SeqCNV: a novel method for identification of copy number variations in targeted next-generation sequencing data

• DOI: 10.1186/s12859-017-1566-3
• Publication Date: 2017-03-03T00:33:19Z
• Authors: Yong Chen; Li Zhao; Yi Wang; Ming Cao; Violet Gelowani; Mingchu Xu; Smriti A. Agrawal; Yumei Li; Stephen P. Daiger; Richard Gibbs; Fei Wang; Rui Chen
• Keywords: Identification

Targeted next-generation sequencing (NGS) has been widely used as a cost-effective way to identify the genetic basis of human disorders. Copy number variations (CNVs) contribute significantly genomic variability, some which can lead disease. However, effective detection CNVs from targeted capture data remains challenging. Here we present SeqCNV, novel CNV calling method designed use NGS data. SeqCNV extracts read depth information and utilizes maximum penalized likelihood estimation (MPLE) model copy ratio boundary. We applied both bacterial artificial clone (BAC) patient CNVs. These were validated by array comparative hybridization (aCGH). is able robustly different size using Compared with other CNV-calling methods, shows significant improvement in sensitivity specificity.