IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies
Open accessAbstract
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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Preliminary analysis of commonly used nuclear and mitochondrial genes produced phylogenies with no statistical support for many key nodes.
“Preliminary analysis of commonly used nuclear and mitochondrial genes, using IQ-TREE 1.6 (Nguyen, 2015) for model selection and tree inference, produced phylogenies with no statistical support for many key nodes.”
From Enigmas no longer: using ultraconserved elements to place several unusual hawk taxa and address the non-monophyly of the genus <i>Accipiter</i> (Accipitriformes: Accipitridae) (2025) · cites this paper as Nguyen, 2015
Best-fit partitions and substitution models were inferred with ModelFinder as implemented in IQ-TREE 1.6.12 with 20% relaxed clustering.
“Best-fit partitions and substitution models were inferred with ModelFinder 12 , as implemented in IQ-TREE 1.6.12 2 , with 20% relaxed clustering”
From Available data do not rule out Ctenophora as the sister group to all other Metazoa (2023) · cites this paper as [2]
The high performance of IQ-TREE results from the efficient integration of novel phylogenetic methods that improve the three key steps in phylogenetic analysis: fast model selection via ModelFinder, an effective tree search algorithm, and a novel ultrafast bootstrap approximation.
“The high performance of IQ-TREE results from the efficient integration of novel phylogenetic methods that improve the three key steps in phylogenetic analysis: fast model selection via ModelFinder (Kalyaanamoorthy et al. 2017), an effective tree search algorithm (Nguyen et al. 2015), and a novel ultrafast bootstrap approximation (Minh et al. 2013; Hoang et al. 2018).”
From IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era (2020) · cites this paper as Nguyen et al. 2015
In prior studies, RAxML and PhyML showed largely similar performance in identifying trees of higher likelihood scores, whereas IQ-TREE exhibited improved efficiency compared with both RAxML and PhyML.
“In these studies, RAxML and PhyML showed largely similar performance in identifying trees of higher likelihood scores (Guindon et al. 2010; Money and Whelan 2012), whereas IQ-TREE exhibited improved efficiency compared with both RAxML and PhyML (Nguyen et al. 2015; Chernomor et al. 2016).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Nguyen et al. 2015
Our single-gene tree inference results are consistent with previous smaller-scale studies on the better efficiency of IQ-TREE relative to RAxML and PhyML and the inferior performance of FastTree in likelihood score maximization.
“Overall, our results at the level of single-gene tree inference are consistent with previous, smaller-scale studies on the better efficiency of IQ-TREE relative to RAxML and PhyML (all using one search per alignment) (Nguyen et al. 2015), and the inferior performance of FastTree in likelihood score maximization when compared with other programs (Guindon et al. 2010; Liu et al. 2011).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Nguyen et al. 2015
IQ-TREE uses NNI as its topological rearrangement mechanism, whereas RAxML and PhyML are both based on SPR.
“A potential explanation could be that IQ-TREE uses NNI as its topological rearrangement mechanism (Nguyen et al. 2015), whereas RAxML and PhyML are both based on SPR (Stamatakis 2006; Guindon et al. 2010).”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Nguyen et al. 2015
Some of the examined programs displayed different time efficiency on protein and DNA data sets.
“in agreement with previous studies (Guindon et al. 2010; Nguyen et al. 2015), we found that some programs displayed different time efficiency on protein and DNA data sets”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Nguyen et al. 2015
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- Authors
- Nguyen, L.-T., Schmidt, H. A., von Haeseler, A., & Minh, B. Q.
- Affiliations
- University of Vienna (AT)
- ORCID
- Schmidt, Heiko A.
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-11-03
- Volume
- 32
- Issue
- 1
- Pages
- 268-274
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- PMC
- PMC4271533
- OpenAlex
- W2111647009
- Cited by
- 26,046
- References
- 34
- Citations by year
- 2026: 2889 · 2025: 3709 · 2024: 4129 · 2023: 3524 · 2022: 3424 · 2021: 3244 · 2020: 2420 · 2019: 1466 · 2018: 720 · 2017: 319 · 2016: 147 · 2015: 32 · 2014: 2 · 2013: 1 · 2012: 4
- FWCI
- 68.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Molecular Biology and Evolution (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Austrian Science Fund (I760)
- Universität Wien (I059-N)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
- Keywords
- IQ-TREE, maximum likelihood phylogenetic inference, stochastic search, hill climbing, random perturbations, phylogenomics, stopping rules, RAxML, PhyML
- MeSH terms
- Algorithms, Phylogeny, Software, Time Factors, Likelihood Functions, Genome, Sequence Analysis, DNA, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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