
Statistical models of evolutionModels of sequence evolution are used in molecular phylogenetics and comparative genomics to describe substitution patterns that occur during evolution. Progressively more realistic models of evolution are regularly developed, which improve the accuracy of phylogenetic tree estimation, allow functional information to be inferred from sequences alignments, and offer fascinating insights into how genomes evolve. The WAG model [publication]
The SDT model [publication]
If you want to try out the software used in this study please contact me. Note: this study also clarifies the relationship between models of codon and nucleotide substitution. Technical details Markov models of sequence evolution typically describe the rate that characters in the data, such as nucleotides or amino acids, replace each other using one (or more) instantaneous rate matrices. The values in the instantaneous rate matrix, Q, are described by a series of fixed prespecified values, a selection of free parameters that adapt to the observed data, or both. Models containing only fixed parameters are frequently called empirical models, while models with only free parameters are often called mechanistic models. The instantaneous rate matrix is used to calculate P(t) = e^{Qt}, whose elements p_{i,j}(t) describe the probability of character i replacing character j after time t. The P(t) matrix is required to perform likelihood calculations using Felsenstein's pruning algorithm. Likelihood can be used to optimise the parameters in a model, allowing biological inferences to be drawn from them. Alternatively, the likelihood function can be incorporated into a Bayesian inference framework. Good reviews and books on this subject include: 1. Inferring Phylogenies, Joe Felsenstein 2. Molecular phylogenetics: stateoftheart methods for looking into the past, Whelan et al. 3. Models of molecular evolution and phylogeny, Lió and Goldman.
 
Last modified: 22 May, 2007 