Journal Article

Investigating Protein-Coding Sequence Evolution with Probabilistic Codon Substitution Models

Maria Anisimova and Carolin Kosiol

in Molecular Biology and Evolution

Published on behalf of Society for Molecular Biology and Evolution

Volume 26, issue 2, pages 255-271
Published in print February 2009 | ISSN: 0737-4038
Published online October 2008 | e-ISSN: 1537-1719 | DOI:
Investigating Protein-Coding Sequence Evolution with Probabilistic Codon Substitution Models

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  • Evolutionary Biology
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This review is motivated by the true explosion in the number of recent studies both developing and ameliorating probabilistic models of codon evolution. Traditionally parametric, the first codon models focused on estimating the effects of selective pressure on the protein via an explicit parameter in the maximum likelihood framework. Likelihood ratio tests of nested codon models armed the biologists with powerful tools, which provided unambiguous evidence for positive selection in real data. This, in turn, triggered a new wave of methodological developments. The new generation of models views the codon evolution process in a more sophisticated way, relaxing several mathematical assumptions. These models make a greater use of physicochemical amino acid properties, genetic code machinery, and the large amounts of data from the public domain. The overview of the most recent advances on modeling codon evolution is presented here, and a wide range of their applications to real data is discussed. On the downside, availability of a large variety of models, each accounting for various biological factors, increases the margin for misinterpretation; the biological meaning of certain parameters may vary among models, and model selection procedures also deserve greater attention. Solid understanding of the modeling assumptions and their applicability is essential for successful statistical data analysis.

Keywords: Markov model; maximum likelihood; Bayesian approach; codon evolution; positive selection

Journal Article.  12300 words.  Illustrated.

Subjects: Evolutionary Biology ; Molecular and Cell Biology

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