title: Pseudo-likelihood analysis of codon substitution models with neighbor-dependent rates.
abstract: Recently, Markov processes for the evolution of coding DNA with neighbor dependence in the instantaneous substitution rates have been considered. The neighbor dependency makes the models analytically intractable, and previously Markov chain Monte Carlo methods have been used for statistical inference. Using a pseudo-likelihood idea, we introduce in this paper an approximative estimation method which is fast to compute. The pseudo-likelihood estimates are shown to be very accurate, and from analyzing 348 human-mouse coding sequences we conclude that the incorporation of a CpG effect improves the fit of the model considerably.
authors.affiliation: Bioinformatics Research Center, University of Aarhus, Denmark. olefc@birc.au.dk
authors.forename: Ole F
authors.initials: OF
authors.lastname: Christensen
authors.forename: Asger
authors.initials: A
authors.lastname: Hobolth
authors.forename: Jens L
authors.initials: JL
authors.lastname: Jensen
mesh_terms: D000465:Algorithms; D019943:Amino Acid Substitution; D000818:Animals; D003062:Codon / Q000235:genetics*; D019295:Computational Biology; D018899:CpG Islands; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D051379:Mice; D008957:Models, Genetic; D009010:Monte Carlo Method; D011506:Proteins / Q000235:genetics; D016415:Sequence Alignment / Q000706:statistics & numerical data
chemical_list: D003062:Codon; D011506:Proteins