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Modeling read counts for CNV detection in exome sequencing data

MPG-Autoren
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Love,  M. I.
IMPRS 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;

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Mysickova,  A.
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Sun,  R.
Gene Structure and Array Design (Stefan Haas), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Kalscheuer,  V. M.
Chromosome Rearrangements and Disease (Vera Kalscheuer), Dept. of Human Molecular Genetics (Head: Hans-Hilger Ropers), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Vingron,  M.
Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Haas,  S. A.
Gene Structure and Array Design (Stefan Haas), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Zitation

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.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-D053-7
Zusammenfassung
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.