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RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

机译:RAxML-VI-HPC:基于最大似然的系统发育分析,包含数千个分类单元和混合模型

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RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets >= 4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25 057 (1463 bp) and 2182 (51 089 bp) taxa, respectively.
机译:RAxML-VI-HPC(用于高性能计算的随机化加倍最大似然)是用于推理具有最大似然(ML)的大型系统发育的顺序和并行程序。通过进行低级技术优化,修改搜索算法以及使用GTR + CAT近似值替代GTR + Gamma,可以使程序比以前版本的RAxML快2.7至52倍。与GARLI,PHYML,IQPNNI和MrBayes进行的大规模性能比较(包含1000个至6722个分类单元的真实数据)显示,与最佳竞争程序(GARLI)相比,RAxML所需的主内存至少少5.6倍,并且在相似的时间内产生更好的树最多2500个分类单元的数据集。在> = 4000分类单元的数据集上,它的运行速度也比GARLI快2-3倍。 RAxML已与MPI并行化,以在不同的起始树上进行并行的多个引导和推断。该程序已用于计算迄今为止最大的两个比对中的ML树,它们分别包含2557个(1463 bp)和2182个(51089 bp)的分类单元。

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