Author ORCID Identifier

https://orcid.org/0000-0003-0385-1831

Date of Award

8-10-2021

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Alex Zelikovsky

Second Advisor

Pavel Skums

Third Advisor

Robert Harrison

Fourth Advisor

William M. Switzer

Abstract

The deep coverage offered by next-generation sequencing (NGS) technology has facilitated the reconstruction of intra-host RNA viral populations at an unprecedented level of detail. However, NGS data requires sophisticated analysis dealing with millions of error-prone short reads. This dissertation will first review the challenges and methods for viral NGS genomic data analysis in the NGS era. Second, it presents a software tool CliqueSNV for inferring viral quasispecies based on extracting pairs of statistically linked mutations from noisy reads, which effectively reduces sequencing noise and enables identifying minority haplotypes with a frequency below the sequencing error rate. Finally, the dissertation describes algorithms VOICE and MinDistB for inference of relatedness between viral samples, identification of transmission clusters, and sources of infection.

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