Copy number variation estimation from multiple next-generation sequencing samples

Authors:
Junbo Duan;Ji-Gang Zhang;Hongbao Cao;Hong-Wen Deng;Yu-Ping Wang
Affiliations:
Tulane University, New Orleans;Tulane University, New Orleans;Tulane University, New Orleans;Tulane University, New Orleans;Tulane University, New Orleans
Venue:
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Year:
2012

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Abstract

Robust and accurate detection of copy number variations (CNVs) from next-generation sequencing (NGS) data is challenging. Because of the high fluctuation of read depth signal, most existing methods, which use only one data sample, yield high false positive rate and low power. By integrating information from multiple samples, the detection could be improved. In this paper, a method to detect CNVs from multiple samples is proposed. The proposed method explores the concurrency of read depth signals across multiple samples, promising to increase the detection power. Our experiments on real data sets show that the proposed method can improve the CNV detection over several existing ones.