Efficient phylogenomic software by maximum likelihood

Latest standard version 1.5.5 released on June 2, 2017

IQ-TREE key features

IQ-TREE - Efficient Tree Reconstruction

A fast and effective stochastic algorithm to infer phylogenetic trees by maximum likelihood. IQ-TREE compares favorably to RAxML and PhyML in terms of likelihoods with similar computing time (Nguyen et al., 2015).

ModelFinder - Fast and Accurate Model Selection

ModelFinder (Kalyaanamoorthy et al., 2017) enables a free rate variation model and is 10 to 100 times faster than jModelTest and ProtTest. It also finds best-fit partitioning scheme like PartitionFinder.

UFBoot - Ultrafast Bootstrap Approximation

UFBoot provides approximately unbiased branch support values and runs 100X faster than nonparametric bootstrap and 10 to 40 times faster than RAxML rapid bootstrap (Minh et al., 2013).

Big Data Analysis

Supporting huge datasets with thousands of sequences or millions of alignment sites via checkpointing, safe numerical and low memory mode. Multicore CPUs and parallel MPI system are utilized to speedup analysis.

IQ-TREE supports a wide range of evolutionary models

Common Models

All common substitution models for DNA, protein, codon, binary and morphological data with rate heterogeneity among sites.

Partition Models

Phylogenomic partition models allowing for mixed data types, mixed rate heterogeneity types, linked or unlinked branch lengths.

Mixture Models

Mixture models such as empirical protein mixture models and customizable mixture models.

IQ-TREE is user-friendly and well-documented

Online Web Service

IQ-TREE web server for online computations. It is very easy to use with as few as just 3 clicks!

User Support

Please refer to Frequently Asked Questions. For further feedback and bug reports, please sign up and post a topic to IQ-TREE Google group.

The average response time is two working days.

User Documentation

User guide, tutorial and extensive documentation for how to use IQ-TREE.

What's new in version 1.6?

During the beta-testing phase, feedback is much appreciated!

How to cite IQ-TREE?

To maintain IQ-TREE, support users and secure fundings, it is important for us that you cite the following papers, whenever the corresponding features were applied for your analysis.

Example 1: "...We obtained branch supports with the ultrafast bootstrap (Minh et al. 2013) implemented in the IQ-TREE software (Nguyen et al. 2015)..."

Example 2: "...We inferred the maximum-likelihood tree using the edge-linked partition model (Chernomor et al. 2016)..."

When using model selection please cite: New!

S. Kalyaanamoorthy, B.Q. Minh, T.K.F. Wong, A. von Haeseler, and L.S. Jermiin (2017) ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates. Nat. Methods, 14:587–589.
DOI: 10.1038/nmeth.4285

When using partition models e.g., for phylogenomic analysis please cite:

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

When performing tree reconstruction, please cite:

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

When using polymorphism-aware models please cite:

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

When performing 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

When using IQ-TREE web server please cite:

J. Trifinopoulos, L.-T. Nguyen, A. von Haeseler, and B.Q. Minh (2016) W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res., 44 (W1):W232-W235.
DOI: 10.1093/nar/gkw256