文献_2011-Modeling read counts for CNV detection in exome sequencing data.pdfVIP

Statistical Applications in Genetics and Molecular Biology Volume 10, Issue 1 2011 Article 52 Modeling Read Counts for CNV Detection in Exome Sequencing Data Michael I. Love, Max Planck Institute for Molecular Genetics Alena Myšičková, Max Planck Institute for Molecular Genetics Ruping Sun, Max Planck Institute for Molecular Genetics Vera Kalscheuer, Max Planck Institute for Molecular Genetics Martin Vingron, Max Planck Institute for Molecular Genetics Stefan A. Haas, Max Planck Institute for Molecular Genetics Reco ended Citation: Love, Michael I.; Myšičková, Alena; Sun, Ruping; Kalscheuer, Vera; Vingron, Martin; and Haas, Stefan A. (2011) Modeling Read Counts for CNV Detection in Exome Sequencing Data, Statistical Applications in Genetics and Molecular Biology : Vol. 10: Iss. 1, Article 52. DOI: 10.2202/1544-6115.1732 ©2011 De Gruyter. All rights reserved. Modeling Read Counts for CNV Detection in Exome Sequencing Data Michael I. Love, Alena Myšičková, Ruping Sun, Vera Kalscheuer, Martin Vingron, and Stefan A. Haas Abstract Varying depth of high-throughput sequencing reads along a chromosome makes it possible to observe copy number variants (CNVs) in a sample relative to a reference. In exome and other targeted sequencing projects, technical factors increase variation in read depth while reducing the number of observed locations, adding difficulty to the problem of identifying CNVs. We present a hidden Markov model for detecting CNVs from raw read count data, using background read depth from a control set as well as other position