Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

CNV-TV: A robust method to discover copy number variation from short sequencing reads

Junbo Duan13, Ji-Gang Zhang23, Hong-Wen Deng123 and Yu-Ping Wang123*

Author Affiliations

1 Department of Biomedical Engineering, Tulane University, New Orleans, USA

2 Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA

3 Center for Bioinformatics and Genomics, Tulane University, New Orleans, USA

For all author emails, please log on.

BMC Bioinformatics 2013, 14:150  doi:10.1186/1471-2105-14-150

Published: 2 May 2013

Abstract

Background

Copy number variation (CNV) is an important structural variation (SV) in human genome. Various studies have shown that CNVs are associated with complex diseases. Traditional CNV detection methods such as fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (aCGH) suffer from low resolution. The next generation sequencing (NGS) technique promises a higher resolution detection of CNVs and several methods were recently proposed for realizing such a promise. However, the performances of these methods are not robust under some conditions, e.g., some of them may fail to detect CNVs of short sizes. There has been a strong demand for reliable detection of CNVs from high resolution NGS data.

Results

A novel and robust method to detect CNV from short sequencing reads is proposed in this study. The detection of CNV is modeled as a change-point detection from the read depth (RD) signal derived from the NGS, which is fitted with a total variation (TV) penalized least squares model. The performance (e.g., sensitivity and specificity) of the proposed approach are evaluated by comparison with several recently published methods on both simulated and real data from the 1000 Genomes Project.

Conclusion

The experimental results showed that both the true positive rate and false positive rate of the proposed detection method do not change significantly for CNVs with different copy numbers and lengthes, when compared with several existing methods. Therefore, our proposed approach results in a more reliable detection of CNVs than the existing methods.