Many-core algorithms for statistical phylogenetics

Motivation: Statistical phylogenetics is computationally intensive, resulting in considerable attention meted on techniques for parallelization. Codon-based models allow for independent rates of synonymous and replacement substitutions and have the potential to more adequately model the process of p...

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Vydané v:Bioinformatics Ročník 25; číslo 11; s. 1370 - 1376
Hlavní autori: Suchard, Marc A., Rambaut, Andrew
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Oxford University Press 01.06.2009
Oxford Publishing Limited (England)
Predmet:
Algorithms
Bayes Theorem
Biological and medical sciences
Carnivores
Codon - genetics
Computational Biology - methods
Evolution, Molecular
Fundamental and applied biological sciences. Psychology
General aspects
Genome, Mitochondrial - genetics
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Original Papers
Phylogeny
Cores
Algorithm
Phylogeny
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
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Shrnutí:Motivation: Statistical phylogenetics is computationally intensive, resulting in considerable attention meted on techniques for parallelization. Codon-based models allow for independent rates of synonymous and replacement substitutions and have the potential to more adequately model the process of protein-coding sequence evolution with a resulting increase in phylogenetic accuracy. Unfortunately, due to the high number of codon states, computational burden has largely thwarted phylogenetic reconstruction under codon models, particularly at the genomic-scale. Here, we describe novel algorithms and methods for evaluating phylogenies under arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the large number of processing cores to efficiently parallelize calculations even for large state-size models. Results: We implement the approach in an existing Bayesian framework and apply the algorithms to estimating the phylogeny of 62 complete mitochondrial genomes of carnivores under a 60-state codon model. We see a near 90-fold speed increase over an optimized CPU-based computation and a >140-fold increase over the currently available implementation, making this the first practical use of codon models for phylogenetic inference over whole mitochondrial or microorganism genomes. Availability and implementation: Source code provided in BEAGLE: Broad-platform Evolutionary Analysis General Likelihood Evaluator, a cross-platform/processor library for phylogenetic likelihood computation (http://beagle-lib.googlecode.com/). We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (http://beast.bio.ed.ac.uk/). Contact: msuchard@ucla.edu; a.rambaut@ed.ac.uk
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istex:FF81F7E0B612238FA72BCB805998948AB1D5CB45
Associate Editor: Martin Bishop
ArticleID:btp244
ark:/67375/HXZ-VN8P6M8W-N
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btp244