IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

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...

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Veröffentlicht in:Molecular biology and evolution Jg. 32; H. 1; S. 268 - 274
Hauptverfasser: Nguyen, Lam-Tung, Schmidt, Heiko A., von Haeseler, Arndt, Minh, Bui Quang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Oxford University Press 01.01.2015
Schlagworte:
Algorithms
Computational Biology - methods
Genome
Genomics
Heuristic
Intelligence
Likelihood Functions
Maximum likelihood method
Methods
Perturbation methods
Phylogenetics
Phylogeny
Proteins
Sequence Analysis, DNA
Software
Stochastic models
Time Factors
Trees
phylogeny
phylogenetic inference
stochastic algorithm
maximum likelihood
ISSN:0737-4038, 1537-1719, 1537-1719
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Zusammenfassung: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.
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Associate editor: Barbara Holland
ISSN:0737-4038
1537-1719
1537-1719
DOI:10.1093/molbev/msu300