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We propose, implement, and evaluate a novel method (GATK gCNV) for accurate discovery of rare and common copy-number variations (CNVs) from read-depth data obtained from whole genome sequencing (WGS), whole exome sequencing (WES), or custom gene panels. GATK gCNV utilizes a sophisticated Bayesian model to learn bias factors arising from sequencing and library preparation. This model accounts for ploidy of sex chromosomes and autosomal aneuploidies, treats GC bias probabilistically, and automatically determines the necessary level of model complexity in a data-driven manner. Unlike most existing read-depth methods, GATK gCNV maintains a high level of sensitivity in common CNV regions, due to a hierarchical hidden Markov model used for accurate genotyping of multi-allelic loci. Furthermore, GATK gCNV performs bias modeling and CNV discovery simultaneously and self …