Dataset Information

Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV.

ABSTRACT:

Motivation

The ability to detect copy-number variation (CNV) and loss of heterozygosity (LOH) from exome sequencing data extends the utility of this powerful approach that has mainly been used for point or small insertion/deletion detection.

Results

We present ExomeCNV, a statistical method to detect CNV and LOH using depth-of-coverage and B-allele frequencies, from mapped short sequence reads, and we assess both the method's power and the effects of confounding variables. We apply our method to a cancer exome resequencing dataset. As expected, accuracy and resolution are dependent on depth-of-coverage and capture probe design.

Availability

CRAN package 'ExomeCNV'.

Contact

fsathira@fas.harvard.edu; snelson@ucla.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Sathirapongsasuti JF

PROVIDER: S-EPMC3179661 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Publications

Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV.

Sathirapongsasuti Jarupon Fah JF Lee Hane H Horst Basil A J BA Brunner Georg G Cochran Alistair J AJ Binder Scott S Quackenbush John J Nelson Stanley F SF

Bioinformatics (Oxford, England) 20110809 19

<h4>Motivation</h4>The ability to detect copy-number variation (CNV) and loss of heterozygosity (LOH) from exome sequencing data extends the utility of this powerful approach that has mainly been used for point or small insertion/deletion detection.<h4>Results</h4>We present ExomeCNV, a statistical method to detect CNV and LOH using depth-of-coverage and B-allele frequencies, from mapped short sequence reads, and we assess both the method's power and the effects of confounding variables. We appl ...[more]

PMID: 21828086

Similar Datasets

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

Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV.

Motivation

Results

Availability

Contact

Supplementary information

Publications

Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV.

Similar Datasets

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