IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v14y2015i4p361-374n3.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2014-0054
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/sagmb-2014-0054?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bertschek, Irene & Ohnemus, Jörg & Erdsiek, Daniel & Rammer, Christian & Andres, Raphaela & Kimpeler, Simone, 2019. "Monitoringbericht Kultur- und Kreativwirtschaft 2018," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 203157.
    2. 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.
    3. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    4. Huixia Judy Wang & Jianhua Hu, 2011. "Identification of Differential Aberrations in Multiple-Sample Array CGH Studies," Biometrics, The International Biometric Society, vol. 67(2), pages 353-362, June.
    5. Paul J. Plummer & Jie Chen, 2014. "A Bayesian approach for locating change points in a compound Poisson process with application to detecting DNA copy number variations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 423-438, February.
    6. Yang Guo & Shuzhen Wang & A. K. Alvi Haque & Xiguo Yuan, 2022. "WAVECNV: A New Approach for Detecting Copy Number Variation by Wavelet Clustering," Mathematics, MDPI, vol. 10(12), pages 1-11, June.
    7. Ao Yuan & Guanjie Chen & Juan Xiong & Wenqing He & Wen Jin & Charles Rotimi, 2011. "Bayesian--frequentist hybrid model with application to the analysis of gene copy number changes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 987-1005, February.
    8. Yu Chuan Tai & Mark N. Kvale & John S. Witte, 2010. "Segmentation and Estimation for SNP Microarrays: A Bayesian Multiple Change-Point Approach," Biometrics, The International Biometric Society, vol. 66(3), pages 675-683, September.
    9. Jonghyun Yun & Tao Wang & Guanghua Xiao, 2014. "Bayesian hidden Markov models to identify RNA–protein interaction sites in PAR-CLIP," Biometrics, The International Biometric Society, vol. 70(2), pages 430-440, June.
    10. Xian F Mallory & Mohammadamin Edrisi & Nicholas Navin & Luay Nakhleh, 2020. "Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-24, July.
    11. Xuesong Yu & Timothy W. Randolph & Hua Tang & Li Hsu, 2010. "Detecting Genomic Aberrations Using Products in a Multiscale Analysis," Biometrics, The International Biometric Society, vol. 66(3), pages 684-693, September.
    12. C. Yau & O. Papaspiliopoulos & G. O. Roberts & C. Holmes, 2011. "Bayesian non‐parametric hidden Markov models with applications in genomics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 37-57, January.
    13. Xue-Ke Zhao & Pengwei Xing & Xin Song & Miao Zhao & Linxuan Zhao & Yonglong Dang & Ling-Ling Lei & Rui-Hua Xu & Wen-Li Han & Pan-Pan Wang & Miao-Miao Yang & Jing-Feng Hu & Kan Zhong & Fu-You Zhou & Xu, 2021. "Focal amplifications are associated with chromothripsis events and diverse prognoses in gastric cardia adenocarcinoma," Nature Communications, Nature, vol. 12(1), pages 1-14, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:14:y:2015:i:4:p:361-374:n:3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.