Variant Annotation

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

Variant Annotation

Query and interpret gene variant clinical significance from ClinVar and dbSNP databases with ACMG guideline support.

When to Use

• Use this skill when the task needs Query and annotate gene variants from ClinVar and dbSNP databases. .
• Use this skill for evidence insight 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: Query and annotate gene variants from ClinVar and dbSNP databases. \n.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• 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.
• dataclasses: unspecified. Declared in requirements.txt.

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Evidence Insight/variant-annotation"
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.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• 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.

Purpose

Provide comprehensive variant annotation including:

• Clinical significance classification (Pathogenic, Likely Pathogenic, VUS, Likely Benign, Benign)
• ACMG guideline-based pathogenicity assessment
• Population allele frequencies (gnomAD, ExAC, 1000 Genomes)
• Disease and phenotype associations
• Functional predictions (SIFT, PolyPhen, CADD)

Supported Input Formats

Usage

Python API

from scripts.main import VariantAnnotator

# Initialize annotator
annotator = VariantAnnotator()

# Query by rsID
result = annotator.query_variant("rs80357410")

# Query by HGVS notation
result = annotator.query_variant("NM_007294.3:c.5096G>A")

# Query by genomic coordinate
result = annotator.query_variant("chr17:43094692:G>A")

# Batch query
results = annotator.batch_query(["rs80357410", "rs28897696", "rs11571658"])

Command Line

# Single variant query
python scripts/main.py --variant rs80357410

# HGVS notation
python scripts/main.py --variant "NM_007294.3:c.5096G>A"

# Genomic coordinate
python scripts/main.py --variant "chr17:43094692:G>A"

# Batch from file
python scripts/main.py --file variants.txt --output results.json

# With output format
python scripts/main.py --variant rs80357410 --format json

Output Format

{
  "variant_id": "rs80357410",
  "gene": "BRCA1",
  "chromosome": "17",
  "position": 43094692,
  "ref_allele": "G",
  "alt_allele": "A",
  "hgvs_genomic": "NC_000017.11:g.43094692G>A",
  "hgvs_cdna": "NM_007294.3:c.5096G>A",
  "hgvs_protein": "NP_009225.1:p.Arg1699Gln",
  
  "clinical_significance": {
    "clinvar": "Pathogenic",
    "acmg_classification": "Pathogenic",
    "acmg_criteria": ["PS4", "PM1", "PM2", "PP2", "PP3", "PP5"],
    "acmg_score": 13.0,
    "review_status": "criteria provided, multiple submitters, no conflicts"
  },
  
  "disease_associations": [
    {
      "disease": "Breast-ovarian cancer, familial 1",
      "medgen_id": "C2676676",
      "significance": "Pathogenic"
    }
  ],
  
  "population_frequencies": {
    "gnomAD_genome_all": 0.000008,
    "gnomAD_exome_all": 0.000012,
    "1000G_all": 0.0
  },
  
  "functional_predictions": {
    "sift": "deleterious",
    "polyphen2": "probably_damaging",
    "cadd_score": 24.5,
    "mutation_taster": "disease_causing"
  },
  
  "literature_count": 42,
  "last_evaluated": "2023-12-15",
  
  "interpretation_summary": "This variant (BRCA1 p.Arg1699Gln) is classified as Pathogenic based on ACMG guidelines. It shows strong evidence of pathogenicity including population data (extremely rare), computational predictions (deleterious), and strong clinical significance (established association with hereditary breast-ovarian cancer)."
}

ACMG Classification Criteria

The annotator implements the ACMG/AMP guidelines for variant interpretation:

Pathogenic Evidence (Score)

• PVS1 (8.0): Null variant in a gene where LOF is known mechanism
• PS1 (4.0): Same amino acid change as known pathogenic
• PS2 (4.0): De novo with confirmed paternity/maternity
• PS3 (4.0): Well-established functional studies show damaging effect
• PS4 (4.0): Prevalence in affected > controls
• PM1 (2.0): Located in critical functional domain
• PM2 (2.0): Absent from controls (MAF <0.0001)
• PM3 (2.0): AR disorder, detected in trans with pathogenic
• PM4 (2.0): Protein length changing
• PM5 (2.0): Novel missense at same position as known pathogenic
• PM6 (2.0): Assumed de novo without confirmation
• PP1 (1.0): Cosegregation with disease
• PP2 (1.0): Missense in gene with low benign rate
• PP3 (1.0): Multiple computational evidence support
• PP4 (1.0): Phenotype/patient history matches gene
• PP5 (1.0): Reputable source reports pathogenic

Benign Evidence

• BA1 (-8.0): MAF >5% in population
• BS1 (-4.0): MAF >expected for disorder
• BS2 (-4.0): Observed in healthy adult
• BS3 (-4.0): Functional studies show no damage
• BS4 (-4.0): Lack of cosegregation
• BP1 (-1.0): Missense in gene where truncating are pathogenic
• BP2 (-1.0): Observed in trans with pathogenic
• BP3 (-1.0): In-frame indel in repetitive region
• BP4 (-1.0): Multiple computational evidence benign
• BP5 (-1.0): Alternate cause found
• BP6 (-1.0): Reputable source reports benign
• BP7 (-1.0): Synonymous with no splicing impact

Classification Thresholds

Technical Difficulty: HIGH

⚠️ AI independent acceptance status: manual inspection required This skill requires:

• NCBI E-utilities API integration (ClinVar, dbSNP)
• HGVS notation parsing and validation
• VCF format handling
• ACMG guideline implementation
• Multiple prediction algorithm integration
• Complex data transformation and scoring

Data Sources