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tooluniverse-variant-functional-annotation

Annotate protein variants with structural, population, deleteriousness, and clinical evidence from genomic databases.

1.7k|254|Updated Mar 3, 2025
Search Tags:#genomics#clinvar#gnomad#variant annotation#missense#cadd#protvar
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-variant-functional-annotation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-variant-functional-annotation
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-variant-functional-annotation
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-variant-functional-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Interpreting the functional impact of a protein variant requires querying many separate databases (ProtVar, gnomAD, CADD, ClinVar, ClinGen) and synthesizing conflicting evidence, which is slow and error-prone when done manually.

Core Features & Use Cases

  • Multi-source variant annotation: Combines ProtVar structural context, gnomAD population frequencies, CADD deleteriousness scores, ClinVar clinical classifications, and ClinGen gene-disease validity into one integrated report.
  • Flexible input handling: Accepts HGVS notation, genomic coordinates, rsIDs, or gene plus protein change, with a one-call FAVOR annotation path for GRCh38 coordinates.
  • Evidence-graded reporting: Produces a structured annotation report with T1-T4 evidence grading and a four-dimension pathogenicity reasoning framework (conservation, location, frequency, prediction).
  • Use Case: Given "TP53 R175H", retrieve its domain and active-site context, gnomAD allele frequencies, CADD PHRED score, ClinVar classification, and ClinGen validity, then synthesize an integrated functional assessment.

Quick Start

Annotate the functional impact of the variant TP53 p.Arg175His including population frequency, deleteriousness scores, and ClinVar classification.

Frequently Asked Questions about tooluniverse-variant-functional-annotation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I annotate the functional impact of a protein variant?▼

Provide the variant as HGVS notation, genomic coordinate, rsID, or gene plus protein change. The workflow queries ProtVar for structural context, gnomAD for population frequency, CADD for deleteriousness, and ClinVar for clinical classification, then synthesizes an integrated report.

What databases are used for variant annotation?▼

The workflow combines ProtVar for protein structure and conservation, gnomAD for allele frequencies, CADD for deleteriousness scores, ClinVar for clinical classifications, ClinGen for gene-disease validity, plus OpenCRAVAT and FAVOR for multi-source annotation.

What is the difference between variant functional annotation and variant interpretation?▼

Functional annotation focuses on protein-level evidence such as structural mapping, residue context, domain impact, and population frequencies. It does not produce full ACMG classifications or treatment recommendations; use the variant-interpretation skill for complete ACMG clinical classification.

Can I annotate a variant using only an rsID or genomic coordinate?▼

Yes, the workflow accepts HGVS coding or protein notation, genomic coordinates in chr:pos:ref:alt format, rsIDs, or gene plus amino acid change. For GRCh38 genomic coordinates, FAVOR provides one-call comprehensive annotation.

What happens when CADD or ClinVar tools are unavailable?▼

Fallback chains handle tool failures: OpenCRAVAT annotators substitute for CADD (cadd_exome), ClinVar (clinvar), and gnomAD (gnomad3). FAVOR provides a single-call alternative returning frequency, deleteriousness, conservation, and ClinVar data for GRCh38 coordinates.

What are the limitations of ProtVar-based variant annotation?▼

ProtVar covers only UniProt canonical isoforms, so alternative isoforms are not mapped. Structural coordinates derive from AlphaFold2 when no experimental structure exists, and CADD scores are computational predictions that do not replace experimental evidence.

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