bio-clinical-databases-variant-prioritization
Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinic
npx skills add BioTender-max/awesome-bio-agent-skills --skill variant-prioritization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Prioritizes rare-disease variants from trio/quad WES/WGS using de novo calls, compound-heterozygous phasing, mosaic VAF tiering, phenotype-driven ranking, ClinGen validity gating, and ACMG SF v3.2 secondary findings reporting.
How it works
- Pipeline components are Python-based (tool_type: python) with primary tool pandas.
- Stage 1: Quality control and population frequency filtering to reduce raw variants (e.g., 100k-150k raw to ~5-15k).
- Stage 2: De novo candidate calling using DeNovoGear, Triodenovo, GATK PossibleDeNovo; supplement with DeNovoCNN as of 2024-2026; apply Mendelian-violation logic and IGV inspection guidance.
- Stage 3: Compound-heterozygous candidate detection via trio phasing or WhatsHap read-based phasing (within ~500 bp; longer needs parents or long-read).
- Stage 4: ACMG SF v3.2 flagging for reportable secondary findings based on a predefined gene list and ClinGen criteria.
- Stage 5: ClinGen gene-disease validity gating (Possible outputs: Definitive/Strong/Moderate/Limited/Disputed/No Known Disease Relationship) with filtering rules.
- Stage 6: Phenotype-driven prioritization via Exomiser, Phen2Gene, AMELIE (and others) using HPO terms; produce an Exomiser command for YML/VCF input.
- Stage 7: Optional ACMG/AMP classification integration through acmg-classification for final tiering.
- Console/CLI commands include:
exomiser --analysis hiPHIVE-prioritised.yml, as well as Python-based filtering and phasing steps shown in code blocks.
When to use it
Use when running diagnostic exome/genome pipelines to identify candidate Mendelian disease genes, screen incidental findings, or audit VUS reclassification cycles. It relies on high-quality HPO input for accurate phenotype-driven prioritization.
What it can touch
- Python environment with pandas and cyvcf2 for VCF handling; Python code references DeNovoGear, Triodenovo, GATK PossibleDeNovo, WhatsHap, Exomiser, Phen2Gene, AMELIE, Phenolyzer, and acmg-classification.
- Exomiser CLI for phenotype-driven ranking:
exomiser --analysis hiPHIVE-prioritised.yml. - ACMG SF v3.2 gene set constants as shown in the script (ACMG_SF_V3_2_GENES).
Caveats
- Version compatibility notes specify pandas 2.2+, cyvcf2 0.30+, pyhgvs 0.12+, Exomiser 14.0+, Phen2Gene 1.2+, DeNovoGear 1.1.1+, WhatsHap 2.0+, HPO 2024+. If code throws ImportError, AttributeError, or TypeError, adapt API usage accordingly.
- Phenotype-driven methods degrade with sparse HPO terms; requires 5-10 specific HPO terms rather than generic annotations.
- False-positive risk in De Novo calling (~10-30%) without IGV inspection, due to calls in repetitive regions, low parental coverage, or mosaicism.
- Compound-het results depend on accurate phasing; without trio data, reliance on WhatsHap read-based phasing may still produce false positives if not properly phased.
## Version Compatibility Reference examples tested with: pandas 2.2+, cyvcf2 0.30+, pyhgvs 0.12+, Exomiser 14.0+ (Smedley 2015), Phen2Gene 1.2+ (Zhao 2020), DeNovoGear 1.1.1+ (Ramu 2013), WhatsHap 2.0+ (Patterson 2015), HPO 2024+ (Human Phenotype Ontology). ACMG Secondary Findings list is v3.2 (Miller 2023): 81 genes. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. Phenotype-driven prioritization REQUIRES high-quality HPO terms; without rich phenotypic input Exomiser/AMELIE degrade significantly. # Rare-Disease Variant Prioritization Pipeline **'Prioritize candidate disease-causing variants from this trio exome'** -> Filter to rare + functional + inheritance-consistent variants; rank by phenotype concordance; flag ACMG SF v3.2 incidental findings; report tiers with classification logic deferred to `acmg-classification`. - Python (filtering pipeline): pandas + cyvcf2 + myvariant.info aggreg
- Version Compatibility
- Pipeline Architecture: The Standard Rare-Disease Funnel
- Inheritance-Based Filtering
- De Novo Calling: Trio Analysis
- Phenotype-Driven Prioritization
- ClinGen Gene-Disease Validity: Mandatory Gating
- ACMG Secondary Findings v3.2 (Miller 2023 Genet Med 25:100866)
- Decision Tree by Scenario
- Standard Pipeline Workflow
- Per-Operation Failure Modes
- Reconciliation: When Sources Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
What does the bio-clinical-databases-variant-prioritization skill do?
Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinic
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill variant-prioritization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.