Variant Pathogenicity Predictor

Source: https://github.com/aipoch/medical-research-skills

Variant Pathogenicity Predictor

Integrate REVEL, CADD, PolyPhen and other scores to predict variant pathogenicity.

When to Use

• Use this skill when the task needs Integrate REVEL, CADD, PolyPhen scores to predict variant pathogenicity.
• Use this skill for data analysis 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.

Key Features

• Scope-focused workflow aligned to: Integrate REVEL, CADD, PolyPhen scores to predict variant pathogenicity.
• Packaged executable path(s): scripts/main.py.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Data Analytics/variant-pathogenicity-predictor"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Usage

python scripts/main.py --variant "chr17:43094692:G:A" --gene "BRCA1"
python scripts/main.py --vcf variants.vcf --output report.json

Parameters

• --variant: Variant in format chr:pos:ref:alt
• --vcf: VCF file with variants
• --gene: Gene symbol
• --scores: Prediction scores to use (REVEL,CADD,PolyPhen)

Integrated Scores

• REVEL (Rare Exome Variant Ensemble Learner)
• CADD (Combined Annotation Dependent Depletion)
• PolyPhen-2 (Polymorphism Phenotyping)
• SIFT (Sorting Intolerant From Tolerant)
• MutationTaster

Output

• Pathogenicity classification
• ACMG guideline interpretation
• Individual score breakdown
• Confidence assessment

Risk Assessment