#!/usr/bin/env python3
"""
Core Variant Annotator - Functional annotation engine
"""
from typing import Dict, List, Optional
import random
[docs]
class VariantAnnotator:
"""
Core variant annotation engine
Provides functional annotations for genomic variants including
gene symbols, consequences, and pathogenicity scores.
"""
[docs]
def __init__(self, genome: str = "hg38"):
"""
Initialize variant annotator
Args:
genome: Reference genome version
"""
self.genome = genome
# Mock gene database (in real implementation, this would be loaded from files)
self.gene_regions = {
"1": [("BRCA1", 43044295, 43125483), ("TP53", 7565097, 7590856)],
"2": [("BRCA2", 32315086, 32400266), ("MSH2", 47630108, 47710367)],
"17": [("TP53", 7565097, 7590856), ("BRCA1", 43044295, 43125483)],
# Add more chromosomes and genes as needed
}
# Mock consequence types
self.consequence_types = [
"missense_variant", "synonymous_variant", "stop_gained",
"stop_lost", "frameshift_variant", "inframe_deletion",
"inframe_insertion", "splice_donor_variant", "splice_acceptor_variant",
"intron_variant", "5_prime_UTR_variant", "3_prime_UTR_variant",
"upstream_gene_variant", "downstream_gene_variant", "intergenic_variant"
]
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def get_functional_annotations(self, variant: Dict) -> Dict:
"""
Get functional annotations for a variant
Args:
variant: Variant dictionary with CHROM, POS, REF, ALT
Returns:
Dictionary with functional annotations
"""
annotations = {}
# Find overlapping gene
gene_symbol = self._find_overlapping_gene(variant)
if gene_symbol:
annotations['gene_symbol'] = gene_symbol
# Predict consequence type
consequence = self._predict_consequence(variant, gene_symbol)
annotations['consequence'] = consequence
# Calculate mock CADD score
cadd_score = self._calculate_cadd_score(variant)
annotations['cadd_score'] = cadd_score
# Add transcript information
annotations['transcript_id'] = f"ENST{random.randint(100000, 999999)}"
annotations['protein_position'] = random.randint(1, 500)
else:
annotations['gene_symbol'] = "intergenic"
annotations['consequence'] = "intergenic_variant"
annotations['cadd_score'] = random.uniform(0.1, 5.0)
return annotations
def _find_overlapping_gene(self, variant: Dict) -> Optional[str]:
"""Find gene that overlaps with variant position"""
chrom = variant['CHROM']
pos = variant['POS']
if chrom in self.gene_regions:
for gene_name, start, end in self.gene_regions[chrom]:
if start <= pos <= end:
return gene_name
return None
def _predict_consequence(self, variant: Dict, gene_symbol: str) -> str:
"""Predict variant consequence type"""
# Simple mock prediction based on variant type
variant_type = variant.get('variant_type', 'SNV')
if variant_type == 'SNV':
# For SNVs, randomly assign consequence with realistic probabilities
consequences = [
("missense_variant", 0.4),
("synonymous_variant", 0.3),
("stop_gained", 0.05),
("splice_donor_variant", 0.02),
("splice_acceptor_variant", 0.02),
("intron_variant", 0.21)
]
elif variant_type == 'DEL':
consequences = [
("frameshift_variant", 0.4),
("inframe_deletion", 0.3),
("intron_variant", 0.3)
]
elif variant_type == 'INS':
consequences = [
("frameshift_variant", 0.4),
("inframe_insertion", 0.3),
("intron_variant", 0.3)
]
else:
consequences = [("intron_variant", 1.0)]
# Weighted random selection
rand_val = random.random()
cumulative = 0
for consequence, prob in consequences:
cumulative += prob
if rand_val <= cumulative:
return consequence
return consequences[0][0] # fallback
def _calculate_cadd_score(self, variant: Dict) -> float:
"""Calculate mock CADD pathogenicity score"""
# Mock CADD score calculation
# Real implementation would use pre-computed CADD scores
base_score = random.uniform(0.1, 30.0)
# Adjust based on variant type
variant_type = variant.get('variant_type', 'SNV')
if variant_type in ['DEL', 'INS']:
base_score += random.uniform(2.0, 10.0)
# Adjust based on chromosome (some chromosomes have higher scores)
if variant['CHROM'] in ['17', '13', 'X']:
base_score += random.uniform(1.0, 5.0)
return round(min(base_score, 99.0), 2)
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def annotate_batch(self, variants: List[Dict]) -> List[Dict]:
"""
Annotate a batch of variants
Args:
variants: List of variant dictionaries
Returns:
List of annotated variants
"""
annotated_variants = []
for variant in variants:
annotations = self.get_functional_annotations(variant)
annotated_variant = {**variant, **annotations}
annotated_variants.append(annotated_variant)
return annotated_variants