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Accelerating Bayesian inference for evolutionary biology models

MOTIVATION: Bayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data...

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Autores principales: Meyer, Xavier, Chopard, Bastien, Salamin, Nicolas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408833/
https://www.ncbi.nlm.nih.gov/pubmed/28025203
http://dx.doi.org/10.1093/bioinformatics/btw712
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author Meyer, Xavier
Chopard, Bastien
Salamin, Nicolas
author_facet Meyer, Xavier
Chopard, Bastien
Salamin, Nicolas
author_sort Meyer, Xavier
collection PubMed
description MOTIVATION: Bayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data is pushing the limits of the current use of these methods. RESULTS: We present a parallel Metropolis-Hastings (M-H) framework built with a novel combination of enhancements aimed towards parameter-rich and complex models. We show on a parameter-rich macroevolutionary model increases of the sampling speed up to 35 times with 32 processors when compared to a sequential M-H process. More importantly, our framework achieves up to a twentyfold faster convergence to estimate the posterior probability of phylogenetic trees using 32 processors when compared to the well-known software MrBayes for Bayesian inference of phylogenetic trees. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/XavMeyer/hogan SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-54088332017-05-03 Accelerating Bayesian inference for evolutionary biology models Meyer, Xavier Chopard, Bastien Salamin, Nicolas Bioinformatics Original Papers MOTIVATION: Bayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data is pushing the limits of the current use of these methods. RESULTS: We present a parallel Metropolis-Hastings (M-H) framework built with a novel combination of enhancements aimed towards parameter-rich and complex models. We show on a parameter-rich macroevolutionary model increases of the sampling speed up to 35 times with 32 processors when compared to a sequential M-H process. More importantly, our framework achieves up to a twentyfold faster convergence to estimate the posterior probability of phylogenetic trees using 32 processors when compared to the well-known software MrBayes for Bayesian inference of phylogenetic trees. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/XavMeyer/hogan SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2017-03-01 2016-12-07 /pmc/articles/PMC5408833/ /pubmed/28025203 http://dx.doi.org/10.1093/bioinformatics/btw712 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Papers
Meyer, Xavier
Chopard, Bastien
Salamin, Nicolas
Accelerating Bayesian inference for evolutionary biology models
title Accelerating Bayesian inference for evolutionary biology models
title_full Accelerating Bayesian inference for evolutionary biology models
title_fullStr Accelerating Bayesian inference for evolutionary biology models
title_full_unstemmed Accelerating Bayesian inference for evolutionary biology models
title_short Accelerating Bayesian inference for evolutionary biology models
title_sort accelerating bayesian inference for evolutionary biology models
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408833/
https://www.ncbi.nlm.nih.gov/pubmed/28025203
http://dx.doi.org/10.1093/bioinformatics/btw712
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