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BioMed Research International
Volume 2017 (2017), Article ID 9631282, 11 pages
https://doi.org/10.1155/2017/9631282
Research Article

ExCNVSS: A Noise-Robust Method for Copy Number Variation Detection in Whole Exome Sequencing Data

1Department of Computer Engineering, Hallym University, Chuncheon, Republic of Korea
2Smart Computing Lab, Hallym University, Chuncheon, Republic of Korea
3Department of Electronic Engineering, Kyonggi University, Suwon, Republic of Korea
4Department of Electronic Engineering, Hallym University, Chuncheon, Republic of Korea

Correspondence should be addressed to Unjoo Lee; rk.ca.myllah@eelje and Jeehee Yoon; rk.ca.myllah@nooyhj

Received 20 February 2017; Revised 4 May 2017; Accepted 21 May 2017; Published 18 June 2017

Academic Editor: Marco Fichera

Copyright © 2017 Jinhwa Kong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Copy number variations (CNVs) are structural variants associated with human diseases. Recent studies verified that disease-related genes are based on the extraction of rare de novo and transmitted CNVs from exome sequencing data. The need for more efficient and accurate methods has increased, which still remains a challenging problem due to coverage biases, as well as the sparse, small-sized, and noncontinuous nature of exome sequencing. In this study, we developed a new CNV detection method, ExCNVSS, based on read coverage depth evaluation and scale-space filtering to resolve these problems. We also developed the method ExCNVSSnoRatio, which is a version of ExCNVSS, for applying to cases with an input of test data only without the need to consider the availability of a matched control. To evaluate the performance of our method, we tested it with 11 different simulated data sets and 10 real HapMap samples’ data. The results demonstrated that ExCNVSS outperformed three other state-of-the-art methods and that our method corrected for coverage biases and detected all-sized CNVs even without matched control data.