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Modeling Read Counts for CNV Detection in Exome Sequencing Data

Author

Listed:
  • Love Michael I.

    (Max Planck Institute for Molecular Genetics)

  • Myšičková Alena

    (Max Planck Institute for Molecular Genetics)

  • Sun Ruping

    (Max Planck Institute for Molecular Genetics)

  • Kalscheuer Vera

    (Max Planck Institute for Molecular Genetics)

  • Vingron Martin

    (Max Planck Institute for Molecular Genetics)

  • Haas Stefan A.

    (Max Planck Institute for Molecular Genetics)

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.

Suggested Citation

  • Love Michael I. & Myšičková Alena & Sun Ruping & Kalscheuer Vera & Vingron Martin & Haas Stefan A., 2011. "Modeling Read Counts for CNV Detection in Exome Sequencing Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-30, November.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:52
    DOI: 10.2202/1544-6115.1732
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    References listed on IDEAS

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    1. Christopher A Miller & Oliver Hampton & Cristian Coarfa & Aleksandar Milosavljevic, 2011. "ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-7, January.
    2. Fridlyand, Jane & Snijders, Antoine M. & Pinkel, Dan & Albertson, Donna G. & Jain, A.N.Ajay N., 2004. "Hidden Markov models approach to the analysis of array CGH data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 132-153, July.
    3. Dale J Hedges & Toumy Guettouche & Shan Yang & Guney Bademci & Ashley Diaz & Ashley Andersen & William F Hulme & Sara Linker & Arpit Mehta & Yvonne J K Edwards & Gary W Beecham & Eden R Martin & Marga, 2011. "Comparison of Three Targeted Enrichment Strategies on the SOLiD Sequencing Platform," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-8, April.
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    Cited by:

    1. Rocco Piazza & Vera Magistroni & Alessandra Pirola & Sara Redaelli & Roberta Spinelli & Serena Redaelli & Marta Galbiati & Simona Valletta & Giovanni Giudici & Giovanni Cazzaniga & Carlo Gambacorti-Pa, 2013. "CEQer: A Graphical Tool for Copy Number and Allelic Imbalance Detection from Whole-Exome Sequencing Data," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-12, October.
    2. Yuki Matsushita & Jialin Liu & Angel Ka Yan Chu & Chiaki Tsutsumi-Arai & Mizuki Nagata & Yuki Arai & Wanida Ono & Kouhei Yamamoto & Thomas L. Saunders & Joshua D. Welch & Noriaki Ono, 2023. "Bone marrow endosteal stem cells dictate active osteogenesis and aggressive tumorigenesis," Nature Communications, Nature, vol. 14(1), pages 1-23, December.

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