High-Performance Computing in Bayesian Phylogenetics and Phylodynamics Using BEAGLE.
Adaptive Markov chain Monte Carlo
BEAGLE
BEAST
Bayesian phylogenetics
Data integration
Generalized linear model
High-performance computing
Multipartite data
Pathogen phylodynamics
Phylogenomics
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2019
2019
Historique:
entrez:
7
7
2019
pubmed:
7
7
2019
medline:
9
1
2020
Statut:
ppublish
Résumé
In this chapter, we focus on the computational challenges associated with statistical phylogenomics and how use of the broad-platform evolutionary analysis general likelihood evaluator (BEAGLE), a high-performance library for likelihood computation, can help to substantially reduce computation time in phylogenomic and phylodynamic analyses. We discuss computational improvements brought about by the BEAGLE library on a variety of state-of-the-art multicore hardware, and for a range of commonly used evolutionary models. For data sets of varying dimensions, we specifically focus on comparing performance in the Bayesian evolutionary analysis by sampling trees (BEAST) software between multicore central processing units (CPUs) and a wide range of graphics processing cards (GPUs). We put special emphasis on computational benchmarks from the field of phylodynamics, which combines the challenges of phylogenomics with those of modelling trait data associated with the observed sequence data. In conclusion, we show that for increasingly large molecular sequence data sets, GPUs can offer tremendous computational advancements through the use of the BEAGLE library, which is available for software packages for both Bayesian inference and maximum-likelihood frameworks.
Identifiants
pubmed: 31278682
doi: 10.1007/978-1-4939-9074-0_23
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
691-722Subventions
Organisme : NHGRI NIH HHS
ID : R01 HG006139
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI117011
Pays : United States