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

2014/08/18 by Andre J. Aberer, Kassian Kobert, Alexandros Stamatakis · 8 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #Artificial intelligence #Bayesian inference #Bayesian probability #Bioinformatics and Genomic Networks #Biology #Computational biology #Computer science #Genetic diversity and population structure #Genomics and Phylogenetic Studies #Inference #Massively parallel #Mathematics #Parallel computing #Programming language #Software #Supercomputer #Theoretical computer science #Tree (set theory)

paper · pdf · doi:10.1093/molbev/msu236

openalex publication_date 2014/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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.

Citations

• Broad phylogenomic sampling improves resolution of the animal tree of life
• Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: Approximate methods
• Monte Carlo sampling methods using Markov chains and their applications
• MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

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