A Bayesian Procedure for Copy Number Variations Detection from DNA-Sequencing

Abstract

Copy number variations (CNVs) are genomic structural mutations with abnormal gene fragment copies. CNV detection algorithms for next generation sequencing (NGS) data could be classified by genome targets including whole genome sequencing (WGS) and targeted exome sequencing (TES) with different suppositions. Some tools have been published to predict CNVs by NGS data, but most of them just apply to a specific data type. Many whole genome tools assume that the continuity of search space and reads uniform coverage across the genome. These assumptions break down in the exome capture because of discontinuous segments and exome specific functional biases. We specify the large unconsidered genomic fragments as gaps to preserve the truly location information. The gap labels will get great help for expressing the information from the discontinuous regions and it will adapt to detect CNV for both WGS and TES with the following Bayesian procedure. We built a Bayesian hierarchical model and an efficient reversible jump Markov chain Monte Carlo inference algorithm for analyzing NGS read depths. The performance of the Bayesian procedure was evaluated and compared with competing approaches using both simulations and real data.