Date of Award

Spring 5-4-2021

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Dr. Alexander Zelikovsky

Abstract

RNA viruses mutate at extremely high rates, forming an intra-host viral population of closely related variants, which allows them to evade the host’s immune system and makes them particularly dangerous. Viral outbreaks pose a significant threat for public health. Progress of sequencing technologies made it possible to identify and sample intra-host viral populations at great depth. Consequently, the contribution of sequencing technologies to molecular surveillance of viral outbreaks becomes more and more substantial. Genome sequencing of viral populations reveals similarities between samples, allows to measure viral genetic distance and facilitate outbreak identification and isolation. Computational methods can be used to infer transmission characteristics from sequencing data. However, due to the specifics of next-generation sequencing (NGS) approaches, and the limited availability of viral data, existing methods lack accuracy and efficiency. In this dissertation, I present a novel, flexible methods, that allow tackling crucial epidemiological problems, such as identification of transmission clusters, sources of infection, and transmission direction.

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