Detect Copy Number Variations from Read-depth of High-throughput Sequencing Data Public Deposited

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  • March 19, 2019
Creator
  • Wang, Weibo
    • Affiliation: College of Arts and Sciences, Department of Computer Science
Abstract
  • Copy-number variation (CNV) is a major form of genetic variation and a risk factor for various human diseases, so it is crucial to accurately detect and characterize CNVs. High-throughput sequencing (HTS) technologies promise to revolutionize CNV detection but present substantial analytic challenges. This dissertation investigates improving the CNV detection using HTS data mainly from the following aspects. It is observed that various sources of experimental biases in HTS confound read-depth estimation, and bias correction has not been adequately addressed by existing methods. This dissertation presents a novel read-depth-based method, GENSENG, which identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. It is conceivable that allele-specific reads from HTS data could be leveraged to both enhance CNV detection as well as produce allele-specific copy number (ASCN) calls. Although statistical methods have been developed to detect CNVs using whole-genome sequence (WGS) and/or whole-exome sequence (WES) data, information from allele-specific read counts has not yet been adequately exploited. This dissertation presents an integrated method, called AS-GENSENG, which incorporates allele-specific read counts in CNV detection and estimates ASCN using either WGS or WES data. Although statistically powerful, the GLM+NB method used in GENSENG and AS-GENSENG has a quadric computational complexity and therefore suffers from slow running time when applied to large-scale sequencing data. This dissertation aims to substantially speed up the GLM+NB method by using a randomized algorithm and demonstrate the utility of our approach by providing R-GENSENG, a speeded up version of GENSENG.
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  • In Copyright
Advisor
  • Wang, Wei
  • Sun, Wei
  • Szatkiewicz, Jin
  • McMillan, Leonard
  • Prins, Jan
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2015
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  • Chapel Hill, NC
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