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  2. High-throughput sequencing
  3. Whole-exome sequencing
  4. CNV detection
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A statistical method to detect CNV and LOH using depth-of-coverage and B-allele frequencies from mapped short sequence reads in exome sequencing data. We apply ExomeCNV to a cancer exome resequencing dataset. As expected, accuracy and resolution are dependent on depth-of-coverage and capture probe design.

Software type:
Package
Interface:
Command line interface
Restrictions to use:
None
Operating system:
Unix/Linux, Mac OS, Windows
Programming languages:
R
Computer skills:
Advanced
Version:
ExomeCNV version 1.4
Stability:
Stable
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Institution(s)

National Heart, Lung, Blood Institute of the US National Institutes of Health grant (1RC2 HL101715); National Institute for Mental Health (R01 MH071852); National Institute of Arthritis, Musculoskeletal and Skin Disorders (P30 AR057230); National Cancer Institute (P30 CA16042)

  • (Sathirapongsasuti et al., 2011) Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV. Bioinformatics.
    PMID: 21828086
  • (Samarakoon et al., 2014) Identification of copy number variants from exome sequence data. BMC genomics.
    PMID: 25102989
  • (Kadalayil et al., 2014) Exome sequence read depth methods for identifying copy number changes. Briefings in bioinformatics.
    PMID: 25169955
  • (Tan et al., 2014) An evaluation of copy number variation detection tools from whole-exome sequencing data. Human mutation.
    PMID: 24599517

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