Estimating Gene Expression and Codon-Specific Translational Efficiencies, Mutation Biases, and Selection Coefficients from Genomic Data Alone.
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.
Affiliation: Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville National Institute for Mathematical and Biological Synthesis, Knoxville, Tennessee email@example.com.Show MeSH
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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.—
Affiliation: Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville National Institute for Mathematical and Biological Synthesis, Knoxville, Tennessee firstname.lastname@example.org.