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This article is part of the supplement: Selected articles from The Second Workshop on Data Mining of Next-Generation Sequencing in conjunction with the 2012 IEEE International Conference on Bioinformatics and Biomedicine

Open Access Highly Accessed Research

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

Min Zhao1, Qingguo Wang1, Quan Wang1, Peilin Jia1 and Zhongming Zhao123*

Author Affiliations

1 Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

2 Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

3 Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

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BMC Bioinformatics 2013, 14(Suppl 11):S1  doi:10.1186/1471-2105-14-S11-S1

Published: 13 September 2013

Abstract

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.