Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Biology, Life Sciences & Biomedicine - Other Topics, Bayesian phylogenetics, scalable inference, online inference, Hamiltonian Monte Carlo, BEAST, adapative MCMC, CHAIN MONTE-CARLO, RANDOM-WALK, INFERENCE, GUIDE, MODEL, TIME, Algorithms, Bayes Theorem, COVID-19, Humans, Markov Chains, Monte Carlo Method, Phylogeny, SARS-CoV-2, Software, G0E1420N#55517644, G098321N#56127204, G051322N#56762601, C14/18/094#54689608, 06 Biological Sciences, 11 Medical and Health Sciences, Evolutionary Biology, 31 Biological sciences, 32 Biomedical and clinical sciences

Abstract:

Recent advances in Bayesian phylogenetics offer substantial computational savings to accommodate increased genomic sampling that challenges traditional inference methods. In this review, we begin with a brief summary of the Bayesian phylogenetic framework, and then conceptualize a variety of methods to improve posterior approximations via Markov chain Monte Carlo (MCMC) sampling. Specifically, we discuss methods to improve the speed of likelihood calculations, reduce MCMC burn-in, and generate better MCMC proposals. We apply several of these techniques to study the evolution of HIV virulence along a 1536-tip phylogeny and estimate the internal node heights of a 1000-tip SARS-CoV-2 phylogenetic tree in order to illustrate the speed-up of such analyses using current state-of-the-art approaches. We conclude our review with a discussion of promising alternatives to MCMC that approximate the phylogenetic posterior. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.