Author ORCID Identifier

https://orcid.org/0009-0005-4050-1613

Date of Award

12-2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Alex Zelikovsky

Second Advisor

Pavel Skums

Third Advisor

Murray Patterson

Fourth Advisor

Alexander Kirpich

Abstract

The utilization of extensive sequencing data is essential for exploration of computational challenges, particularly resolving clustering problems and simulating single- cell tumor evolution. However, an abundance of sequencing information introduces its own set of distinctive computational hurdles. For example, clustering difficulties emerge when trying to categorize related genetic sequences. This is crucial in understanding viral evolution, monitoring outbreaks, and formulating effective vaccines. To overcome these challenges it is necessary to create specialized algorithms and computational models precisely crafted to suit the complexities inherent in genomics data. Additionally, In the field of simulation of single-cell cancer tumors has emerged as a crucial area of research. By replicating the genetic diversification and migration processes within tumors, scientists gain unprecedented insights into cancer evolution. This simulation, rooted in complex computational frameworks, allows for the prediction of tumor progression patterns and the identification of critical genetic events. It represents a crucial step towards personalized cancer treatment strategies and a deeper understanding of oncogenesis. We introduce innovative approaches to extract valuable insights from the RNA virus sequencing data. The first method, Monte Carlo based clustering for the Reconstruction of Viral Variants. The second method, ”Genetic Algorithm with Evolutionary Jumps,” is designed to pinpoint variants of concern that might not be readily apparent in the current viral population. Additionally, we also put forth a novel tool for simulating tumor evolution from single-cell DNA sequencing data, encompassing both Single Nucleotide Variations (SNVs) and Copy Number Aberrations (CNAs).

DOI

https://doi.org/10.57709/38062772

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