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Activity Number: 429
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #314791 View Presentation
Title: Modeling the Next Generation Sequencing Read Count Data for DNA Copy Number Variant Study
Author(s): Tieming Ji* and Jie Chen
Companies: University of Missouri - Columbia and Georgia Regents University
Keywords: Bayesian analysis ; Change point analysis ; Copy number variation ; Moving window algorithm ; Next generation sequencing reads
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). 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.


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