Author ORCID Identifier
0000-0003-0747-8245
Date of Award
Summer 8-8-2023
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
RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. The availability of RNA-seq data encouraged computational biologists to develop algorithms to process the data in a statistically disciplinary manner to generate biologically meaningful results. Clustering viral sequences allows us to characterize the composition and structure of intrahost and interhost viral populations, which play a crucial role in disease progression and epidemic spread. In this research, we propose and validate a new entropy-based method for clustering aligned viral sequences considered as categorical data. The method finds a homogeneous clustering by minimizing information entropy rather than the distance between sequences in the same cluster. Moreover in this research, we present a novel pathway analysis method based on Expectation-Maximization (EM) algorithm to study the enzyme expression and pathway activity using meta-transcriptomic data. We will also discuss our approaches to generating unique gene signatures to understand the role of sensory nerve interference in the anti-melanoma immune response and study the racial disparity in Triple-negative breast cancer. Finally, we present our method to detect the retained introns in RNA-seq data to develop a vaccine against cancer having p53 mutations. In summary, this research provides novel approaches to exploring RNA-seq data and their application to real-world biological research.
DOI
https://doi.org/10.57709/35871534
Recommended Citation
Hosseini, Akram Sadat, "Bioinformatics Tools for RNA-seq Data Analysis." Dissertation, Georgia State University, 2023.
doi: https://doi.org/10.57709/35871534
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