Bloom, Jesse DHannon, William2024-02-122024-02-122024-02-122023Hannon_washington_0250E_26368.pdfhttp://hdl.handle.net/1773/51223Thesis (Ph.D.)--University of Washington, 2023High-throughput experiments, including deep sequencing and deep mutational scanning (DMS), provide insight into the genotypic and phenotypic landscapes traversed by an evolving virus. However, interpreting the large amount of data produced by these techniques requires a robust computational strategy. Recognizing this challenge, in my dissertation, I describe how I used a computational approach to tackle three distinct but interconnected aspects of viral evolution. In the first chapter, I characterize the adaptive progression that enables measles to infect the brain. Ordinarily, a Measles infection is acute and self-limiting. However, through unknown mechanisms, Measles can persist after acute infection, remain undetected in the body, migrate to the brain, and become neurotropic. Previous studies of measles infections of the brain have been limited by low genetic resolution and restricted sampling schemes. Using the most comprehensive spatially-sampled neurotropic measles dataset to date, our study offers compelling clues into the evolutionary processes that allowed measles to colonize the brain in a patient who succumbed to this rare disease. In the next chapter, I determine how superspreading influences the transmission of SARS-CoV-2 viral diversity between hosts. Most studies of the impact of transmission on shared viral diversity for respiratory viruses involve household or nosocomial transmission scenarios. In contrast, the dynamics of shared viral diversity in superspreading events are poorly understood, despite playing a significant role in the global spread of viruses. To address this, I investigated the spread of viral diversity during a SARS-CoV-2 superspreading event on a fishing boat to see if circumstances highly conducive to transmission exhibit unique patterns of viral evolution. I found that superspreading imposed a narrow bottleneck on viral diversity between hosts despite the unique transmission scenario. In the final chapter, I describe an interactive visualization tool to help analyze large mutation-function datasets from high-throughput experiments like deep-mutational scanning. The mutation-based data generated by these approaches is often best understood in the context of a protein’s 3D structure. However, current approaches for visualizing mutation data in the context of a protein’s structure are cumbersome and require multiple steps and software. To streamline the visualization of mutation-associated data in the context of a protein structure, I developed a web-based tool called 'dms-viz'. With 'dms-viz', researchers can easily create, analyze, and share customized visualizations of their mutation-based datasets with the broader research community. In my graduate research, I developed and applied computational methods to study viral evolution. First, I explored how virus evolution can occur within an individual host. Then, I characterized the impact of transmission between hosts on viral evolution. And finally, I developed a computational tool to help analyze large mutation-based datasets to help answer a myriad of evolutionary questions.application/pdfen-USCC BYBioinformaticsEvolutionVirologyVirologyBioinformaticsMolecular and cellular biologyThesis