Journal
MOLECULAR BIOLOGY AND EVOLUTION
Volume 32, Issue 1, Pages 268-274Publisher
OXFORD UNIV PRESS
DOI: 10.1093/molbev/msu300
Keywords
phylogenetic inference; phylogeny; maximum likelihood; stochastic algorithm
Funding
- Austrian Science Fund-FWF [I760-B17]
- University of Vienna [Initiativkolleg I059-N]
- Austrian Science Fund (FWF) [I 760] Funding Source: researchfish
Ask authors/readers for more resources
Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%. IQ-TREE is freely available at http://www.cibiv.at/software/iqtree.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available