Author ORCID Identifier

0000-0002-7230-4009

Date of Award

12-13-2021

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Pavel Skums

Second Advisor

Alex Zelikovsky

Abstract

Next-generation sequencing (NGS) technologies experienced giant leaps in recent years. Short read samples reach millions of reads, and the number of samples has been growing enormously in the wake of the COVID-19 pandemic. This data can expose essential aspects of disease transmission and development and reveal the key to its treatment. At the same time, single-cell sequencing saw the progress of getting from dozens to tens of thousands of cells per sample. These technological advances bring new challenges for computational biology and require the development of scalable, robust methods to deal with a wide range of problems varying from epidemiology to cancer studies.

The first part of this work is focused on processing virus NGS data. It proposes algorithms that can facilitate the initial data analysis steps by filtering genetically related sequencing and the tool investigating intra-host virus diversity vital for biomedical research and epidemiology.

The second part addresses single-cell data in cancer studies. It develops evolutionary cancer models involving new quantitative parameters of cancer subclones to understand the underlying processes of cancer development better.

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