Variant Analysis

name: tooluniverse-variant-analysis description: Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions. Parses VCF files (streaming, large files), classifies mutation types (missense, nonsense, synonymous, frameshift, splice, intronic, intergenic) and structural variants (deletions, duplications, inversions, translocations), applies VAF/depth/quality/consequence filters, annotates with ClinVar/dbSNP/gnomAD/CADD via ToolUniverse, interprets SV/CNV clinical significance using ClinGen dosage sensitivity scores, computes variant statistics, and generates reports. Solves questions like "What fraction of variants with VAF < 0.3 are missense?", "How many non-reference variants remain after filtering intronic/intergenic?", "What is the pathogenicity of this deletion affecting BRCA1?", or "Which dosage-sensitive genes overlap this CNV?". Use when processing VCF files, annotating variants, filtering by VAF/depth/consequence, classifying mutations, interpreting structural variants, assessing CNV pathogenicity, comparing cohorts, or answering variant analysis questions.

Variant Analysis and Annotation

Production-ready VCF processing and variant annotation skill combining local bioinformatics computation with ToolUniverse database integration. Designed to answer bioinformatics analysis questions about VCF data, mutation classification, variant filtering, and clinical annotation.

When to Use This Skill

Triggers:

• User provides a VCF file (SNV/indel or SV) and asks questions about its contents
• Questions about variant allele frequency (VAF) filtering
• Mutation type classification queries (missense, nonsense, synonymous, etc.)
• Structural variant interpretation requests (deletions, duplications, CNVs)
• Variant annotation requests (ClinVar, gnomAD, CADD, dbSNP)
• CNV pathogenicity assessment using ClinGen dosage sensitivity
• Cohort comparison questions
• Population frequency filtering (SNVs or SVs)
• Intronic/intergenic variant filtering
• Gene dosage sensitivity queries

Example Questions:

• "What fraction of variants with VAF < 0.3 are annotated as missense mutations?"
• "After filtering intronic/intergenic variants, how many non-reference variants remain?"
• "What is the clinical significance of this deletion affecting BRCA1?"
• "Which dosage-sensitive genes overlap this 500kb duplication on chr17?"
• "How many variants have clinical significance annotations?"
• "Compare variant counts between samples"

Core Capabilities

Workflow Overview

Input VCF File (SNVs/indels or SVs)
    |
    v
Phase 1: Parse VCF
    |-- Pure Python parser (any VCF 4.x)
    |-- cyvcf2 parser (faster, C-based)
    |-- Extract: CHROM, POS, REF, ALT, QUAL, FILTER, INFO, FORMAT, samples
    |-- Extract per-sample: GT, VAF, depth
    |-- Extract annotations from INFO (ANN, CSQ, FUNCOTATION)
    |-- Detect variant class: SNV/indel vs SV/CNV
    |
    v
Phase 2: Classify Variants
    |-- Variant type: SNV, INS, DEL, MNV, COMPLEX, SV
    |-- Mutation type: missense, nonsense, synonymous, frameshift, splice, etc.
    |-- Impact: HIGH, MODERATE, LOW, MODIFIER
    |-- SV type: DEL, DUP, INV, BND, CNV (if structural variant)
    |
    v
Phase 3: Apply Filters
    |-- VAF range (min/max)
    |-- Read depth minimum
    |-- Quality threshold
    |-- PASS only
    |-- Variant/mutation type inclusion/exclusion
    |-- Consequence exclusion (intronic, intergenic)
    |-- Population frequency range
    |-- Chromosome selection
    |-- SV size range (for structural variants)
    |
    v
Phase 4: Compute Statistics
    |-- Variant type distribution
    |-- Mutation type distribution
    |-- Impact distribution
    |-- Chromosome distribution
    |-- Ti/Tv ratio (for SNVs)
    |-- Per-sample VAF/depth stats
    |-- Gene mutation counts
    |-- SV size distribution (for structural variants)
    |
    v
Phase 5: Annotate with ToolUniverse (optional)
    |-- MyVariant.info: ClinVar, dbSNP, gnomAD, CADD, SIFT, PolyPhen
    |-- dbSNP: Population frequencies, gene associations
    |-- gnomAD: Population allele frequencies
    |-- Ensembl VEP: Consequence prediction
    |
    v
Phase 6: Generate Report / Answer Question
    |-- Markdown report with tables
    |-- Direct answer to specific question
    |-- DataFrame for downstream analysis
    |
    v
Phase 7: Structural Variant & CNV Analysis (if SV/CNV detected)
    |-- Annotate with gnomAD SV population frequencies
    |-- Query DGVa/dbVar for known SVs (Ensembl)
    |-- Identify affected genes
    |-- Query ClinGen dosage sensitivity (HI/TS scores)
    |-- Classify pathogenicity (Pathogenic/Likely Pathogenic/VUS/Benign)
    |-- Generate SV clinical report with ACMG/ClinGen guidelines

