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A codon model of nucleotide substitution with selection on synonymous codon usage

Overview of attention for article published in Molecular Phylogenetics & Evolution, September 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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Title
A codon model of nucleotide substitution with selection on synonymous codon usage
Published in
Molecular Phylogenetics & Evolution, September 2015
DOI 10.1016/j.ympev.2015.08.026
Pubmed ID
Authors

Laura Kubatko, Premal Shah, Radu Herbei, Michael A. Gilchrist

Abstract

The quality of phylogenetic inference made from protein-coding genes depends, in part, on the realism with which the codon substitution process is modeled. Here we propose a new mechanistic model that combines the standard M0 substitution model of Yang (1997) with a simplified model from Gilchrist (2007) that includes selection on synonymous substitutions as a function of codon-specific nonsense error rates. We tested the newly proposed model by applying it to 104 protein-coding genes in brewer's yeast, and compared the fit of the new model to the standard M0 model and to the mutation-selection model of Yang and Nielsen (2008) using the AIC. Our new model provided significantly better fit in approximately 85% of the cases considered for the basic M0 model and in approximately 25% of the cases for the M0 model with estimated codon frequencies, but only in a few cases when the mutation-selection model was considered. However, our model includes a parameter that can be interpreted as a measure of the rate of protein production, and the estimates of this parameter were highly correlated with an independent measure of protein production for the yeast genes considered here. Finally, we found that in some cases the new model led to the preference of a different phylogeny for a subset of the genes considered, indicating that substitution model choice may have an impact on the estimated phylogeny.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 1 3%
Australia 1 3%
Unknown 37 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 26%
Researcher 10 26%
Student > Doctoral Student 4 10%
Student > Master 4 10%
Professor 4 10%
Other 5 13%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 41%
Biochemistry, Genetics and Molecular Biology 12 31%
Computer Science 5 13%
Environmental Science 1 3%
Materials Science 1 3%
Other 1 3%
Unknown 3 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 October 2015.
All research outputs
#5,141,226
of 25,374,917 outputs
Outputs from Molecular Phylogenetics & Evolution
#1,186
of 4,836 outputs
Outputs of similar age
#60,644
of 279,890 outputs
Outputs of similar age from Molecular Phylogenetics & Evolution
#30
of 79 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,836 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 279,890 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.