scholarly journals Investigating Protein-Coding Sequence Evolution with Probabilistic Codon Substitution Models

2008 ◽  
Vol 26 (2) ◽  
pp. 255-271 ◽  
Author(s):  
M. Anisimova ◽  
C. Kosiol
Keyword(s):  
Sequence Evolution ◽  
Protein Coding ◽  
Coding Sequence ◽  
Codon Substitution ◽  
Substitution Models

scholarly journals Standard codon substitution models overestimate purifying selection for non-stationary data

2016 ◽  
Author(s):  
Benjamin D Kaehler ◽  
Von Bing Yap ◽  
Gavin A Huttley
Keyword(s):  
Natural Selection ◽  
De Novo ◽  
Purifying Selection ◽  
Neutral Evolution ◽  
Protein Coding ◽  
Synonymous Substitutions ◽  
New Model ◽  
Sequence Composition ◽  
Codon Substitution ◽  
Substitution Models

Estimation of natural selection on protein-coding sequences is a key comparative genomics approach for de novo prediction of lineage specific adaptations. Selective pressure is measured on a per-gene basis by comparing the rate of non-synonymous substitutions to the rate of neutral evolution, typically assumed to be the rate of synonymous substitutions. All published codon substitution models have been time-reversible and thus assume that sequence composition does not change over time. We previously demonstrated that if time-reversible DNA substitution models are applied blindly in the presence of changing sequence composition, the number of substitutions is systematically biased towards overestimation. We extend these findings to the case of codon substitution models and further demonstrate that the ratio of non-synonymous to synonymous rates of substitution tends to be underestimated over three data sets of insects, mammals, and vertebrates. Our basis for comparison is a non-stationary codon substitution model that allows sequence composition to change. Model selection and model fit results demonstrate that our new model tends to fit the data better. Direct measurement of non-stationarity shows that bias in estimates of natural selection and genetic distance increases with the degree of violation of the stationarity assumption. Additionally, inferences drawn under time-reversible models are systematically affected by compositional divergence. As genomic sequences accumulate at an accelerating rate, the importance of accurate de novo estimation of natural selection increases. Our results establish that our new model provides a more robust perspective on this fundamental quantity.

scholarly journals The Impact of the Nucleosome Code on Protein-Coding Sequence Evolution in Yeast

PLoS Genetics ◽  
2008 ◽  
Vol 4 (11) ◽  
pp. e1000250 ◽  
Author(s):  
Tobias Warnecke ◽  
Nizar N. Batada ◽  
Laurence D. Hurst
Keyword(s):  
Sequence Evolution ◽  
Protein Coding ◽  
Coding Sequence ◽  
The Impact

scholarly journals High Rate of Protein Coding Sequence Evolution and Species Diversification in the Lycaenids

2017 ◽  
Vol 5 ◽  
Author(s):  
Loïc Pellissier ◽  
Anna Kostikova ◽  
Glenn Litsios ◽  
Nicolas Salamin ◽  
Nadir Alvarez
Keyword(s):  
High Rate ◽  
Sequence Evolution ◽  
Protein Coding ◽  
Coding Sequence ◽  
Species Diversification

scholarly journals Standard codon substitution models overestimate purifying selection for non-stationary data

2016 ◽  
Author(s):  
Benjamin D Kaehler ◽  
Von Bing Yap ◽  
Gavin A Huttley
Keyword(s):  
Natural Selection ◽  
De Novo ◽  
Purifying Selection ◽  
Neutral Evolution ◽  
Protein Coding ◽  
Synonymous Substitutions ◽  
New Model ◽  
Sequence Composition ◽  
Codon Substitution ◽  
Substitution Models

Estimation of natural selection on protein-coding sequences is a key comparative genomics approach for de novo prediction of lineage specific adaptations. Selective pressure is measured on a per-gene basis by comparing the rate of non-synonymous substitutions to the rate of neutral evolution, typically assumed to be the rate of synonymous substitutions. All published codon substitution models have been time-reversible and thus assume that sequence composition does not change over time. We previously demonstrated that if time-reversible DNA substitution models are applied blindly in the presence of changing sequence composition, the number of substitutions is systematically biased towards overestimation. We extend these findings to the case of codon substitution models and further demonstrate that the ratio of non-synonymous to synonymous rates of substitution tends to be underestimated over three data sets of insects, mammals, and vertebrates. Our basis for comparison is a non-stationary codon substitution model that allows sequence composition to change. Model selection and model fit results demonstrate that our new model tends to fit the data better. Direct measurement of non-stationarity shows that bias in estimates of natural selection and genetic distance increases with the degree of violation of the stationarity assumption. Additionally, inferences drawn under time-reversible models are systematically affected by compositional divergence. As genomic sequences accumulate at an accelerating rate, the importance of accurate de novo estimation of natural selection increases. Our results establish that our new model provides a more robust perspective on this fundamental quantity.

scholarly journals Codon-Substitution Models for Heterogeneous Selection Pressure at Amino Acid Sites

Genetics ◽  
2000 ◽  
Vol 155 (1) ◽  
pp. 431-449 ◽  
Author(s):  
Ziheng Yang ◽  
Rasmus Nielsen ◽  
Nick Goldman ◽  
Anne-Mette Krabbe Pedersen
Keyword(s):  
Amino Acid ◽  
Positive Selection ◽  
Selective Pressure ◽  
Acid Sites ◽  
Data Sets ◽  
Protein Coding ◽  
Important Indicator ◽  
Diversifying Selection ◽  
Codon Substitution ◽  
Neutral Mutations

AbstractComparison of relative fixation rates of synonymous (silent) and nonsynonymous (amino acid-altering) mutations provides a means for understanding the mechanisms of molecular sequence evolution. The nonsynonymous/synonymous rate ratio (ω = dN/dS) is an important indicator of selective pressure at the protein level, with ω = 1 meaning neutral mutations, ω < 1 purifying selection, and ω > 1 diversifying positive selection. Amino acid sites in a protein are expected to be under different selective pressures and have different underlying ω ratios. We develop models that account for heterogeneous ω ratios among amino acid sites and apply them to phylogenetic analyses of protein-coding DNA sequences. These models are useful for testing for adaptive molecular evolution and identifying amino acid sites under diversifying selection. Ten data sets of genes from nuclear, mitochondrial, and viral genomes are analyzed to estimate the distributions of ω among sites. In all data sets analyzed, the selective pressure indicated by the ω ratio is found to be highly heterogeneous among sites. Previously unsuspected Darwinian selection is detected in several genes in which the average ω ratio across sites is <1, but in which some sites are clearly under diversifying selection with ω > 1. Genes undergoing positive selection include the β-globin gene from vertebrates, mitochondrial protein-coding genes from hominoids, the hemagglutinin (HA) gene from human influenza virus A, and HIV-1 env, vif, and pol genes. Tests for the presence of positively selected sites and their subsequent identification appear quite robust to the specific distributional form assumed for ω and can be achieved using any of several models we implement. However, we encountered difficulties in estimating the precise distribution of ω among sites from real data sets.

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