Phase Summaries

Phase 1: VCF Parsing

Use pandas for:

• Reading VCF as structured data
• Quick exploratory analysis
• When you need to manipulate columns and rows

Use python_implementation tools for:

• Production parsing with annotation extraction
• Multi-sample VCF handling
• VAF extraction from FORMAT fields
• Large file streaming

Key functions:

vcf_data = parse_vcf("input.vcf")           # Pure Python (always works)
vcf_data = parse_vcf_cyvcf2("input.vcf")    # Fast C-based (if installed)
df = variants_to_dataframe(vcf_data.variants, sample="TUMOR")  # For pandas

Phase 2: Variant Classification

Automatic classification from annotations:

• SnpEff ANN field
• VEP CSQ field
• GATK Funcotator FUNCOTATION field
• Standard INFO keys: EFFECT, EFF, TYPE

Mutation types supported: missense, nonsense, synonymous, frameshift, splice_site, splice_region, inframe_insertion, inframe_deletion, intronic, intergenic, UTR_5, UTR_3, upstream, downstream, stop_lost, start_lost

See references/mutation_classification_guide.md for full details

Phase 3: Filtering

Common filtering patterns:

# Somatic-like variants
criteria = FilterCriteria(
    min_vaf=0.05, max_vaf=0.95,
    min_depth=20, pass_only=True,
    exclude_consequences=["intronic", "intergenic", "upstream", "downstream"]
)

# High-confidence germline
criteria = FilterCriteria(
    min_vaf=0.25, min_depth=30, pass_only=True,
    chromosomes=["1", "2", ..., "22", "X", "Y"]
)

# Rare pathogenic candidates
criteria = FilterCriteria(
    min_depth=20, pass_only=True,
    mutation_types=["missense", "nonsense", "frameshift"]
)

See references/vcf_filtering.md for all filter options

Phase 4: Statistics

Use pandas for:

• Complex aggregations (groupby, pivot tables)
• Custom statistical tests
• Data exploration

Use python_implementation for:

• Standard variant statistics (Ti/Tv, type distribution)
• Per-sample VAF/depth summary
• Quick mutation type counts

Phase 5: ToolUniverse Annotation

When to use ToolUniverse annotation tools:

1. ClinVar clinical significance: Use MyVariant.info or dbSNP tools
2. Population frequencies: Use MyVariant.info (aggregates gnomAD, ExAC, 1000G)
3. Pathogenicity scores: Use MyVariant.info (aggregates CADD, SIFT, PolyPhen)
4. Consequence prediction: Use Ensembl VEP tools

Best practices:

• Annotate variants with rsIDs first (most reliable)
• Use MyVariant.info for batch annotation (aggregates multiple sources)
• Limit to top variants (max_annotate=50-100) to respect rate limits
• Query dbSNP/gnomAD directly for specific use cases

Key tools:

• MyVariant_query_variants: Batch annotation (ClinVar, dbSNP, gnomAD, CADD)
• dbsnp_get_variant_by_rsid: Population frequencies
• gnomad_get_variant: Basic variant metadata
• EnsemblVEP_annotate_rsid: Consequence prediction

See references/annotation_guide.md for detailed examples

Phase 6: Report Generation

Report includes:

1. Summary Statistics (total variants, type counts, Ti/Tv)
2. Mutation Type Distribution (table with counts and percentages)
3. Impact Distribution
4. Chromosome Distribution
5. VAF Distribution (per-sample)
6. Clinical Significance
7. Top Mutated Genes
8. Variant Annotations (ClinVar-annotated variants)

Phase 7: Structural Variant & CNV Analysis

When VCF contains SV calls (SVTYPE=DEL/DUP/INV/BND):

1. Identify affected genes (from VCF annotation or coordinate overlap)
2. Query ClinGen dosage sensitivity:

   clingen = ClinGen_dosage_by_gene(gene_symbol="BRCA1")
   # Returns: haploinsufficiency_score, triplosensitivity_score

3. Check population frequency:

   gnomad_sv = gnomad_get_sv_by_gene(gene_symbol="BRCA1")
   # Returns: SVs with AF, AC, AN

4. Classify pathogenicity:
   • Pathogenic: Deletion + HI score = 3, AF < 0.0001
   • Likely Pathogenic: Deletion + HI score = 2, AF < 0.001
   • VUS: HI/TS score = 0-1, AF 0.001-0.01
   • Benign: AF > 0.01

ClinGen dosage score interpretation:

• 3: Sufficient evidence for dosage pathogenicity (HIGH impact)
• 2: Some evidence (MODERATE impact)
• 1: Little evidence (LOW impact)
• 0: No evidence (MINIMAL impact)
• 40: Dosage sensitivity unlikely

See references/sv_cnv_analysis.md for full SV workflow