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layout: userdoc title: "Introduction" author: AUTHOR date: DATE docid: 0 icon: info-circle doctype: tutorial tags:

• tutorial description: "" sections:
  • name: Why IQ-TREE? url: why-iq-tree
  • name: Key features url: key-features
  • name: Free web server url: free-web-server
  • name: User support url: user-support
  • name: Documentation url: documentation
  • name: How to cite IQ-TREE? url: how-to-cite-iq-tree
  • name: Development team url: development-team
  • name: Credits and acknowledgements url: credits-and-acknowledgements

---

Introduction

Why IQ-TREE?

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 and freely available at http://www.iqtree.org/ as open-source software under the GNU-GPL license version 2. It is actively maintained by the core development team (see below) and a number of collabrators.

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

Key features

• Efficient search algorithm: Fast and effective stochastic algorithm to reconstruct phylogenetic trees by maximum likelihood. IQ-TREE compares favorably to RAxML and PhyML in terms of likelihood while requiring similar amount of computing time (Nguyen et al., 2015).
• Ultrafast bootstrap: An ultrafast bootstrap approximation (UFBoot) to assess branch supports. UFBoot is 10 to 40 times faster than RAxML rapid bootstrap and obtains less biased support values (Minh et al., 2013).
• Ultrafast model selection: An ultrafast and automatic model selection (ModelFinder) which is 10 to 100 times faster than jModelTest and ProtTest. ModelFinder also finds best-fit partitioning scheme like PartitionFinder.
• 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.
• Phylogenetic testing: Several fast branch tests like SH-aLRT and aBayes test (Anisimova et al., 2011) and tree topology tests like the approximately unbiased (AU) test (Shimodaira, 2002).

The strength of IQ-TREE is the availability of a wide variety of phylogenetic models:

• Common models: All common substitution models for DNA, protein, codon, binary and morphological data with rate heterogeneity among sites and ascertainment bias correction for e.g. SNP data.
• Partition models: Allowing individual models for different genomic loci (e.g. genes or codon positions), mixed data types, mixed rate heterogeneity types, linked or unlinked branch lengths between partitions.
• Mixture models: fully customizable mixture models and empirical protein mixture models and.
• Polymorphism-aware models: Accounting for incomplete lineage sorting to infer species tree from genome-wide population data (Schrempf et al., 2016).

Free web server

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

User support

Please refer to the user documentation and frequently asked questions. If you have further 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 two working days.

Documentation

IQ-TREE has an extensive documentation with several tutorials and manual:

• Getting started guide: recommended for users who just downloaded IQ-TREE.
• Web Server Tutorial: A quick starting guide for the IQ-TREE Web Server.
• Beginner's tutorial: recommended for users starting to use IQ-TREE.
• Advanced tutorial: recommended for more experienced users who want to explore more features of IQ-TREE.
• Command Reference: Comprehensive documentation of command-line options available in IQ-TREE.
• Substitution Models: All common substitution models and usages.
• Complex Models: Complex models such as partition and mixture models.
• Polymorphism Aware Models: Polymorphism-aware phylogenetic Models (PoMo) related documentation.
• Compilation guide: for advanced users who wants to compile IQ-TREE from source code.
• Frequently asked questions (FAQ): recommended to have a look before you post a question in the IQ-TREE group.

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 in IQ-TREE (Chernomor et al., 2016; Nguyen et al., 2015).

If you performed the ultrafast bootstrap (UFBoot) please cite:

• D.T. Hoang, O. Chernomor, A. von Haeseler, B.Q. Minh, and L.S. Vinh (2017) UFBoot2: Improving the ultrafast bootstrap approximation. Mol. Biol. Evol., in press. DOI: 10.1093/molbev/msx281

If you used posterior mean site frequency model please cite:

• H.C. Wang, B.Q. Minh, S. Susko and A.J. Roger (2017) Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation. Syst. Biol., in press. DOI: 10.1093/sysbio/syx068

If you used ModelFinder please cite:

• 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, Nature Methods, 14:587–589. DOI: 10.1038/nmeth.4285

If you performed 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

If you used 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

If you used the 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

If you used the 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

Development team

IQ-TREE is actively developed by:

Bui Quang Minh, Team leader, Designs IQ-TREE software core, ultrafast bootstrap, model selection.

Lam Tung Nguyen, Developer, Designs and parallelizes IQ-TREE search algorithm.

Olga Chernomor, Developer, Designs partition models and phylogenomic tree search.

Heiko A. Schmidt, Developer, Integrates TREE-PUZZLE features into IQ-TREE.

Jana Trifinopoulos, Developer, Designs and maintains IQ-TREE web service.

Dominik Schrempf, Developer, Implements polymorphism-aware models (PoMo).

Arndt von Haeseler, Advisor, Provides advice, inspiring ideas and financial support.

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 (grant no. I760-B17 from 2012-2015 and and I 2508-B29 from 2016-2019) and the University of Vienna (Initiativkolleg I059-N).