Effective online Bayesian phylogenetics via sequential Monte Carlo with guided proposals

Fourment, M; Claywell, B; Dinh, V; McCoy, C; Matsen, F; Darling, A

Effective online Bayesian phylogenetics via sequential Monte Carlo with guided proposals

Fourment, M Claywell, B Dinh, V McCoy, C Matsen, F Darling, A

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Publication Type:: Journal Article
Citation:: 2017, pp. 145219
Issue Date:: 2017-06-02

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Field	Value	Language
dc.contributor.author	Fourment, M
dc.contributor.author	Claywell, B
dc.contributor.author	Dinh, V
dc.contributor.author	McCoy, C
dc.contributor.author	Matsen, F
dc.contributor.author	Darling, A https://orcid.org/0000-0003-2397-7925
dc.date.accessioned	2022-04-01T00:19:27Z
dc.date.available	2022-04-01T00:19:27Z
dc.date.issued	2017-06-02
dc.identifier.citation	2017, pp. 145219
dc.identifier.uri	http://hdl.handle.net/10453/155833
dc.description.abstract	<h4>A bstract </h4> 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.
dc.language	en
dc.relation	Directorate for Mathematical & Physical Sciences1223057
dc.relation.isbasedon	10.1101/145219
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Effective online Bayesian phylogenetics via sequential Monte Carlo with guided proposals
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - ithree - Institute of Infection, Immunity and Innovation
utslib.copyright.status	open_access	*
dc.date.updated	2022-04-01T00:19:26Z
pubs.publication-status	Published

Abstract:

A bstract

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.

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http://hdl.handle.net/10453/155833