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Substitution Models

minh edited this page Nov 9, 2015 · 78 revisions

IQ-TREE supports a wide range of substitution models, including advanced partition and mixture models. This guide gives a detailed information of all available models.

DNA models

IQ-TREE includes all common DNA models (ordered by complexity):

  • JC or JC69: equal rates and equal base frequencies ([Jukes and Cantor, 1969]).
  • F81: equal rates but unequal base freq. ([Felsenstein, 1981]).
  • K80 or K2P: unequal transition/transversion rates and equal base freq. ([Kimura, 1980]).
  • HKY or HKY85: Like K80 but unequal base freq. ([Hasegawa, Kishino and Yano, 1985]).
  • TN or TN93: Like HKY but unequal purine/pyrimidine rates ([Tamura and Nei, 1993]).
  • TNe: Like TN but equal base freq.
  • K81 or K3P: three substitution types model and equal base freq. ([Kimura, 1981]).
  • K81u: Like K81 but unequal base freq.
  • TPM2: AC=AT, AG=CT, CG=GT and equal base freq.
  • TPM2u: Like TPM2 but unequal base freq.
  • TPM3: AC=CG, AG=CT, AT=GT and equal base freq.
  • TPM3u: Like TPM3 but unequal base freq.
  • TIM: transition model, AC=GT, AT=CG and unequal base freq.
  • TIMe: Like TIM but equal base freq.
  • TIM2: AC=AT, CG=GT and unequal base freq.
  • TIM2e: Like TIM2 but equal base freq.
  • TIM3: AC=CG, AT=GT and unequal base freq.
  • TIM3e: Like TIM3 but equal base freq.
  • TVM: transversion model, AG=CT and unequal base freq.
  • TVMe: Like TVM but equal base freq.
  • SYM: Symmetric model with unequal rates and equal base freq. ([Zharkihk, 1994]).
  • GTR: General time reversible model with unequal rates and unequal base freq. ([Tavare, 1986]).

Moreover, IQ-TREE supports arbitrarily restricted DNA model via a 6-digit code. The 6 digits define the equality for 6 nucleotide substitution types: A-C, A-G, A-T, C-G, C-T and G-T. 010010 means that A-G rate is equal to C-T rate and the remaining four substitution rates are equal. Thus, 010010 is equivalent to K80 or HKY model (depending on whether base frequencies are equal or not). 123450 is equivalent to GTR or SYM model as there is no restriction defined by such 6-digit code.

If users want to fix model parameters, append the model name with a curly bracket {, followed by the comma-separated rate parameters, and a closing curly bracket }. For example, GTR{1.0,2.0,1.5,3.7,2.8,1.0} is a valid model.

Users can also specify three different kinds of base frequencies:

  • +F: empirical base frequencies. This is the default if model has unequal base freq.
  • +FQ: equal base frequencies.
  • +FO: optimized base frequencies by maximum-likelihood.

For example, GTR+FO optimizes base frequencies by ML whereas GTR+F (default) counts base frequencies from directly the alignment.

Finally, users can fix base frequencies with e.g. GTR+F{0.1,0.2,0.3,0.4} to fix the corresponding frequencies of A, C, G and T (must sum up to 1.0).

Protein models

IQ-TREE supports all common empirical amino-acid exchange rate matrices:

  • Blosum62: BLOcks SUbstitution Matrix ([Henikoff and Henikoff, 1992]), although not recommended.
  • cpREV: chloroplast matrix (Adachi et al., 2000)
  • Dayhoff: ([Dayhoff et al., 1978]).
  • DCMut: ([Kosiol and Goldman, 2005]).
  • FLU: (Dang et al., 2010).
  • HIVb: (Dang et al., 2010).
  • HIVw: (Dang et al., 2010).
  • JTT: ([Jones et al., 1992]).
  • JTTDCMut:
  • LG: ([Le and Gascuel, 2008]).
  • mtART: (Abascal et al., 2007).
  • mtMAM: ([Yang et al., 1998]).
  • mtREV: (Adachi and Hasegawa, 1996).
  • mtZOA: ([Rota-Stabelli et al., 2009]).
  • PMB: ([Veerassamy et al., 2004]).
  • rtREV: ([Dimmic et al., 2002]).
  • VT: ([Mueller and Vingron, 2000]).
  • WAG: ([Whelan and Goldman, 2001]).

Codon models

"MG", "MGK", "GY", "KOSI07", "SCHN05"

Binary and morphological models

"JC2", "GTR2" {"MK", "ORDERED"}

Ascertainment bias correction

Rate heterogeneity across sites

Partition models

Mixture models

Customized models

[Dayhoff et al., 1978]: [Dimmic et al., 2002]: http://dx.doi.org/10.1007/s00239-001-2304-y [Felsenstein, 1981]: https://dx.doi.org/10.1007%2FBF01734359 [Hasegawa, Kishino and Yano, 1985]: https://dx.doi.org/10.1007%2FBF02101694 [Henikoff and Henikoff, 1992]: https://dx.doi.org/10.1073%2Fpnas.89.22.10915 [Jones et al., 1992]: https://dx.doi.org/10.1093%2Fbioinformatics%2F8.3.275 [Jukes and Cantor, 1969]: https://books.google.at/books?hl=en&lr=&id=FDHLBAAAQBAJ&oi=fnd&pg=PA21&ots=bkfqSDR2jB&sig=zxqY3TXK5UuKVU2ndxjm_VnD4B0&redir_esc=y#v=onepage&q&f=false [Kimura, 1980]: http://dx.doi.org/10.1007%2FBF01731581 [Kimura, 1981]: http://dx.doi.org/10.1073/pnas.78.1.454 [Kosiol and Goldman, 2005]: http://dx.doi.org/10.1093/molbev/msi005 [Le and Gascuel, 2008]: http://dx.doi.org/10.1093/molbev/msn067 [Mueller and Vingron, 2000]: http://dx.doi.org/10.1089/10665270050514918 [Rota-Stabelli et al., 2009]: http://dx.doi.org/10.1016/j.ympev.2009.01.011 [Tamura and Nei, 1993]: http://mbe.oxfordjournals.org/cgi/content/abstract/10/3/512 [Tavare, 1986]: http://www.damtp.cam.ac.uk/user/st321/CV_&_Publications_files/STpapers-pdf/T86.pdf [Veerassamy et al., 2004]: http://dx.doi.org/10.1089/106652703322756195 [Whelan and Goldman, 2001]: http://dx.doi.org/10.1093/oxfordjournals.molbev.a003851 [Yang et al., 1998]: http://mbe.oxfordjournals.org/content/15/12/1600.abstract [Zharkihk, 1994]: http://dx.doi.org/10.1007/BF00160155

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