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Estimating Gene Expression and Codon-Specific Translational Efficiencies, Mutation Biases, and Selection Coefficients from Genomic Data Alone.

Gilchrist MA, Chen WC, Shah P, Landerer CL, Zaretzki R - Genome Biol Evol (2015)

Bottom Line: We also observe strong agreement between our parameter estimates and those derived from alternative data sets.Our estimates of codon-specific translational inefficiencies and tRNA copy number-based estimates of ribosome pausing time ([Formula: see text]), and mRNA and ribosome profiling footprint-based estimates of gene expression ([Formula: see text]) are also highly correlated, thus supporting the hypothesis that selection against translational inefficiency is an important force driving the evolution of CUB.In conclusion, our method demonstrates that an enormous amount of biologically important information is encoded within genome scale patterns of codon usage, accessing this information does not require gene expression measurements, but instead carefully formulated biologically interpretable models.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville National Institute for Mathematical and Biological Synthesis, Knoxville, Tennessee mikeg@utk.edu.

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Evaluation of predicted gene expression levels between models and empiricalmeasurements from Yassour etal. (2009). (a) Comparison ofwith and without ROC SEMPPR estimates of protein synthesisrates, . The units for  are proteins/t and timet is scaled such that the prior for satisfies . Note the very strong correlation betweenthe with and without estimates of  for the high expression genes.(b) Comparison of without estimates of  and empirical measurements of mRNAabundances, . The empirical mRNA abundance measurements,[mRNA], are being used here as a proxy for protein synthesis rates, that is,[mRNA]  proteins/t. Themeasurements are scaled such that the mean [mRNA] value is 1. Pearsoncorrelation coefficients ρ are given and the dashed gray line indicates1:1 line.
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evv087-F5: Evaluation of predicted gene expression levels between models and empiricalmeasurements from Yassour etal. (2009). (a) Comparison ofwith and without ROC SEMPPR estimates of protein synthesisrates, . The units for are proteins/t and timet is scaled such that the prior for satisfies . Note the very strong correlation betweenthe with and without estimates of for the high expression genes.(b) Comparison of without estimates of and empirical measurements of mRNAabundances, . The empirical mRNA abundance measurements,[mRNA], are being used here as a proxy for protein synthesis rates, that is,[mRNA] proteins/t. Themeasurements are scaled such that the mean [mRNA] value is 1. Pearsoncorrelation coefficients ρ are given and the dashed gray line indicates1:1 line.

Mentions: Given the strong correlation between ROC SEMPPR’s with andwithout estimates of the codon-specific mutation biases and translational inefficiencies, it is not surprising that with andwithout estimates of from ROC SEMPPR are highly correlated(, fig.5a). More importantly, the without based estimates of show substantial correlation with the mRNA abundancebased estimates of values from Yassour et al. (2009) (, fig.5b). To be clear, these values are the same values used as inputs to thewith model fits. Fig. 5.—


Estimating Gene Expression and Codon-Specific Translational Efficiencies, Mutation Biases, and Selection Coefficients from Genomic Data Alone.

Gilchrist MA, Chen WC, Shah P, Landerer CL, Zaretzki R - Genome Biol Evol (2015)

Evaluation of predicted gene expression levels between models and empiricalmeasurements from Yassour etal. (2009). (a) Comparison ofwith and without ROC SEMPPR estimates of protein synthesisrates, . The units for  are proteins/t and timet is scaled such that the prior for satisfies . Note the very strong correlation betweenthe with and without estimates of  for the high expression genes.(b) Comparison of without estimates of  and empirical measurements of mRNAabundances, . The empirical mRNA abundance measurements,[mRNA], are being used here as a proxy for protein synthesis rates, that is,[mRNA]  proteins/t. Themeasurements are scaled such that the mean [mRNA] value is 1. Pearsoncorrelation coefficients ρ are given and the dashed gray line indicates1:1 line.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4494061&req=5

evv087-F5: Evaluation of predicted gene expression levels between models and empiricalmeasurements from Yassour etal. (2009). (a) Comparison ofwith and without ROC SEMPPR estimates of protein synthesisrates, . The units for are proteins/t and timet is scaled such that the prior for satisfies . Note the very strong correlation betweenthe with and without estimates of for the high expression genes.(b) Comparison of without estimates of and empirical measurements of mRNAabundances, . The empirical mRNA abundance measurements,[mRNA], are being used here as a proxy for protein synthesis rates, that is,[mRNA] proteins/t. Themeasurements are scaled such that the mean [mRNA] value is 1. Pearsoncorrelation coefficients ρ are given and the dashed gray line indicates1:1 line.
Mentions: Given the strong correlation between ROC SEMPPR’s with andwithout estimates of the codon-specific mutation biases and translational inefficiencies, it is not surprising that with andwithout estimates of from ROC SEMPPR are highly correlated(, fig.5a). More importantly, the without based estimates of show substantial correlation with the mRNA abundancebased estimates of values from Yassour et al. (2009) (, fig.5b). To be clear, these values are the same values used as inputs to thewith model fits. Fig. 5.—

Bottom Line: We also observe strong agreement between our parameter estimates and those derived from alternative data sets.Our estimates of codon-specific translational inefficiencies and tRNA copy number-based estimates of ribosome pausing time ([Formula: see text]), and mRNA and ribosome profiling footprint-based estimates of gene expression ([Formula: see text]) are also highly correlated, thus supporting the hypothesis that selection against translational inefficiency is an important force driving the evolution of CUB.In conclusion, our method demonstrates that an enormous amount of biologically important information is encoded within genome scale patterns of codon usage, accessing this information does not require gene expression measurements, but instead carefully formulated biologically interpretable models.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville National Institute for Mathematical and Biological Synthesis, Knoxville, Tennessee mikeg@utk.edu.

Show MeSH
Related in: MedlinePlus