IQ-TREE

Efficient and versatile phylogenomic software by maximum likelihood http://www.iqtree.org

Introduction

The IQ-TREE software was created as the successor of IQPNNI and TREE-PUZZLE (thus the name IQ-TREE). IQ-TREE was motivated by the rapid accumulation of phylogenomic data, leading to a need for efficient phylogenomic software that can handle a large amount of data and provide more complex models of sequence evolution. To this end, IQ-TREE can utilize multicore computers and distributed parallel computing to speed up the analysis. IQ-TREE automatically performs checkpointing to resume an interrupted analysis.

As input IQ-TREE accepts all common sequence alignment formats including PHYLIP, FASTA, Nexus, Clustal and MSF. As output IQ-TREE will write a self-readable report file (name suffix .iqtree), a NEWICK tree file (.treefile) which can be visualized by tree viewer programs such as FigTree, Dendroscope or iTOL.

Key features of IQ-TREE

• Efficient search algorithm: Fast and effective stochastic algorithm to reconstruct phylogenetic trees by maximum likelihood. IQ-TREE compares favorably to RAxML and PhyML in terms of likelihood while requiring similar amount of computing time (Nguyen et al., 2015).
• Ultrafast bootstrap: An ultrafast bootstrap approximation (UFBoot) to assess branch supports. UFBoot is 10 to 40 times faster than RAxML rapid bootstrap and obtains less biased support values (Minh et al., 2013).
• Ultrafast model selection: An ultrafast and automatic model selection (ModelFinder) which is 10 to 100 times faster than jModelTest and ProtTest. ModelFinder also finds best-fit partitioning scheme like PartitionFinder (Kalyaanamoorthy et al., 2017).
• Phylogenetic testing: Several fast branch tests like SH-aLRT and aBayes test (Anisimova et al., 2011) and tree topology tests like the approximately unbiased (AU) test (Shimodaira, 2002).

The strength of IQ-TREE is the availability of a wide variety of phylogenetic models:

• Common models: All common substitution models for DNA, protein, codon, binary and morphological data with rate heterogeneity among sites and ascertainment bias correction for e.g. SNP data.
• Partition models: Allowing individual models for different genomic loci (e.g. genes or codon positions), mixed data types, mixed rate heterogeneity types, linked or unlinked branch lengths between partitions.
• Mixture Models: fully customizable mixture models and empirical protein mixture models and.
• Polymorphism-aware models (PoMo): http://www.iqtree.org/doc/Polymorphism-Aware-Models

IQ-TREE web service

For a quick start you can also try the IQ-TREE web server, which performs online computation using a dedicated computing cluster. It is very easy to use with as few as just 3 clicks! Try it out at:

• Vienna Bioinformatics Cluster: http://iqtree.cibiv.univie.ac.at
• CIPRES Gateway: https://www.phylo.org
• Los Alamos Laboratories: https://www.hiv.lanl.gov/content/sequence/IQTREE/iqtree.html

User support

Please refer to the user documentation and frequently asked questions. If you have further questions and feedback, please create a topic at Github discussions. For feature requests bug reports please post a topic at Github issues.

Citations

When using tree mixture models (MAST) please cite:

• T.K.F. Wong, C. Cherryh, A.G. Rodrigo, M.W. Hahn, B.Q. Minh, R. Lanfear (2024) MAST: Phylogenetic Inference with Mixtures Across Sites and Trees. Syst. Biol., in press. https://doi.org/10.1093/sysbio/syae008

When computing concordance factors please cite:

• Y.K. Mo, R. Lanfear, M.W. Hahn, B.Q. Minh (2023) Updated site concordance factors minimize effects of homoplasy and taxon sampling. Bioinformatics, 39:btac741. https://doi.org/10.1093/bioinformatics/btac741

When using AliSim to simulate alignments please cite:

• N. Ly-Trong, S. Naser-Khdour, R. Lanfear, B.Q. Minh (2022) AliSim: A Fast and Versatile Phylogenetic Sequence Simulator for the Genomic Era. Mol. Biol. Evol., 39:msac092. https://doi.org/10.1093/molbev/msac092

When estimating amino-acid Q matrix please cite:

• B.Q. Minh, C. Cao Dang, L.S. Vinh, R. Lanfear (2021) QMaker: Fast and accurate method to estimate empirical models of protein evolution. Syst. Biol., 70:1046–1060. https://doi.org/10.1093/sysbio/syab010

When using the heterotachy GHOST model "+H" please cite:

• S.M. Crotty, B.Q. Minh, N.G. Bean, B.R. Holland, J. Tuke, L.S. Jermiin, A. von Haeseler (2020) GHOST: Recovering Historical Signal from Heterotachously Evolved Sequence Alignments. Syst. Biol., 69:249-264. https://doi.org/10.1093/sysbio/syz051

When performing tree reconstruction please cite:

• B.Q. Minh, H.A. Schmidt, O. Chernomor, D. Schrempf, M.D. Woodhams, A. von Haeseler, R. Lanfear (2020) IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol., 37:1530-1534. https://doi.org/10.1093/molbev/msaa015

For the ultrafast bootstrap (UFBoot) please cite:

• D.T. Hoang, O. Chernomor, A. von Haeseler, B.Q. Minh, and L.S. Vinh (2018) UFBoot2: Improving the ultrafast bootstrap approximation. Mol. Biol. Evol., 35:518–522. https://doi.org/10.1093/molbev/msx281

When using posterior mean site frequency model (PMSF) please cite:

• H.C. Wang, B.Q. Minh, S. Susko, A.J. Roger (2018) Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation. Syst. Biol., 67:216–235. https://doi.org/10.1093/sysbio/syx068

When using ModelFinder please cite:

• S. Kalyaanamoorthy, B.Q. Minh, T.K.F. Wong, A. von Haeseler, L.S. Jermiin (2017) ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods, 14:587-589. https://doi.org/10.1038/nmeth.4285

When using partition models please cite:

• O. Chernomor, A. von Haeseler, B.Q. Minh (2016) Terrace aware data structure for phylogenomic inference from supermatrices. Syst. Biol., 65:997-1008. https://doi.org/10.1093/sysbio/syw037

When using polymorphism-aware models please cite:

• D. Schrempf, B.Q. Minh, N. De Maio, A. von Haeseler, C. Kosiol (2016) Reversible polymorphism-aware phylogenetic models and their application to tree inference. J. Theor. Biol., 407:362-370. https://doi.org/10.1016/j.jtbi.2016.07.042

When using IQ-TREE version 1 please cite:

• L. Nguyen, H.A. Schmidt, A. von Haeseler, B.Q. Minh (2015) IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Mol. Biol. and Evol., 32:268-274. https://doi.org/10.1093/molbev/msu300

Credits and Acknowledgements

Some parts of the code were taken from the following packages/libraries: Phylogenetic likelihood library, TREE-PUZZLE, BIONJ, Nexus Class Libary, Eigen library, SPRNG library, Zlib library, gzstream library, vectorclass library, GNU scientific library.

IQ-TREE was funded by the Austrian Science Fund - FWF (grant no. I 760-B17 from 2012-2015 and and I 2508-B29 from 2016-2019), the University of Vienna (Initiativkolleg I059-N), the Australian National University, Chan-Zuckerberg Initiative (open source software for science grants), Simons Foundation, Moore Foundation, and Australian Research Council.