Parallelization of Bayesian Phylogenetics to Greatly Improve Run Times
Date
2024-03-24
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Abstract
Phylogenetic analyses are invaluable to understanding the transmission of viruses, especially during disease outbreaks. In particular, Bayesian phylogenetics has great potential in modeling viral transmission due to the numerous phylogenetic models that can be incorporated. Currently, the availability of user-friendly software and accessibility to sequence data makes phylogenetic analyses easy to perform. However, to date, Bayesian phylogenetic analyses are still limited by long computational run-times which are especially unfavorable during ongoing and evolving disease outbreaks that demand real-time phylogeny results. Current optimization methods of Bayesian phylogenetic analysis mainly focus on iteration-level parallelization and mostly overlook the potential of larger-scale parallelization approaches. In this thesis, we provide an in-depth overview of topics including phylogenetic analysis, relevant biological information, and phylogenetic analysis optimization methods. We also proposed a novel parallelized Markov Chain Monte Carlo method that greatly improved Bayesian phylogenetic run times and integrated the approach into a data pipeline to allow for the direct analysis of viral samples. We demonstrated the validity of our methods by performing phylogenetic analyses on two sets of HIV simulation data and one set of real-world SARS-CoV-2 data. Our results suggested that the parallelization of MCMC in Bayesian phylogenetic analyses drastically reduces run times by 29-fold without causing significant deviations in parameter estimates and predicted phylogenetic trees.
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Keywords
Markov Chain Monte Carlo, SARS-CoV-2, Simulation, Bayesian, Phylogenetic analysis, Parallelization
Citation
Yang, D. (2024). Parallelization of Bayesian phylogenetics to greatly improve run times (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.