Data & ReproClinical MedicineFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction
BI

bio-clinical-databases-variant-prioritization

Maintainer FreedomIntelligence · Last updated April 1, 2026

Filter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.

OpenClawNanoClawAnalysisReproductionbio-clinical-databases-variant-prioritization🧬 bioinformatics (gptomics bio-* suite)bioinformatics — clinical databases & variant analysisfilter

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-clinical-databases-variant-prioritization

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Python: pandas for multi-criteria filtering with ACMG/AMP classification logic.
  • Prioritize candidate disease variants from my exome data" → Filter and rank variants by pathogenicity scores, population frequency, inheritance pattern, and clinical evidence to identify candidate disease-causing mutations. Python: pandas for multi-criteria filtering with ACMG/AMP classification logic.
  • Goal: Filter variants to retain rare, potentially pathogenic candidates for rare disease analysis.
  • Approach: Apply gnomAD population frequency and ClinVar significance filters, retaining pathogenic, VUS, and unannotated variants.

Source Doc

Excerpt From SKILL.md

ACMG-Style Filtering

Goal: Score variants using ACMG-style evidence criteria for pathogenicity assessment.

Approach: Evaluate PM2 (population rarity) and PVS1 (loss-of-function) evidence, then compute a weighted priority score.

Multi-Database Prioritization

Goal: Prioritize variants using aggregated evidence from ClinVar, gnomAD, CADD, and REVEL in a single query.

Approach: Fetch annotations via myvariant.info, then compute a composite priority score weighting clinical, population, and computational evidence.

Inheritance-Based Filtering

Goal: Filter variants by expected inheritance pattern (autosomal dominant, recessive, or X-linked).

Approach: Select heterozygous ultra-rare variants for AD, or homozygous plus compound heterozygous candidates for AR.

Use cases

  • Use when identifying candidate disease-causing variants from exome or genome sequencing.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Upstream Related Skills

  • clinvar-lookup - ClinVar pathogenicity queries
  • gnomad-frequencies - Population frequency filtering
  • variant-calling/clinical-interpretation - ACMG classification
  • variant-calling/filtering-best-practices - Quality filtering

Related skills

Related skills

Back to directory
AR
Data & ReproClinical Medicine

armored-cart-design-agent

Design armored CAR-T cells with cytokine payloads and resistance mechanisms.

OpenClawNanoClawAnalysis
FreedomIntelligence/OpenClaw-Medical-SkillsView
AR
Data & ReproClinical Medicine

arxiv-search

Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.

OpenClawNanoClawAnalysis
FreedomIntelligence/OpenClaw-Medical-SkillsView
AU
Data & ReproClinical Medicine

autonomous-oncology-agent

Autonomous oncology research agent: literature mining, trial matching, biomarker analysis, and treatment hypothesis generation.

OpenClawNanoClawAnalysis
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & ReproClinical Medicine

bio-cfdna-preprocessing

Preprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate remo…

OpenClawNanoClawAnalysis
FreedomIntelligence/OpenClaw-Medical-SkillsView