Dataset Information

A Sparse Model Based Detection of Copy Number Variations From Exome Sequencing Data.

ABSTRACT: Whole-exome sequencing provides a more cost-effective way than whole-genome sequencing for detecting genetic variants, such as copy number variations (CNVs). Although a number of approaches have been proposed to detect CNVs from whole-genome sequencing, a direct adoption of these approaches to whole-exome sequencing will often fail because exons are separately located along a genome. Therefore, an appropriate method is needed to target the specific features of exome sequencing data.In this paper, a novel sparse model based method is proposed to discover CNVs from multiple exome sequencing data. First, exome sequencing data are represented with a penalized matrix approximation, and technical variability and random sequencing errors are assumed to follow a generalized Gaussian distribution. Second, an iteratively reweighted least squares algorithm is used to estimate the solution.The method is tested and validated on both synthetic and real data, and compared with other approaches including CoNIFER, XHMM, and cn.MOPS. The test demonstrates that the proposed method outperform other approaches.The proposed sparse model can detect CNVs from exome sequencing data with high power and precision. Significance: Sparse model can target the specific features of exome sequencing data. The software codes are freely available at http://www.tulane.edu/ wyp/software/Exon_CNV.m.

SUBMITTER: Duan J

PROVIDER: S-EPMC4808620 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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A Sparse Model Based Detection of Copy Number Variations From Exome Sequencing Data.

Duan Junbo J Wan Mingxi M Deng Hong-Wen HW Wang Yu-Ping YP

IEEE transactions on bio-medical engineering 20160301 3

<h4>Goal</h4>Whole-exome sequencing provides a more cost-effective way than whole-genome sequencing for detecting genetic variants, such as copy number variations (CNVs). Although a number of approaches have been proposed to detect CNVs from whole-genome sequencing, a direct adoption of these approaches to whole-exome sequencing will often fail because exons are separately located along a genome. Therefore, an appropriate method is needed to target the specific features of exome sequencing data. ...[more]

PMID: 26258935

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Project description:BackgroundRecently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors' complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer.ResultsIn this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly.ConclusionsThe limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.

Dataset Information

A Sparse Model Based Detection of Copy Number Variations From Exome Sequencing Data.

Publications

A Sparse Model Based Detection of Copy Number Variations From Exome Sequencing Data.

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