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IQ-TREE is a very efficient maximum likelihood phylogenetic software with following key features among others:
- A novel fast and effective stochastic algorithm to estimate maximum likelihood trees. IQ-TREE outperforms both RAxML and PhyML in terms of likelihood while requiring similar amount of computing time (Nguyen et al., 2015)
- An ultrafast bootstrap approximation to assess branch supports (Minh et al., 2013).
- Ultrafast and automatic model selection (10 to 100 times faster than jModelTest and ProtTest) and best partitioning scheme selection (like PartitionFinder).
The strength of IQ-TREE is the availability of a wide range of models:
- All common [substitution models](Substitution Models) for DNA, protein, codon, binary and morphological data with possibility of [rate heterogeneity among sites](Substitution Models#rate-heterogeneity-across-sites) and [ascertainment bias correction](Substitution Models#ascertainment-bias-correction).
- [Phylogenomic partition models](Complex Models#partition-models) allowing for mixed data types, linked or unlinked branch lengths, and different rate types.
- Mixture models such as [empirical protein mixture models](Substitution Models#protein-models) and [customizable mixture models](Complex Models#mixture-models).
The latest IQ-TREE version 1.3.10 (October 16, 2015) is available for three popular platforms with a sequential and a parallel multicore version:
Please follow Getting started guide once you downloaded IQ-TREE.
See Release notes for more details of this version or to download older versions.
If you want to obtain and build IQ-TREE source code, please refer to Compilation guide.
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
http://iqtree.cibiv.univie.ac.at
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Getting started guide: recommended for users who just downloaded IQ-TREE.
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Beginner's tutorial: recommended for users starting to use IQ-TREE.
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[Advanced tutorial](Advanced Tutorial): recommended for more experienced users who want to explore more features of IQ-TREE.
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Substitution Models and Complex Models: learn more about maximum-likelihood models available in IQ-TREE.
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Frequently asked questions (FAQ): recommended to have a look before you post a question in the IQ-TREE group.
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[Compilation guide](Compilation guide): for advanced users who wants to compile IQ-TREE from source code.
If you have questions, feedback, feature requests, and bug reports, please sign up the following Google group (if not done yet) and post a topic to the
https://groups.google.com/d/forum/iqtree
The average response time is one working day.
To cite IQ-TREE please use:
- L.-T. Nguyen, H.A. Schmidt, A. von Haeseler, and B.Q. Minh (2015) IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol. Biol. Evol., 32, 268-274. DOI: 10.1093/molbev/msu300
For the ultrafast bootstrap (UFBoot) please cite:
- B.Q. Minh, M.A.T. Nguyen, and A. von Haeseler (2013) Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol., 30:1188-1195. DOI: 10.1093/molbev/mst024
IQ-TREE can use PLL for likelihood computations, if you use -pll
option please cite:
- T. Flouri, F. Izquierdo-Carrasco, D. Darriba, A.J. Aberer, L.-T. Nguyen, B.Q. Minh, A. von Haeseler, and A. Stamatakis (2015) The phylogenetic likelihood library. Syst. Biol., 64:356-362. DOI: 10.1093/sysbio/syu084
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, vectorclass library.
IQ-TREE was partially funded by the Austrian Science Fund - FWF (grant no. I760-B17 from 2012-2015) and the University of Vienna (Initiativkolleg I059-N).
Copyright (c) 2010-2016 IQ-TREE development team.
- First example
- Model selection
- New model selection
- Codon models
- Binary, Morphological, SNPs
- Ultrafast bootstrap
- Nonparametric bootstrap
- Single branch tests
- Partitioned analysis
- Partitioning with mixed data
- Partition scheme selection
- Bootstrapping partition model
- Utilizing multi-core CPUs
- Tree topology tests
- User-defined models
- Consensus construction and bootstrap value assignment
- Computing Robinson-Foulds distance
- Generating random trees
- DNA models
- Protein models
- Codon models
- Binary, morphological models
- Ascertainment bias correction
- Rate heterogeneity
- Counts files
- First running example
- Substitution models
- Virtual population size
- Sampling method
- Bootstrap branch support
- Interpretation of branch lengths