Degree Type

Thesis

Date of Award

2020

Degree Name

Doctor of Philosophy

Department

Computer Science

Major

Computer Science

First Advisor

Oliver Eulenstein

Abstract

Phylogenetic networks become a significant scheme for enhancing our understanding of how different groups of species associate with each other, which provides substantial knowledge about the relationship among species. RNA virus diversity is the result of mutation, recombination, and reassortment. Traditional virus evolution studies rely on single-gene phylogenetic trees that do not account for all these processes. Phylogenetic network algorithms can do so but are hampered by computational limitations. We introduce an efficient software package with a graphical user interface called PhyloVirus that constructs and allows visualization of phylogenetic trees, median trees, and networks. We apply our software to swine influenza A virus (IAV), quantifying reassortment events in the evolution of H3N2 viruses and identify novel reassorted viruses. We developed PhyloVirus, a multi-platform Java application which accepts a set of Newick-format gene trees and constructs a species tree using our Robinson-Foulds Network (RF-Net) algorithm. Phylogenetic networks are visualized by displaying a phylogeny string in the extended Newick format and drawing the network with k reticulation events. We apply RF-Net to a swine IAV H3N2 dataset, estimating a reassortment network with over 500 strains.We apply a usability study to confirm that the PhyloVirus system meets potential users' expectations and investigates the participant's behavior and preferences during the study. Involving users in the early stage of improving systems can lead to more valuable and helpful features and functions. Our usability study has been undertaken with biologists from different groups and different laborites to identify any usability issues, gather quantitative and qualitative data, and determine the participant's fulfillment with PhyloVirus software.

DOI

https://doi.org/10.31274/etd-20210114-101

Copyright Owner

Fathi Mubaraki

Language

en

File Format

application/pdf

File Size

114 pages

Available for download on Friday, January 07, 2022

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