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  Modeling read counts for CNV detection in exome sequencing data

Love, M. I., Mysickova, A., Sun, R., Kalscheuer, V. M., Vingron, M., & Haas, S. A. (2011). Modeling read counts for CNV detection in exome sequencing data. Statistical Applications in Genetics and Molecular Biology, 10(1). doi:10.2202/1544-6115.1732.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-D053-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-D054-6
Genre: Journal Article

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Love.pdf (Publisher version), 834KB
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© 2011 De Gruyter
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 Creators:
Love, M. I.1, Author              
Mysickova, A.2, Author              
Sun, R.3, Author              
Kalscheuer, V. M.4, Author              
Vingron, M.5, Author              
Haas, S. A.3, Author              
Affiliations:
1IMPRS for Computational Biology and Scientific Computing - IMPRS-CBSC (Kirsten Kelleher), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479666              
2Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              
3Gene Structure and Array Design (Stefan Haas), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479640              
4Chromosome Rearrangements and Disease (Vera Kalscheuer), Dept. of Human Molecular Genetics (Head: Hans-Hilger Ropers), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479642              
5Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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Free keywords: Algorithms Base Composition Chromosomes, Human, X/genetics Computer Simulation *DNA Copy Number Variations Databases, Genetic *Exome Gene Frequency Genetic Carrier Screening Homozygote Humans *Markov Chains Predictive Value of Tests Sensitivity and Specificity Sequence Analysis, DNA/*methods
 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 positional covariates such as GC-content. The model, exomeCopy, is applied to a large chromosome X exome sequencing project identifying a list of large unique CNVs. CNVs predicted by the model and experimentally validated are then recovered using a cross-platform control set from publicly available exome sequencing data. Simulations show high sensitivity for detecting heterozygous and homozygous CNVs, outperforming normalization and state-of-the-art segmentation methods.

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Language(s): eng - English
 Dates: 2011-11-082011
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.2202/1544-6115.1732
ISSN: 1544-6115 (Electronic)
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Title: Statistical Applications in Genetics and Molecular Biology
Source Genre: Journal
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Publ. Info: Berkeley, CA : Berkeley Electronic Press
Pages: - Volume / Issue: 10 (1) Sequence Number: - Start / End Page: - Identifier: ISSN: 1544-6115
CoNE: https://pure.mpg.de/cone/journals/resource/111055796786000