Bioinformatics

Core concepts and patterns for biological data analysis.

Variant Analysis

# Classify variant types
def classify_variant(ref, alt):
    if len(ref) == len(alt) == 1:
        return 'SNP'
    elif len(ref) > len(alt):
        return 'deletion'
    elif len(ref) < len(alt):
        return 'insertion'
    else:
        return 'complex'

# Transition vs transversion
transitions = {('A', 'G'), ('G', 'A'), ('C', 'T'), ('T', 'C')}
def is_transition(ref, alt):
    return (ref, alt) in transitions

Common Metrics

# Allele frequency
af = alt_count / (ref_count + alt_count)

# Read depth filtering
min_depth = 10
filtered = df[df['DP'] >= min_depth]

# Quality filtering
high_qual = df[df['QUAL'] >= 30]

Genomic Coordinates

# BED format: 0-based, half-open
# VCF format: 1-based, closed

def vcf_to_bed(chrom, pos, ref):
    return (chrom, pos - 1, pos - 1 + len(ref))

# Check overlap
def overlaps(start1, end1, start2, end2):
    return start1 < end2 and start2 < end1