Baele Guy, Ayres Daniel L, Rambaut Andrew, Suchard Marc A, Lemey Philippe
Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.
Methods Mol Biol. 2019;1910:691-722. doi: 10.1007/978-1-4939-9074-0_23.
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.
在本章中，我们聚焦于与统计系统发育基因组学相关的计算挑战，以及如何通过使用广义平台进化分析通用似然评估器（BEAGLE）来大幅减少系统发育基因组学和系统发育动力学分析中的计算时间。BEAGLE是一个用于似然计算的高性能库。我们讨论了BEAGLE库在各种先进的多核硬件上以及一系列常用进化模型上所带来的计算改进。对于不同维度的数据集，我们特别着重比较在多核中央处理器（CPU）和多种图形处理器（GPU）之间，通过抽样树的贝叶斯进化分析（BEAST）软件的性能。我们特别强调系统发育动力学领域的计算基准，该领域将系统发育基因组学的挑战与对与观测序列数据相关的性状数据建模的挑战结合在一起。总之，我们表明，对于越来越大的分子序列数据集，通过使用BEAGLE库，GPU可以带来巨大的计算进步，该库可用于贝叶斯推断和最大似然框架的软件包。
