Journal Article

Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants

Ruibin Xi, Semin Lee, Yuchao Xia, Tae-Min Kim and Peter J. Park

in Nucleic Acids Research

Volume 44, issue 13, pages 6274-6286
Published in print July 2016 | ISSN: 0305-1048
Published online June 2016 | e-ISSN: 1362-4962 | DOI: https://dx.doi.org/10.1093/nar/gkw491

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Whole-genome sequencing data allow detection of copy number variation (CNV) at high resolution. However, estimation based on read coverage along the genome suffers from bias due to GC content and other factors. Here, we develop an algorithm called BIC-seq2 that combines normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to detect both somatic and germline CNVs accurately. Analysis of simulation data showed that this method outperforms existing methods. We apply this algorithm to low coverage whole-genome sequencing data from peripheral blood of nearly a thousand patients across eleven cancer types in The Cancer Genome Atlas (TCGA) to identify cancer-predisposing CNV regions. We confirm known regions and discover new ones including those covering KMT2C, GOLPH3, ERBB2 and PLAG1. Analysis of colorectal cancer genomes in particular reveals novel recurrent CNVs including deletions at two chromatin-remodeling genes RERE and NPM2. This method will be useful to many researchers interested in profiling CNVs from whole-genome sequencing data.

Journal Article.  9289 words.  Illustrated.

Subjects: Chemistry ; Biochemistry ; Bioinformatics and Computational Biology ; Genetics and Genomics ; Molecular and Cell Biology

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