文献_2010-Detecting copy number variation with mated short reads.pdfVIP

Resource Detecting copy number variation with mated short reads Paul Medvedev,1 Marc Fiume,1 Misko Dzamba,1 Tim Smith,1 and Michael Brudno1,2,3 1Department of Computer Science, University of Toronto, Toronto, Ontario M5R 3G4, Canada; 2Banting and Best, Department of Medical Research and Centre for Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario M5R 3G4, Canada The development of high-throughput sequencing (HTS) technologies has opened the door to novel methods for detecting copy number variants (CNVs) in the human genome. While in the past CNVs have been detected based on array CGH data, recent studies have shown that depth-of-coverage information from HTS technologies can also be used for the reliable identification of large copy-variable regions. Such methods, however, are hindered by sequencing biases that lead certain regions of the genome to be over- or undersampled, lowering their resolution and ability to accurately identify the exact breakpoints of the variants. In this work, we develop a method for CNV detection that supplements the depth-of- coverage with paired-end mapping information, where mate pairs mapping discordantly to the reference serve to indicate the presence of variation. Our algorithm, called CNVer, combines this information within a unified computational framework called the donor graph, allowing us to better mitigate the sequencing biases that cause uneven local coverage and accurately predict CNVs. We use CNVer to detect 4879 CNVs in the recently described genome of a Yoruban individual. Most of the calls (77%) coincide with previously known variants within the Database of Genomic Variants, while 81% of deletion copy number variants previously known for this individual coincide with one of our loss calls. Furthermore, we demonstrate that CNVer can re