Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals.

ABSTRACT: Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phylogenetic 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 article, we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with

SUBMITTER: Fourment M 

PROVIDER: S-EPMC5920299 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature