期刊
BIOINFORMATICS
卷 27, 期 19, 页码 2648-2654出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btr462
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资金
- National Heart, Lung, Blood Institute of the US National Institutes of Health [1RC2 HL101715]
- National Institute for Mental Health [R01 MH071852]
- National Institute of Arthritis, Musculoskeletal and Skin Disorders [P30 AR057230]
- National Cancer Institute [P30 CA16042]
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.
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