Affiliation:
1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Abstract
Abstract
Whole-genome sequencing (WGS) of parent–offspring trios has become widely used to identify causal copy number variations (CNVs) in rare and complex diseases. Existing CNV detection approaches usually do not make effective use of Mendelian inheritance in parent–offspring trios and yield low accuracy. In this study, we propose a novel integrated approach, TrioCNV2, for jointly detecting CNVs from WGS data of the parent–offspring trio. TrioCNV2 first makes use of the read depth and discordant read pairs to infer approximate locations of CNVs and then employs the split read and local de novo assembly approaches to refine the breakpoints. We use the real WGS data of two parent–offspring trios to demonstrate TrioCNV2’s performance and compare it with other CNV detection approaches. The software TrioCNV2 is implemented using a combination of Java and R and is freely available from the website at https://github.com/yongzhuang/TrioCNV2.
Funder
National Key R&D Program of China
Fundamental Research Funds for the Central Universities
Heilongjiang Postdoctoral Science Foundation
China Postdoctoral Science Foundation
Natural Science Foundation of China
Publisher
Oxford University Press (OUP)
Subject
Molecular Biology,Information Systems