ExaBayes: massively parallel bayesian tree inference for the whole-genome era. 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 for this software
References in zbMATH (referenced in 2 articles )
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- Warnow, Tandy (ed.): Bioinformatics and phylogenetics. Seminal contributions of Bernard Moret (2019)
- Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)