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62 lines
3.1 KiB
Text
62 lines
3.1 KiB
Text
iqtree (IQ-TREE): Efficient and versatile phylogenomic software by
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maximum likelihood (ML)
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The IQ-TREE software was created as the successor of IQPNNI and TREE-
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PUZZLE (thus the name IQ-TREE). IQ-TREE was motivated by the rapid
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accumulation of phylogenomic data, leading to a need for efficient
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phylogenomic software that can handle a large amount of data and provide
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more complex models of sequence evolution. To this end, IQ-TREE can
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utilize multicore computers and distributed parallel computing to speed
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up the analysis. IQ-TREE automatically performs checkpointing to resume
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an interrupted analysis.
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As input IQ-TREE accepts all common sequence alignment formats including
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PHYLIP, FASTA, Nexus, Clustal and MSF. As output IQ-TREE will write a
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self-readable report file (name suffix .iqtree), a NEWICK tree file
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(.treefile) which can be visualized by tree viewer programs such as
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FigTree, Dendroscope or iTOL.
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Key features
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- Efficient search algorithm: Fast and effective stochastic algorithm to
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reconstruct phylogenetic trees by maximum likelihood. IQ-TREE compares
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favorably to RAxML and PhyML in terms of likelihood while requiring
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similar amount of computing time.
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- Ultrafast bootstrap: An ultrafast bootstrap approximation (UFBoot) to
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assess branch supports. UFBoot is 10 to 40 times faster than RAxML
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rapid bootstrap and obtains less biased support values.
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- Ultrafast model selection: An ultrafast and automatic model selection
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(ModelFinder) which is 10 to 100 times faster than jModelTest and
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ProtTest. ModelFinder also finds best-fit partitioning scheme like
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PartitionFinder.
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- Big Data Analysis: Supporting huge datasets with thousands of
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sequences or millions of alignment sites via checkpointing, safe
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numerical and low memory mode. Multicore CPUs and parallel MPI system
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are utilized to speedup analysis.
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- Phylogenetic testing: Several fast branch tests like SH-aLRT and a
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Bayes test and tree topology tests like the approximately unbiased
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(AU) test.
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The strength of IQ-TREE is the availability of a wide variety of
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phylogenetic models:
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- Common models: All common substitution models for DNA, protein, codon,
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binary and morphological data with rate heterogeneity among sites and
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ascertainment bias correction for e.g. SNP data.
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- Partition models: Allowing individual models for different genomic
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loci (e.g. genes or codon positions), mixed data types, mixed rate
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heterogeneity types, linked or unlinked branch lengths between
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partitions.
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- Mixture models: fully customizable mixture models and empirical
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protein mixture models and.
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- Polymorphism-aware models: Accounting for incomplete lineage sorting
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to infer species tree from genome-wide population data.
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CITING:
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To maintain IQ-TREE, support users and secure fundings, it is important
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that you cite the papers, whenever the corresponding features were
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applied for your analysis. Note that the paper of Nguyen et al. (2015)
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only described the tree search algorithm. Thus, it is not enough to only
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cite this paper if you, for example, use partition models, where
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Chernomor et al. (2016) should be cited.
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Check the "References" file in the package doc folder, as well as, the
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program's web-page.
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