Limits...
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.

Show MeSH

Related in: MedlinePlus

Comparison of without estimates of codon-specific translationalinefficiencies Δη and estimates of differences in ribosome pausingtimes,  based on tRNA gene copy number and wobbleinefficiencies. For each amino acid, the codon with the shortest pausingtime is used as a reference and is not shown because, by definition their values are 0. Pearson correlationcoefficient ρ for all of the codons is given. Thedashed blue line represents the 1:1 line and the red line represents thebest fit linear regression line.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4494061&req=5

evv087-F4: Comparison of without estimates of codon-specific translationalinefficiencies Δη and estimates of differences in ribosome pausingtimes, based on tRNA gene copy number and wobbleinefficiencies. For each amino acid, the codon with the shortest pausingtime is used as a reference and is not shown because, by definition their values are 0. Pearson correlationcoefficient ρ for all of the codons is given. Thedashed blue line represents the 1:1 line and the red line represents thebest fit linear regression line.

Mentions: Specifically, tRNAi is the gene copy number of the tRNAthat recognize codon i and wi is thewobble term between the anticodon of and codon i. When a codon isrecognized by its canonical tRNA, we set wi = 1.We assume a purine–purine (RR) or pyrimidine–pyrimidine (YY) wobble termto be and a purine–pyrimidine (RY/YR) wobble term tobe based on Curran and Yarus (1989) and Lim andCurran (2001). We find that our genome-wide estimates ofΔt are positively correlated with empirical estimates ofΔt in S. cerevisiae(, fig. 4).Fig. 4.—


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)

Comparison of without estimates of codon-specific translationalinefficiencies Δη and estimates of differences in ribosome pausingtimes,  based on tRNA gene copy number and wobbleinefficiencies. For each amino acid, the codon with the shortest pausingtime is used as a reference and is not shown because, by definition their values are 0. Pearson correlationcoefficient ρ for all of the codons is given. Thedashed blue line represents the 1:1 line and the red line represents thebest fit linear regression line.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

evv087-F4: Comparison of without estimates of codon-specific translationalinefficiencies Δη and estimates of differences in ribosome pausingtimes, based on tRNA gene copy number and wobbleinefficiencies. For each amino acid, the codon with the shortest pausingtime is used as a reference and is not shown because, by definition their values are 0. Pearson correlationcoefficient ρ for all of the codons is given. Thedashed blue line represents the 1:1 line and the red line represents thebest fit linear regression line.
Mentions: Specifically, tRNAi is the gene copy number of the tRNAthat recognize codon i and wi is thewobble term between the anticodon of and codon i. When a codon isrecognized by its canonical tRNA, we set wi = 1.We assume a purine–purine (RR) or pyrimidine–pyrimidine (YY) wobble termto be and a purine–pyrimidine (RY/YR) wobble term tobe based on Curran and Yarus (1989) and Lim andCurran (2001). We find that our genome-wide estimates ofΔt are positively correlated with empirical estimates ofΔt in S. cerevisiae(, fig. 4).Fig. 4.—

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