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      ExaBayes: Massively Parallel Bayesian Tree Inference for the Whole-Genome Era

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          Abstract

          Modern sequencing technology now allows biologists to collect the entirety of molecular evidence for reconstructing evolutionary trees. We introduce a novel, user-friendly software package engineered for conducting state-of-the-art Bayesian tree inferences on data sets of arbitrary size. Our software introduces a nonblocking parallelization of Metropolis-coupled chains, modifications for efficient analyses of data sets comprising thousands of partitions and memory saving techniques. We report on first experiences with Bayesian inferences at the whole-genome level using the SuperMUC supercomputer and simulated data.

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          Construction of phylogenetic trees.

          W. M. Fitch, E Margoliash (1967)
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            Some probabilistic and statistical problems on the analysis of DNA sequence

            S Tavare, Tavaré S., Tavaré … (1986)
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              Efficiency of Markov chain Monte Carlo tree proposals in Bayesian phylogenetics.

              Fredrik Ronquist, Clemens Lakner, Paul van Helden … (2008)
              The main limiting factor in Bayesian MCMC analysis of phylogeny is typically the efficiency with which topology proposals sample tree space. Here we evaluate the performance of seven different proposal mechanisms, including most of those used in current Bayesian phylogenetics software. We sampled 12 empirical nucleotide data sets--ranging in size from 27 to 71 taxa and from 378 to 2,520 sites--under difficult conditions: short runs, no Metropolis-coupling, and an oversimplified substitution model producing difficult tree spaces (Jukes Cantor with equal site rates). Convergence was assessed by comparison to reference samples obtained from multiple Metropolis-coupled runs. We find that proposals producing topology changes as a side effect of branch length changes (LOCAL and Continuous Change) consistently perform worse than those involving stochastic branch rearrangements (nearest neighbor interchange, subtree pruning and regrafting, tree bisection and reconnection, or subtree swapping). Among the latter, moves that use an extension mechanism to mix local with more distant rearrangements show better overall performance than those involving only local or only random rearrangements. Moves with only local rearrangements tend to mix well but have long burn-in periods, whereas moves with random rearrangements often show the reverse pattern. Combinations of moves tend to perform better than single moves. The time to convergence can be shortened considerably by starting with a good tree, but this comes at the cost of compromising convergence diagnostics based on overdispersed starting points. Our results have important implications for developers of Bayesian MCMC implementations and for the large group of users of Bayesian phylogenetics software.
              Article 0 comments Cited 63 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Mol Biol Evol
                Journal ID (iso-abbrev): Mol. Biol. Evol
                Journal ID (publisher-id): molbev
                Journal ID (hwp): molbiolevol
                Title: Molecular Biology and Evolution
                Publisher: Oxford University Press
                ISSN (Print): 0737-4038
                ISSN (Electronic): 1537-1719
                Publication date (Print): October 2014
                Publication date (Electronic): 18 August 2014
                Publication date PMC-release: 18 August 2014
                Volume: 31
                Issue: 10
                Pages: 2553-2556
                Affiliations
                1Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
                2Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany
                Author notes
                *Corresponding author: E-mail: andre.aberer@ 123456h-its.org .

                Associate editor: Xun Gu

                Article
                Publisher ID: msu236
                DOI: 10.1093/molbev/msu236
                PMC ID: 4166930
                PubMed ID: 25135941
                SO-VID: d3ffc651-3ac5-47e8-9a88-9f2d4bb1090b
                Copyright © © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Pages: 4
                Categories
                Subject: Fast Track

                ScienceOpen disciplines: Molecular biology
                Keywords: software,bayesian statistics,phylogenetic inference,whole-genome analyses,parallelization
                Data availability:
                ScienceOpen disciplines: Molecular biology
                Keywords: software, bayesian statistics, phylogenetic inference, whole-genome analyses, parallelization

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