Many-core algorithms for statistical phylogenetics
Many-core algorithms for statistical phylogenetics
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DOI：
10.1093/bioinformatics/btp244
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发表时间：
2009-06-01
期刊：
影响因子：
5.8
通讯作者：
Rambaut, Andrew
中科院分区：
文献类型：
--
作者：
Suchard, Marc A.;Rambaut, Andrew
动机：统计遗传学是计算密集型的，导致相当多的注意力集中在并行化技术上。基于密码子的模型允许独立的同义和替换替换率，并有可能更充分地模拟蛋白质编码序列进化的过程，从而提高系统发育的准确性。不幸的是，由于大量的密码子状态，计算负担在很大程度上阻碍了密码子模型下的系统发育重建，特别是在基因组规模。在这里，我们描述了用于在图形处理单元（GPU）上评估任意分子进化模型下的遗传性的新算法和方法，利用大量的处理核心来有效地并行计算，即使是对于大的状态大小模型。我们在现有的贝叶斯框架中实现了该方法，并将该算法应用于估计62个完整的食肉动物线粒体基因组的遗传学。60态密码子模型。我们看到，与优化的基于CPU的计算相比，速度提高了近90倍，与目前可用的实现相比，速度提高了> 140倍，这使其成为密码子模型在整个线粒体或微生物基因组上进行系统发育推断的首次实际应用。
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.