{"id":{"repo_id":"arizona-thes","oai_identifier":"oai:repository.arizona.edu:10150/289906"},"canonical_url":"https://search.dev.ndltd.org/etd/arizona-thes/oai:repository.arizona.edu:10150/289906","repository":{"repo_id":"arizona-thes","name":"University of Arizona","base_url":"https://repository.arizona.edu/oai/request"},"display":{"title":"Modeling evolution of protein coding DNA sequences","abstract":"We develop a new class of computationally feasible stochastic models for statistical analysis of genetic sequence evolution and inference of properties of the underlying substitution processes in the context of maximum likelihood framework. Existing models for evolution of protein coding sequences allow site to site variation in non-synonymous substitution rates, but assume that the rate of synonymous substitutions is constant for all sites. 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