""" Variant Calling Pipeline Process sequencing data to identify genetic variants """ from pathlib import Path from typing import Dict, List, Optional import yaml import logging from dataclasses import dataclass logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @dataclass class Variant: """Represents a genetic variant""" chromosome: str position: int reference: str alternate: str quality: float depth: int allele_frequency: float gene: Optional[str] = None consequence: Optional[str] = None class VariantCaller: """Call variants from sequencing data""" def __init__(self, config_path: str = "config.yml"): with open(config_path, 'r') as f: self.config = yaml.safe_load(f)['pipeline']['variant_calling'] self.min_coverage = self.config['min_coverage'] self.min_allele_frequency = self.config['min_allele_frequency'] self.output_dir = Path(self.config['output_dir']) self.output_dir.mkdir(parents=True, exist_ok=True) def call_variants( self, alignment_file: Path, reference_genome: Path, output_vcf: Optional[Path] = None ) -> Path: """ Call variants from aligned sequencing data Args: alignment_file: BAM/SAM alignment file reference_genome: Reference genome FASTA output_vcf: Output VCF file Returns: Path to VCF file """ if output_vcf is None: output_vcf = self.output_dir / f"{alignment_file.stem}_variants.vcf" logger.info(f"Calling variants from {alignment_file.name}") # Simulate variant calling for demo # In production, use tools like GATK, FreeBayes, or BCFtools variants = self._simulate_variant_calling() # Write VCF self._write_vcf(variants, output_vcf) logger.info(f"Identified {len(variants)} variants") return output_vcf def _simulate_variant_calling(self) -> List[Variant]: """Simulate variant calling for demo purposes""" # Common cancer-associated variants variants = [ Variant('chr17', 7577538, 'C', 'T', 35.2, 50, 0.45, 'TP53', 'missense'), Variant('chr7', 140453136, 'A', 'T', 42.1, 65, 0.52, 'BRAF', 'missense'), Variant('chr13', 32914438, 'T', 'C', 38.7, 55, 0.48, 'BRCA2', 'missense'), Variant('chr17', 41244936, 'G', 'A', 40.3, 60, 0.50, 'BRCA1', 'missense'), Variant('chr3', 178936091, 'G', 'A', 33.5, 48, 0.43, 'PIK3CA', 'missense'), Variant('chr9', 133748283, 'T', 'G', 37.9, 52, 0.46, 'ABL1', 'missense'), Variant('chr12', 25398284, 'C', 'T', 39.4, 58, 0.49, 'KRAS', 'missense'), ] return variants def _write_vcf(self, variants: List[Variant], output_file: Path): """Write variants to VCF format""" with open(output_file, 'w') as f: # VCF header f.write("##fileformat=VCFv4.2\n") f.write("##source=CancerAtHomeVariantCaller\n") f.write("##INFO=\n") f.write("##INFO=\n") f.write("##INFO=\n") f.write("##INFO=\n") f.write("#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\n") # Variant records for v in variants: info = f"DP={v.depth};AF={v.allele_frequency:.3f}" if v.gene: info += f";GENE={v.gene}" if v.consequence: info += f";CONS={v.consequence}" filter_status = "PASS" if v.depth >= self.min_coverage and v.allele_frequency >= self.min_allele_frequency else "LowQual" f.write(f"{v.chromosome}\t{v.position}\t.\t{v.reference}\t{v.alternate}\t{v.quality:.1f}\t{filter_status}\t{info}\n") def filter_variants( self, vcf_file: Path, min_quality: float = 30.0 ) -> List[Variant]: """Filter variants by quality metrics""" variants = [] try: with open(vcf_file, 'r') as f: for line in f: if line.startswith('#'): continue fields = line.strip().split('\t') if len(fields) < 8: continue quality = float(fields[5]) if quality < min_quality: continue # Parse INFO field info = dict(item.split('=') for item in fields[7].split(';') if '=' in item) variant = Variant( chromosome=fields[0], position=int(fields[1]), reference=fields[3], alternate=fields[4], quality=quality, depth=int(info.get('DP', 0)), allele_frequency=float(info.get('AF', 0)), gene=info.get('GENE'), consequence=info.get('CONS') ) variants.append(variant) logger.info(f"Filtered to {len(variants)} high-quality variants") return variants except Exception as e: logger.error(f"Error filtering variants: {e}") return [] def annotate_variants(self, variants: List[Variant]) -> List[Variant]: """ Annotate variants with functional information In production, integrate with tools like: - ANNOVAR - VEP (Variant Effect Predictor) - SnpEff """ # Simulated annotation for variant in variants: if not variant.gene: variant.gene = "UNKNOWN" if not variant.consequence: variant.consequence = "unknown" return variants class VariantAnalyzer: """Analyze and interpret variants""" def __init__(self): self.caller = VariantCaller() def identify_cancer_variants(self, variants: List[Variant]) -> List[Variant]: """Identify known cancer-associated variants""" # Common cancer genes cancer_genes = { 'TP53', 'BRCA1', 'BRCA2', 'KRAS', 'EGFR', 'BRAF', 'PIK3CA', 'APC', 'PTEN', 'MYC', 'RB1', 'CDKN2A' } cancer_variants = [ v for v in variants if v.gene and v.gene in cancer_genes ] logger.info(f"Found {len(cancer_variants)} cancer-associated variants") return cancer_variants def calculate_mutation_burden(self, variants: List[Variant]) -> float: """Calculate tumor mutation burden (TMB)""" # TMB = number of somatic mutations per megabase coding_variants = [v for v in variants if v.consequence in ['missense', 'nonsense', 'frameshift']] # Assume exome size of ~30 Mb exome_size_mb = 30 tmb = len(coding_variants) / exome_size_mb logger.info(f"Tumor Mutation Burden: {tmb:.2f} mutations/Mb") return tmb