RT Journal Article
SR Electronic
T1 Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 142323
DO 10.1101/142323
A1 Xiaofan Zhou
A1 Xingxing Shen
A1 Chris Todd Hittinger
A1 Antonis Rokas
YR 2017
UL http://biorxiv.org/content/early/2017/05/25/142323.abstract
AB Phylogenetics has witnessed dramatic increases in the sizes of data matrices assembled to resolve branches of the tree of life, motivating the development of programs for fast, yet accurate, inference. For example, several different fast programs have been developed in the very popular maximum likelihood framework, including RAxML/ExaML, PhyML, IQ-TREE, and FastTree. Although these four programs are widely used, a systematic evaluation and comparison of their performance using empirical genome-scale data matrices has so far been lacking. To address this question, we evaluated these four programs on 19 empirical phylogenomic data sets from diverse animal, plant, and fungal lineages with respect to likelihood maximization, topological accuracy, and computational speed. For single-gene tree inference, we found that the more exhaustive and slower strategy (ten RAxML searches per alignment) outperformed faster strategies (one tree search per alignment) using RAxML, PhyML, or IQ-TREE. Interestingly, single trees inferred by the three programs yielded comparable coalescent-based species tree estimations. For concatenation–based species tree inference, IQ-TREE consistently achieved the best-observed likelihoods for all data sets, and RAxML/ExaML was a close second. In contrast, PhyML often failed to complete concatenation-based analyses, whereas FastTree was the fastest but exhibited lower likelihood values and topological accuracy in both types of analyses. Finally, data matrix properties, such as the number of taxa and the information content, sometimes substantially influenced the relative performance of the programs. Our results provide real-world gene and species tree phylogenetic inference benchmarks to inform the design and execution of large-scale phylogenomic data analyses.