CNVer

CNVer integrates paired-end mapping and depth-of-coverage data to detect and characterize copy number variants (CNVs) from high-throughput sequencing (HTS) data.

Key Features:

• Donor graph framework: Combines paired-end mapping and depth-of-coverage information within a unified donor graph computational framework.
• Discordant mate-pair use: Utilizes discordant mappings of mate pairs to indicate potential structural variation events.
• Depth-of-coverage analysis: Leverages coverage signals from HTS to identify large copy-variable regions.
• Sequencing-bias mitigation: Integrates multiple evidence types to reduce the impact of sequencing biases that produce uneven local coverage.
• Breakpoint resolution: Improves precision in pinpointing CNV breakpoints by combining mapping and coverage data.
• Absolute copy-count reconstruction: Reconstructs absolute copy counts of genomic segments from donor genomes.
• Low-coverage applicability: Evaluated for use with low-coverage sequencing datasets.

Scientific Applications:

• Genome-wide CNV discovery: Applied to a Yoruban individual's genome to detect 4,879 CNVs from HTS data.
• Variant validation: Demonstrated 77% correspondence of detected variants with entries in the Database of Genomic Variants (DGV).
• Deletion recovery: Recovered 81% of previously identified deletion CNVs for the analyzed individual as loss calls.
• Copy-number reconstruction studies: Used to reconstruct absolute copy counts of genomic segments for donor-genome analyses.
• Low-coverage sequencing studies: Applied and evaluated on low-coverage datasets to assess CNV detection performance under reduced coverage.

Methodology:

CNVer integrates paired-end discordant mapping information with depth-of-coverage data within a donor graph computational framework to infer CNVs and reconstruct absolute copy counts, mitigating sequencing bias effects.