Author ORCID Identifier

https://orcid.org/0000-0001-6481-2583

Date of Award

8-8-2024

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

Ion Mandoiu

Abstract

The advent of RNA sequencing (RNA-seq) technology has significantly advanced transcriptome-related research. The availability of RNA-seq data has spurred computational biologists to develop algorithms that process this data in a statistically rigorous manner, yielding biologically meaningful results. Recent advancements in bioinformatics algorithms enable the extraction of gene expression, fusion, and pathway information as the most immediate results from RNA-seq data. The ongoing progress in computational biology further promises to expand the utility of RNA-seq data in transcriptome-based biological research.

In this dissertation, we introduce a method to detect retained introns in RNA-seq data, with the aim of developing a vaccine against cancers harboring p53 mutations. We discuss our approaches to generating unique gene signatures to elucidate the role of sensory nerve interference in the anti-melanoma immune response and to study racial disparities in triple-negative breast cancer. We propose a clustering algorithm combined with statistical methods to analyze the heterogeneity in quadruple-negative breast cancer. Additionally, we conducted a benchmarking study to assess the resilience of machine learning classification algorithms on SARS-CoV-2 genome sequences, particularly those generated with long-read specific errors.

In summary, this research provides novel methodologies for exploring RNA-seq data and their application to real-world biological research.

DOI

https://doi.org/10.57709/37319187

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