RT Journal Article
SR Electronic
T1 Effective online Bayesian phylogenetics via sequential Monte Carlo with guided proposals
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 145219
DO 10.1101/145219
A1 Mathieu Fourment
A1 Brian C. Claywell
A1 Vu Dinh
A1 Connor McCoy
A1 Frederick A. Matsen IV
A1 Aaron E. Darling
YR 2017
UL http://biorxiv.org/content/early/2017/06/02/145219.abstract
AB Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phy-logenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this paper we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop ‘guided’ proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely-used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.