cgpCaVEManWrapper: Simple Execution of CaVEMan in Order to Detect Somatic Single Nucleotide Variants in NGS Data
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- David Jones
- Cancer Genome Project, Wellcome Trust Sanger Institute Cambridge United Kingdom
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- Keiran M. Raine
- Cancer Genome Project, Wellcome Trust Sanger Institute Cambridge United Kingdom
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- Helen Davies
- Cancer Genome Project, Wellcome Trust Sanger Institute Cambridge United Kingdom
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- Patrick S. Tarpey
- Cancer Genome Project, Wellcome Trust Sanger Institute Cambridge United Kingdom
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- Adam P. Butler
- Cancer Genome Project, Wellcome Trust Sanger Institute Cambridge United Kingdom
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- Jon W. Teague
- Cancer Genome Project, Wellcome Trust Sanger Institute Cambridge United Kingdom
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- Serena Nik‐Zainal
- Cancer Genome Project, Wellcome Trust Sanger Institute Cambridge United Kingdom
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- Peter J. Campbell
- Cancer Genome Project, Wellcome Trust Sanger Institute Cambridge United Kingdom
Abstract
<jats:title>Abstract</jats:title><jats:p>CaVEMan is an expectation maximization–based somatic substitution‐detection algorithm that is written in C. The algorithm analyzes sequence data from a test sample, such as a tumor relative to a reference normal sample from the same patient and the reference genome. It performs a comparative analysis of the tumor and normal sample to derive a probabilistic estimate for putative somatic substitutions. When combined with a set of validated post‐hoc filters, CaVEMan generates a set of somatic substitution calls with high recall and positive predictive value. Here we provide instructions for using a wrapper script called cgpCaVEManWrapper, which runs the CaVEMan algorithm and additional downstream post‐hoc filters. We describe both a simple one‐shot run of cgpCaVEManWrapper and a more in‐depth implementation suited to large‐scale compute farms. © 2016 by John Wiley & Sons, Inc.</jats:p>
Journal
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- Current Protocols in Bioinformatics
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Current Protocols in Bioinformatics 56 (1), 15.10.1-, 2016-12
Wiley
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Details 詳細情報について
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- CRID
- 1360011144225099520
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- DOI
- 10.1002/cpbi.20
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- ISSN
- 1934340X
- 19343396
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- Data Source
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- Crossref