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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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Dependence graph of with and without ROCSEMPPR methods. Shaded circles  and  represent observed data. Dashed circlesrepresent key random model parameters, whereas the solid oval represents arandom hierarchical parameter. Solid black squares provide information onthe distributional relationships between quantities. Large rectangular boxesrepresent replication of each model component across both amino acids andgenes, for example, pausing, and mutation parameters differ across aminoacids but are common across genes, whereas counts  differ across both amino acids andgenes.
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evv087-F9: Dependence graph of with and without ROCSEMPPR methods. Shaded circles and represent observed data. Dashed circlesrepresent key random model parameters, whereas the solid oval represents arandom hierarchical parameter. Solid black squares provide information onthe distributional relationships between quantities. Large rectangular boxesrepresent replication of each model component across both amino acids andgenes, for example, pausing, and mutation parameters differ across aminoacids but are common across genes, whereas counts differ across both amino acids andgenes.

Mentions: Figure 9 presents an overview of thestructure of our approach, but to summarize, k⃗i,j∼Multinom(ni,j,p⃗i,j),p⃗i,j=mlogit−1(−ΔM⃗i−Δη⃗iΦj),Φj∼LogN(−sΦ2/2,sΦ), and ΔM⃗i,Δη⃗i,sΦ∝1. Our MCMC routine provides posterior samples of thegenome-wide parameters , , and and the gene-specific, protein synthesis parametersΦ⃗. We refer to this model as the ROC SEMPPRwithout model. Fig. 9.—


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)

Dependence graph of with and without ROCSEMPPR methods. Shaded circles  and  represent observed data. Dashed circlesrepresent key random model parameters, whereas the solid oval represents arandom hierarchical parameter. Solid black squares provide information onthe distributional relationships between quantities. Large rectangular boxesrepresent replication of each model component across both amino acids andgenes, for example, pausing, and mutation parameters differ across aminoacids but are common across genes, whereas counts  differ across both amino acids andgenes.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

evv087-F9: Dependence graph of with and without ROCSEMPPR methods. Shaded circles and represent observed data. Dashed circlesrepresent key random model parameters, whereas the solid oval represents arandom hierarchical parameter. Solid black squares provide information onthe distributional relationships between quantities. Large rectangular boxesrepresent replication of each model component across both amino acids andgenes, for example, pausing, and mutation parameters differ across aminoacids but are common across genes, whereas counts differ across both amino acids andgenes.
Mentions: Figure 9 presents an overview of thestructure of our approach, but to summarize, k⃗i,j∼Multinom(ni,j,p⃗i,j),p⃗i,j=mlogit−1(−ΔM⃗i−Δη⃗iΦj),Φj∼LogN(−sΦ2/2,sΦ), and ΔM⃗i,Δη⃗i,sΦ∝1. Our MCMC routine provides posterior samples of thegenome-wide parameters , , and and the gene-specific, protein synthesis parametersΦ⃗. We refer to this model as the ROC SEMPPRwithout model. Fig. 9.—

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