title: ExaBayes: massively parallel bayesian tree inference for the whole-genome era.
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.
authors.affiliation: Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany andre.aberer@h-its.org.
authors.forename: Andre J
authors.initials: AJ
authors.lastname: Aberer
authors.affiliation: Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.
authors.forename: Kassian
authors.initials: K
authors.lastname: Kobert
authors.affiliation: Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.
authors.forename: Alexandros
authors.initials: A
authors.lastname: Stamatakis
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D016678:Genome; D008957:Models, Genetic; D010802:Phylogeny; D016415:Sequence Alignment; D012984:Software
keywords: Bayesian statistics; parallelization; phylogenetic inference; software; whole-genome analyses