English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Modeling read counts for CNV detection in exome sequencing data

MPS-Authors
/persons/resource/persons50412

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;

/persons/resource/persons50440

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

/persons/resource/persons50587

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;

/persons/resource/persons50369

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;

/persons/resource/persons50613

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

/persons/resource/persons50183

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;

External Ressource
Fulltext (public)

Love.pdf
(Publisher version), 834KB

Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: http://hdl.handle.net/21.11116/0000-0000-D053-7
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.