Cargando…

Scalable Bayesian phylogenetics

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Fisher, Alexander A., Hassler, Gabriel W., Ji, Xiang, Baele, Guy, Suchard, Marc A., Lemey, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393558/
https://www.ncbi.nlm.nih.gov/pubmed/35989603
http://dx.doi.org/10.1098/rstb.2021.0242
_version_ 1784771295560335360
author Fisher, Alexander A.
Hassler, Gabriel W.
Ji, Xiang
Baele, Guy
Suchard, Marc A.
Lemey, Philippe
author_facet Fisher, Alexander A.
Hassler, Gabriel W.
Ji, Xiang
Baele, Guy
Suchard, Marc A.
Lemey, Philippe
author_sort Fisher, Alexander A.
collection PubMed
description 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’.
format Online
Article
Text
id pubmed-9393558
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher The Royal Society
record_format MEDLINE/PubMed
spelling pubmed-93935582022-08-30 Scalable Bayesian phylogenetics Fisher, Alexander A. Hassler, Gabriel W. Ji, Xiang Baele, Guy Suchard, Marc A. Lemey, Philippe Philos Trans R Soc Lond B Biol Sci Articles 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’. The Royal Society 2022-10-10 2022-08-22 /pmc/articles/PMC9393558/ /pubmed/35989603 http://dx.doi.org/10.1098/rstb.2021.0242 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Fisher, Alexander A.
Hassler, Gabriel W.
Ji, Xiang
Baele, Guy
Suchard, Marc A.
Lemey, Philippe
Scalable Bayesian phylogenetics
title Scalable Bayesian phylogenetics
title_full Scalable Bayesian phylogenetics
title_fullStr Scalable Bayesian phylogenetics
title_full_unstemmed Scalable Bayesian phylogenetics
title_short Scalable Bayesian phylogenetics
title_sort scalable bayesian phylogenetics
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393558/
https://www.ncbi.nlm.nih.gov/pubmed/35989603
http://dx.doi.org/10.1098/rstb.2021.0242
work_keys_str_mv AT fisheralexandera scalablebayesianphylogenetics
AT hasslergabrielw scalablebayesianphylogenetics
AT jixiang scalablebayesianphylogenetics
AT baeleguy scalablebayesianphylogenetics
AT suchardmarca scalablebayesianphylogenetics
AT lemeyphilippe scalablebayesianphylogenetics