MBE Advance Access published online on October 14, 2008
Molecular Biology and Evolution, doi:10.1093/molbev/msn232
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Investigating protein-coding sequence evolution with probabilistic codon substitution models
1 Institute of Computational Science, Swiss Federal Institute of Technology (ETHZ), Zurich, Switzerland
2 Swiss Institute of Bioinformatics, Lausanne, Switzerland
3 Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA
* Corresponding author: Dr Maria Anisimova, Institute of Computational Science, ETH Zurich, 6 Universitatstrasse, 8092 Zurich, Switzerland, Phone: +41 44 632 6076, Fax: +41 44 632 1374, Email: maria.anisimova{at}inf.ethz.ch
Received for publication July 28, 2008. Revision received September 15, 2008. Accepted for publication October 6, 2008.
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 view the codon evolution process in a more sophisticated way, relaxing several mathematical assumptions. These models make a greater use of physico-chemical 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.
Key Words: Markov model • maximum likelihood • Bayesian approach • codon evolution • positive selection
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