Source: iqtree3
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Uploaders: Andreas Tille <tille@debian.org>,
           Kevin Murray <kdmfoss@gmail.com>
Section: science
Build-Depends: debhelper-compat (= 13),
               cmake,
               googletest,
               libboost-dev,
               libeigen3-dev,
               libncl-dev,
               libsimde-dev,
               libsprng2-dev,
               libyaml-cpp-dev,
               mpi-default-dev,
               zlib1g-dev,
               help2man,
               time,
               chrpath
Standards-Version: 4.7.4
Vcs-Browser: https://salsa.debian.org/med-team/iqtree3
Vcs-Git: https://salsa.debian.org/med-team/iqtree3.git
Homepage: http://www.iqtree.org
Rules-Requires-Root: no

Package: iqtree3
Architecture: amd64 arm64
Depends: ${shlibs:Depends},
         ${misc:Depends}
Description: efficient phylogenetic software by maximum likelihood; v3
 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 of IQ-TREE
 .
  - 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).                                                              
  - Accurate 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 (Kalyaanamoorthy et al., 2017).                             
  - Alignment simulation: A flexible simulator (AliSim) which allows to         
    simulate sequence alignments under more realistic models than               
    Seq-Gen and INDELible (Ly-Trong et al., 2023).                              
  - 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 (PoMo):                                           
    http://www.iqtree.org/doc/Polymorphism-Aware-Models