backend/pipeline/variant_caller.py

"""
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=<ID=DP,Number=1,Type=Integer,Description=\"Total Depth\">\n")
            f.write("##INFO=<ID=AF,Number=A,Type=Float,Description=\"Allele Frequency\">\n")
            f.write("##INFO=<ID=GENE,Number=1,Type=String,Description=\"Gene Name\">\n")
            f.write("##INFO=<ID=CONS,Number=1,Type=String,Description=\"Consequence\">\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