iqtree (IQ-TREE): Efficient and versatile phylogenomic software by
maximum likelihood (ML)
The IQ-TREE software was created as the successor of IQPNNI and TREE-
PUZZLE (thus the name IQ-TREE). IQ-TREE was motivated by the rapid
accumulation of phylogenomic data, leading to a need for efficient
phylogenomic software that can handle a large amount of data and provide
more complex models of sequence evolution. To this end, IQ-TREE can
utilize multicore computers and distributed parallel computing to speed
up the analysis. IQ-TREE automatically performs checkpointing to resume
an interrupted analysis.
As input IQ-TREE accepts all common sequence alignment formats including
PHYLIP, FASTA, Nexus, Clustal and MSF. As output IQ-TREE will write a
self-readable report file (name suffix .iqtree), a NEWICK tree file
(.treefile) which can be visualized by tree viewer programs such as
FigTree, Dendroscope or iTOL.
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.
- 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.
- 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 a
Bayes test and tree topology tests like the approximately unbiased
(AU) test.
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.
CITING:
To maintain IQ-TREE, support users and secure fundings, it is important
that you cite the papers, whenever the corresponding features were
applied for your analysis. Note that the paper of Nguyen et al. (2015)
only described the tree search algorithm. Thus, it is not enough to only
cite this paper if you, for example, use partition models, where
Chernomor et al. (2016) should be cited.
Check the "References" file in the package doc folder, as well as, the
program's web-page.
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