Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


case1347053645.pdf (2.03 MB)

ETD Abstract Container

Abstract Header

Variant Detection Using Next Generation Sequencing Data

Pyon, Yoon Soo
http://rave.ohiolink.edu/etdc/view?acc_num=case1347053645

Abstract Details

2013, Doctor of Philosophy, Case Western Reserve University, EECS - Computer and Information Sciences.
Genetic variants, including single nucleotide polymorphisms (SNPs) and genomic structural variations (SVs), not only contribute to human diversity, but also are important to human health because some of them are proved to trigger diseases such as cancer, obesity and diabetes. With recent development of cost effective next generation sequencing (NGS) technologies, it has become possible to identify novel variants with high resolutions and to identify some copy neutral variants such as inversions, which cannot be detected using microarray based technologies. However, enormous amounts of raw sequence data generated from NGS technologies pose great challenges for data analysis. Efficient computational algorithms and tools to analyze these data are in great need. In this dissertation, we propose effective computational methods to identify genomic structural variations (deletions and inversions) and to infer accurate genotypes from multiple genotype and SNP calling algorithms using NGS. For structure variant detection, we propose a model based clustering approach utilizing a set of features defined for each type of SV events. Our method, termed SVMiner, not only provides a probability score for each candidate, but also predicts the heterozygosity of genomic deletions. Extensive experiments on genome-wide deep sequencing data have demonstrated that SVMiner is robust against the variability of a single cluster feature, and it performs well when classifying validated SV events with accentuated features. To improve SNP calling results, we propose a Na¿¿ve Bayes based approach, which combines prior information of known polymorphic sites and population specific allele frequencies with SNP calling results from multiple programs to obtain more accurate SNP genotypes. Results show that our approach has higher genotype calling accuracy than individual algorithms.
Jing Li (Committee Chair)
Thomas LaFramboise (Committee Member)
Mehmet Koyuturk (Committee Member)
Xiang Zhang (Committee Member)
93 p.
Bioinformatics; Computer Science; Genetics
structural variation; SNP; next generation sequencing; naive Bayes classifier

Recommended Citations

Citations

  • Pyon, Y. S. (2013). Variant Detection Using Next Generation Sequencing Data [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1347053645

    APA Style (7th edition)

  • Pyon, Yoon Soo. Variant Detection Using Next Generation Sequencing Data. 2013. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1347053645.

    MLA Style (8th edition)

  • Pyon, Yoon Soo. "Variant Detection Using Next Generation Sequencing Data." Doctoral dissertation, Case Western Reserve University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1347053645

    Chicago Manual of Style (17th edition)

case1347053645
538
© 2012, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.