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Modeling the next generation sequencing read count data for DNA copy number variant study

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  • Ji Tieming

    (Department of Statistics, University of Missouri at Columbia, Columbia, MO 65211, USA)

  • Chen Jie

    (Department of Biostatistics and Epidimeology, Medical College of Georgia, Georgia Regents University, Augusta, GA 30912, USA)

Abstract

As one of the most recent advanced technologies developed for biomedical research, the next generation sequencing (NGS) technology has opened more opportunities for scientific discovery of genetic information. The NGS technology is particularly useful in elucidating a genome for the analysis of DNA copy number variants (CNVs). The study of CNVs is important as many genetic studies have led to the conclusion that cancer development, genetic disorders, and other diseases are usually relevant to CNVs on the genome. One way to analyze the NGS data for detecting boundaries of CNV regions on a chromosome or a genome is to phrase the problem as a statistical change point detection problem presented in the read count data. We therefore provide a statistical change point model to help detect CNVs using the NGS read count data. We use a Bayesian approach to incorporate possible parameter changes in the underlying distribution of the NGS read count data. Posterior probabilities for the change point inferences are derived. Extensive simulation studies have shown advantages of our proposed methods. The proposed methods are also applied to a publicly available lung cancer cell line NGS dataset, and CNV regions on this cell line are successfully identified.

Suggested Citation

  • Ji Tieming & Chen Jie, 2015. "Modeling the next generation sequencing read count data for DNA copy number variant study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(4), pages 361-374, August.
    • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:4:p:361-374:n:3
    DOI: 10.1515/sagmb-2014-0054
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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. Guha, Subharup & Li, Yi & Neuberg, Donna, 2008. "Bayesian Hidden Markov Modeling of Array CGH Data," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 485-497, June.
    3. Jie Chen & Ayten Yiğiter & Kuang-Chao Chang, 2011. "A Bayesian approach to inference about a change point model with application to DNA copy number experimental data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1899-1913, September.
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