Zinfandel

Zinfandel detects copy number variants (CNVs) from low-pass whole genome sequencing (WGS) data to identify medium-size deletions (200–2000 base pairs) relevant to disease and population genomics.

Key Features:

• Low-Pass Whole Genome Sequencing Compatibility: Operates on low-depth WGS data, typically below 10× coverage per sample.
• Hidden Markov Model Integration: Uses a Hidden Markov Model that jointly incorporates depth of coverage and mate-pair relationship data.
• Mate-Pair Relationship Utilization: Infers deletion likelihood by jointly analyzing multiple mate pairs within a region rather than relying on single outlier pairs, improving detection of medium-size deletions (200–2000 bp) at low depth.
• Optimal Power and Resolution: Calibrated to achieve improved power and resolution for CNV detection across varying experimental designs.

Scientific Applications:

• Disease Genomics: Identification of CNVs associated with genetic disorders and elucidation of their contributions to disease phenotypes.
• Population Genetics: Analysis of genomic diversity and population-specific CNV patterns to inform evolutionary and epidemiological studies.

Methodology:

Applies a Hidden Markov Model that integrates depth of coverage and mate-pair relationship data from low-pass WGS and jointly infers deletion likelihood across multiple mate pairs to detect medium-size deletions (200–2000 bp) without relying on single outlier pairs.