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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
CodonTest: Modeling amino acid substitution preferences in coding sequences
PLoS Computational Biology, Volume 6, No. 8, Article e1000885, Year 2010
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Description
Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into K rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of K rate classes, where K is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes. © 2010 Delport et al.
Available Materials
https://efashare.b-cdn.net/share/pmc/articles/PMC2924240/bin/pcbi.1000885.s001.pdf
https://efashare.b-cdn.net/share/pmc/articles/PMC2924240/bin/pcbi.1000885.s002.pdf
https://efashare.b-cdn.net/share/pmc/articles/PMC2924240/bin/pcbi.1000885.s003.pdf
Authors & Co-Authors
Delport, Wayne
United States, La Jolla
Department of Pathology
Scheffler, Konrad
South Africa, Stellenbosch
Stellenbosch University
Botha, Gordon
South Africa, Stellenbosch
Stellenbosch University
Gravenor, Mike B.
United Kingdom, Swansea
Swansea University Medical School
Muse, Spencer V.
United States, Raleigh
Nc State University
Pond, Sergei L.Kosakovsky
United States, La Jolla
Department of Medicine
Statistics
Citations: 74
Authors: 6
Affiliations: 5
Identifiers
Doi:
10.1371/journal.pcbi.1000885
ISSN:
1553734X
e-ISSN:
15537358
Research Areas
Genetics And Genomics