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+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.