Literature DB >> 14668405

The problem of counting sites in the estimation of the synonymous and nonsynonymous substitution rates: implications for the correlation between the synonymous substitution rate and codon usage bias.

Nicolas Bierne1, Adam Eyre-Walker.   

Abstract

Most methods for estimating the rate of synonymous and nonsynonymous substitution per site define a site as a mutational opportunity: the proportion of sites that are synonymous is equal to the proportion of mutations that would be synonymous under the model of evolution being considered. Here we demonstrate that this definition of a site can give misleading results and that a physical definition of site should be used in some circumstances. We illustrate our point by reexamining the relationship between codon usage bias and the synonymous substitution rate. It has recently been shown that the rate of synonymous substitution, calculated using the Goldman-Yang method, which encapsulates the mutational-opportunity definition of a site at a high level of sophistication, is either positively correlated or uncorrelated to synonymous codon bias in Drosophila. Using other methods, which account for synonymous codon bias but define a site physically, we show that there is a negative correlation between the synonymous substitution rate and codon bias and that the lack of a negative correlation using the Goldman-Yang method is due to the way in which the number of synonymous sites is counted. We also show that there is a positive correlation between the synonymous substitution rate and third position GC content in mammals, but that the relationship is considerably weaker than that obtained using the Goldman-Yang method. We argue that the Goldman-Yang method is misleading in this context and conclude that methods that rely on a mutational-opportunity definition of a site should be used with caution.

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Year:  2003        PMID: 14668405      PMCID: PMC1462865     

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  28 in total

1.  Rates of nucleotide substitution and mammalian nuclear gene evolution. Approximate and maximum-likelihood methods lead to different conclusions.

Authors:  J P Bielawski; K A Dunn; Z Yang
Journal:  Genetics       Date:  2000-11       Impact factor: 4.562

2.  Molecular evolution of nuclear genes in Cupressacea, a group of conifer trees.

Authors:  Junko Kusumi; Yoshihiko Tsumura; Hiroshi Yoshimaru; Hidenori Tachida
Journal:  Mol Biol Evol       Date:  2002-05       Impact factor: 16.240

3.  New methods for estimating the numbers of synonymous and nonsynonymous substitutions.

Authors:  Y Ina
Journal:  J Mol Evol       Date:  1995-02       Impact factor: 2.395

4.  A method for estimating the numbers of synonymous and nonsynonymous substitutions per site.

Authors:  J M Comeron
Journal:  J Mol Evol       Date:  1995-12       Impact factor: 2.395

5.  Estimating synonymous and nonsynonymous substitution rates.

Authors:  S V Muse
Journal:  Mol Biol Evol       Date:  1996-01       Impact factor: 16.240

6.  The evolution of genes: the chicken preproinsulin gene.

Authors:  F Perler; A Efstratiadis; P Lomedico; W Gilbert; R Kolodner; J Dodgson
Journal:  Cell       Date:  1980-06       Impact factor: 41.582

7.  PAML: a program package for phylogenetic analysis by maximum likelihood.

Authors:  Z Yang
Journal:  Comput Appl Biosci       Date:  1997-10

8.  Substitution rates in Drosophila nuclear genes: implications for translational selection.

Authors:  K A Dunn; J P Bielawski; Z Yang
Journal:  Genetics       Date:  2001-01       Impact factor: 4.562

9.  Linkage limits the power of natural selection in Drosophila.

Authors:  Andrea J Betancourt; Daven C Presgraves
Journal:  Proc Natl Acad Sci U S A       Date:  2002-10-07       Impact factor: 11.205

10.  Synonymous substitution-rate constants in Escherichia coli and Salmonella typhimurium and their relationship to gene expression and selection pressure.

Authors:  O G Berg; M Martelius
Journal:  J Mol Evol       Date:  1995-10       Impact factor: 2.395

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  37 in total

1.  Quantifying the variation in the effective population size within a genome.

Authors:  Toni I Gossmann; Megan Woolfit; Adam Eyre-Walker
Journal:  Genetics       Date:  2011-09-27       Impact factor: 4.562

2.  Detecting positive and purifying selection at synonymous sites in yeast and worm.

Authors:  Tong Zhou; Wanjun Gu; Claus O Wilke
Journal:  Mol Biol Evol       Date:  2010-03-15       Impact factor: 16.240

3.  Hypervariable noncoding sequences in Saccharomyces cerevisiae.

Authors:  Justin C Fay; Joseph A Benavides
Journal:  Genetics       Date:  2005-06-14       Impact factor: 4.562

4.  Molecular evolution and positive selection of the symbiotic gene NORK in Medicago truncatula.

Authors:  Stéphane De Mita; Sylvain Santoni; Isabelle Hochu; Joëlle Ronfort; Thomas Bataillon
Journal:  J Mol Evol       Date:  2006-02-10       Impact factor: 2.395

5.  No effect of recombination on the efficacy of natural selection in primates.

Authors:  Kevin Bullaughey; Molly Przeworski; Graham Coop
Journal:  Genome Res       Date:  2008-01-16       Impact factor: 9.043

6.  Learning to count: robust estimates for labeled distances between molecular sequences.

Authors:  John D O'Brien; Vladimir N Minin; Marc A Suchard
Journal:  Mol Biol Evol       Date:  2009-01-08       Impact factor: 16.240

7.  Evolutionary rates and expression level in Chlamydomonas.

Authors:  Cristina E Popescu; Tudor Borza; Joseph P Bielawski; Robert W Lee
Journal:  Genetics       Date:  2005-12-15       Impact factor: 4.562

Review 8.  Characterising functionally important and ecologically meaningful genetic diversity using a candidate gene approach.

Authors:  Stuart B Piertney; Lucy M I Webster
Journal:  Genetica       Date:  2008-09-21       Impact factor: 1.082

9.  Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution.

Authors:  D Allan Drummond; Claus O Wilke
Journal:  Cell       Date:  2008-07-25       Impact factor: 41.582

10.  Evidence for variation in the effective population size of animal mitochondrial DNA.

Authors:  Gwenael Piganeau; Adam Eyre-Walker
Journal:  PLoS One       Date:  2009-02-09       Impact factor: 3.240

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