• Some variants may not be present in ClinVar (VUS without clinical data)
• HGVS notation parsing may fail for complex variants
• Population frequencies not available for all variants
• Functional predictions are computational estimates only

Skill page

Conflict

The three skills cover the same high-level task but operate at different levels of abstraction and tooling. The first (aipoch) is a high-level wrapper for a Python script that queries ClinVar/dbSNP for clinical significance. The second (FreedomIntelligence) is a practical guide for using command-line tools (bcftools, VEP, SnpEff, ANNOVAR) to add annotations to VCF files. The third (GPTomics) is a deep-dive into the theoretical and practical pitfalls of variant annotation, focusing on reproducibility, transcript selection (MANE vs RefSeq), and normalization issues. No single copy contains the full procedure; the first lacks the CLI tooling details, the second lacks the deep theoretical context and specific clinical reporting standards (MANE), and the third lacks the simple 'how-to' commands for basic annotation. Combining them provides a complete workflow from basic annotation to clinical-grade reproducibility.