Substitution models and distance estimation
When estimating the evolutionary distance between organisms, one needs
a model of how frequently different mutations occur in the DNA. Such models are
known as substitution models. Our Model Testing and Maximum
Likelihood Phylogeny tools currently support the five nucleotide substitution
models listed here:
- Jukes-Cantor [Jukes and Cantor, 1969]
- Felsenstein 81 [Felsenstein, 1981]
- Kimura 80 [Kimura, 1980]
- HKY [Hasegawa et al., 1985]
- GTR (also known as the REV model) [Yang, 1994a]
![$ C \to
A$](img46.gif)
![$ C \to C$](img47.gif)
![$ C \to G$](img48.gif)
The Jukes-Cantor and Kimura 80 models assume equal base frequencies and the HKY and GTR models allow the frequencies of the four bases to differ
(they will be estimated by the observed frequencies of the bases in
the alignment). In the Jukes-Cantor model all substitutions are assumed to occur
at equal rates, in the Kimura 80 and HKY models transition and transversion
rates are allowed to differ (substitution between two purines (
) or two pyrimidines (
) are
transitions and purine - pyrimidine substitutions are
transversions). The GTR model is the general time reversible model
that allows all substitutions to occur at different rates. For the
substitution rate matrices describing the substitution models we use
the parametrization of Yang [Yang, 1994a].
For protein sequences, our Maximum Likelihood Phylogeny tool supports four substitution models:
- Bishop-Friday [Bishop and Friday, 1985]
- Dayhoff (PAM) [Dayhoff et al., 1978]
- JTT [Jones et al., 1992]
- WAG [Whelan and Goldman, 2001]
The Bishop-Friday model assumes all amino acids occur with same frequency and that all substitutions are equally likely. This is the simplest model, but also the most unrealistic. The remaining three models use amino acid frequencies and substitution rates which have been determined from large scale experiments where huge sets of protein sequences have been aligned and rates have been estimated. These three models reflect the outcome of three different experiments. We recommend using WAG as these rates where estimated from the largest experiment.