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. 2014 Oct;31(10):2553-6.
doi: 10.1093/molbev/msu236. Epub 2014 Aug 18.

ExaBayes: massively parallel bayesian tree inference for the whole-genome era

Andre J Aberer  1 , Kassian Kobert  2 , Alexandros Stamatakis  3
Affiliations

ExaBayes: massively parallel bayesian tree inference for the whole-genome era

Andre J Aberer et al. Mol Biol Evol. 2014 Oct.

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.

Keywords: Bayesian statistics; parallelization; phylogenetic inference; software; whole-genome analyses.

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Figures

F<sc>ig</sc>. 1.
Fig. 1.
The three layers of parallelism employed by ExaBayes (distributed likelihood evaluation, distributed Metropolis-coupled chains, and distributed independent analyses).
F<sc>ig</sc>. 2.
Fig. 2.
Scaling factor (sequential runtime divided by parallel runtime) and efficiency (scaling factor divided by number of processes) for executing ExaBayes on 256 cores up to 32,768 cores on a 200 species alignment with 500,000 characters.
F<sc>ig</sc>. 3.
Fig. 3.
Inference from genome-sized data: 2D-rescaled representation employing multidimensional scaling (MDS) of Robinson–Foulds distances among sampled trees for chains with varying amount of data. The position of the simulated true tree is shown in red. The applied MDS algorithm maps identical trees to adjacent nonidentical positions (i.e., overlapping squares represent identical trees).
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