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bio-clinical-databases-variant-prioritization

Prioritize disease-causing variants from annotated variant lists using ClinVar, gnomAD, CADD, and REVEL scores.

Updated Aug 27, 2026
Search Tags:#genomics#pandas#clinvar#acmg#gnomad#variant-prioritization
One-click install
npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-clinical-databases-variant-prioritization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-clinical-databases-variant-prioritization
Source: https://github.com/stellaromics/fast-bioinfo/tree/main/.claude/agents/spatial-analysis/skills/bio-clinical-databases-variant-prioritization
Command: npx skills add https://github.com/stellaromics/fast-bioinfo --skill bio-clinical-databases-variant-prioritization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps researchers quickly prioritize disease-causing genetic variants from sequencing data by integrating pathogenicity, population frequency, and clinical evidence.

Core Features & Use Cases

  • Multi-criteria prioritization using ClinVar, gnomAD, CADD, and REVEL scores.
  • ACMG-style filtering and inheritance-based prioritization (AD/AR/XL) to rank and select candidate variants.
  • Generate ranked variant lists and ready-to-interpret reports for clinical and research workflows.

Quick Start

Prioritize variants from my exome sequencing data using ClinVar, gnomAD, CADD, and REVEL annotations.

Frequently Asked Questions about bio-clinical-databases-variant-prioritization

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

FAQPage Schema
How do I prioritize disease-causing variants from exome sequencing data?▼

Prioritize disease-causing variants by integrating pathogenicity scores from CADD and REVEL with population frequency data from gnomAD and clinical evidence from ClinVar to rank candidate variants.

What is the best way to filter genetic variants using ACMG criteria and inheritance patterns?▼

Filter genetic variants by applying ACMG-style criteria alongside inheritance-based prioritization for autosomal dominant, autosomal recessive, and X-linked patterns to select and rank candidate variants.

Can I use Python and pandas to rank variants from tabular annotation files?▼

Yes, you can use Python and pandas to process tabular variant annotation inputs, applying multi-criteria filtering to generate a ranked variant table with priority scores and tiers.

Do I need pre-annotated variant lists from ClinVar and gnomAD before prioritization?▼

Yes, variant prioritization requires pre-annotated tabular inputs containing standard annotations from ClinVar, gnomAD, CADD, and REVEL to accurately rank disease-causing candidates.

How does integrating CADD and REVEL scores improve variant prioritization?▼

Integrating CADD and REVEL scores improves variant prioritization by combining computational pathogenicity predictions with clinical evidence and population frequency to produce ranked variant lists.

What limitations should I consider when applying inheritance-based filtering to variant lists?▼

Inheritance-based filtering limitations depend on the completeness of your tabular annotations; incomplete pedigree data or missing standard annotations from ClinVar, gnomAD, CADD, or REVEL may reduce prioritization accuracy.

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