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Bui Quang Minh edited this page Nov 6, 2015
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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 (see Nguyen et al., 2015)
- An ultrafast bootstrap approximation to assess branch supports (see 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 for DNA, protein, codon, binary and morphological data.
- Rate heterogeneity among sites including invariable site [+I] model, discrete Gamma [+G], and FreeRate model [+R].
- Phylogenomic partition models allowing for mixed data types between partitions, linked or unlinked branch lengths, and different rate types (e.g. one partition under GTR+G and another under WAG+I+G).
- Mixture models such as predefined protein mixture models (e.g., LG4X, CAT C10-C60), customizable mixture models (e.g., "MIX{HKY,GTR}"), and frequency/profile mixture models.
- Ascertainment bias correction [+ASC] model for data where constant sites are missing (e.g., SNPs or morphological data).
- New models can be defined and imported via a NEXUS file (see Manual).
Please read carefully before using IQ-TREE the first time or upgrading a new version!
User Manual and Tutorial 1.0
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
The average response time is one working day.
Copyright (c) 2010-2022 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
- Estimating amino acid substitution models
- DNA models
- Protein models
- 3Di and TEA 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