Thanks to the recent advent of next-generation sequencing techniques, the amount of phylogenomic/transcriptomic data have been rapidly accumulated. This extremely facilitates resolving many "deep phylogenetic" questions in the tree of life. At the same time it poses major computational challenges to analyze such big data, where most phylogenetic software cannot handle. Moreover, there is a need to develop more complex probabilistic models to adequately capture realistic aspects of genomic sequence evolution.

This trends motivated us to develop the IQ-TREE software with a strong emphasis on phylogenomic inference. Our goals are:

  • Accuracy: Proposing novel computational methods that perform better than existing approaches.
  • Speed: Allowing fast analysis on big data sets and utilizing high performance computing platforms.
  • Flexibility: Facilitating the inclusion of new (phylogenomic) models and sequence data types.
  • Versatility: Implementing a broad range of commonly-used maximum likelihood analyses.

IQ-TREE has been developed since 2011 as open-source software under the GNU-GPL license. It is actively maintained by the core development team (see below) and a number of collabrators (see right).

The name IQ-TREE comes from the fact that it is the successor of IQPNNI and TREE-PUZZLE software.


If you have further questions, feedback, feature requests and bug reports, please sign up (if not done yet) and post a topic to IQ-TREE Google group.

The average response time is two working days.

Ongoing collaborations

Phylogenetic likelihood library

Further development of the phylogenetic likelihood library (Flouri et al., 2015). In collaboration with Alexandros Stamatakis and Tomas Flouri.

Ultrafast parsimony bootstrap

An ultrafast approximation method for parsimony bootstrap. In collaboration with Le Sy Vinh and Diep Thi Hoang.

Polymorphism Models (PoMo)

Efficient implementation of polymorphism aware model into IQ-TREE. In collaboration with Carolin Kosiol and Dominik Schrempf.


A new model selection procedure based on IQ-TREE code. In collaboration with Lars Jermiin and Thomas Wong.

Heterotachy models

Development of heterotachy models that allows for rate heterogeneity among lineages. In collaboration with Barbara Holland and Stephen Crotty.

Non-reversible and Lie Markov models

In collaboration with Michael Woodhams, Jeremy Sumner, and Michael Charleston.

Approximate mixture models

In collaboration with Huaichun Wang, Andrew Roger, Edward Susko.

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 partially funded by the Austrian Science Fund - FWF (grants I 760-B17 from 2012-2015 and I 2508-B29 from 2016-2019) and the University of Vienna (Initiativkolleg I059-N from 2011-2014).


Lam Tung Nguyen

Google Scholar

Contribution: Tree search algorithm and parallelization.

Olga Chernomor

Google Scholar

Contribution: Partition models and phylogenomic search.

Heiko A. Schmidt

Google Scholar

Contribution: Integration of TREE-PUZZLE features.

Jana Trifinopoulos

Contribution: W-IQ-TREE web service.

Bui Quang Minh

Google Scholar

Contribution: Team leader, software core, ultrafast bootstrap, model selection.

Dominik Schrempf

Google Scholar

Contribution: Polymorphism-aware models (PoMo).

Michael Woodhams

Google Scholar

Contribution: Lie Markov models.

Arndt von Haeseler

Google Scholar

Contribution: Advice, ideas and financial support.