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.


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.


Generous fundings for the IQ-TREE project are provided by the Austrian Science Fund - FWF (grants I 760-B17 from 2012-2015 and I 2508-B29 from 2016-2019), University of Vienna (Initiativkolleg I059-N from 2011-2014), Australian Research Council Discovery Project (DP200103151), and Chan-Zuckerberg Initiative (EOSS-0000000132).


James Barbetti

Contribution: Software engineering for COVID-19 data

Thomas Wong

Google Scholar

Contribution: ModelFinder 2

Michael Woodhams

Google Scholar

Contribution: Lie Markov models.

Robert Lanfear

Google Scholar

Contribution: Co-leader of IQ-TREE 2.

Bui Quang Minh

Google Scholar

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

Nhan Trong Ly

Google Scholar

Contribution: sequence simulations.

Olga Chernomor

Google Scholar

Contribution: Partition models and phylogenomic search.

Arndt von Haeseler

Google Scholar

Contribution: Inspiring ideas and advice.

Dominik Schrempf

Google Scholar

Contribution: Polymorphism-aware models (PoMo).

Heiko A. Schmidt

Google Scholar

Contribution: Integration of TREE-PUZZLE features.

Diep Thi Hoang

Google Scholar

Contribution: Improving ultrafast bootstrap.

Piyumal Demotte

Google Scholar

Contribution: Inferring time tree.


Lam Tung Nguyen

Contribution: Tree search algorithm.

Jana Trifinopoulos

Contribution: W-IQ-TREE web service.