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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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Comparison of without estimates of codon-specific mutation biasesΔM and estimates generated from mutationaccumulation experiments (Zhu et al.2014). 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. The solid line representsthe best fit linear regression.
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evv087-F3: Comparison of without estimates of codon-specific mutation biasesΔM and estimates generated from mutationaccumulation experiments (Zhu et al.2014). 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. The solid line representsthe best fit linear regression.

Mentions: Instead of simply comparing our ROC SEMPPR model’s without estimates of ΔM andΔη to its with estimates, we can also compare these parameters withother data. Due to the detailed balance requirement of the stationary distribution ofour population genetics model (Sella and Hirsh2005), differences in ΔM values between codons thatcan directly mutate to one another will equal the log of the ratio of their mutationrates. Thus, our estimates of ΔM provide testable hypothesesabout the ratio of mutation rates in S. cerevisiae. We use estimatesof per base-pair mutation rates from a recent high-throughput mutation accumulationexperiment in S. cerevisiae (Zhu et al. 2014). These experimental estimates of mutation bias,, are calculated as (3)ΔMNNNi,NNNje=ln[ni→jni/nj→inj], where is the number of mutations observed per nibases in the genome. As mutations in mutation accumulation experiments are strandagnostic, that is, they do not distinguish between the coding and template strandnucleotides, we cannot distinguish between the mutations NNCNNG and NNGNNC nor NNANNT and NNTNNA. As a result, our empirical estimates of and are set to 0. We find that our estimates ofcodon-specific mutation rates correlate highly with empirical mutation rates inS. cerevisiae (, fig. 3).Fig. 3.—


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 mutation biasesΔM and estimates generated from mutationaccumulation experiments (Zhu et al.2014). 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. The solid line representsthe best fit linear regression.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

evv087-F3: Comparison of without estimates of codon-specific mutation biasesΔM and estimates generated from mutationaccumulation experiments (Zhu et al.2014). 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. The solid line representsthe best fit linear regression.
Mentions: Instead of simply comparing our ROC SEMPPR model’s without estimates of ΔM andΔη to its with estimates, we can also compare these parameters withother data. Due to the detailed balance requirement of the stationary distribution ofour population genetics model (Sella and Hirsh2005), differences in ΔM values between codons thatcan directly mutate to one another will equal the log of the ratio of their mutationrates. Thus, our estimates of ΔM provide testable hypothesesabout the ratio of mutation rates in S. cerevisiae. We use estimatesof per base-pair mutation rates from a recent high-throughput mutation accumulationexperiment in S. cerevisiae (Zhu et al. 2014). These experimental estimates of mutation bias,, are calculated as (3)ΔMNNNi,NNNje=ln[ni→jni/nj→inj], where is the number of mutations observed per nibases in the genome. As mutations in mutation accumulation experiments are strandagnostic, that is, they do not distinguish between the coding and template strandnucleotides, we cannot distinguish between the mutations NNCNNG and NNGNNC nor NNANNT and NNTNNA. As a result, our empirical estimates of and are set to 0. We find that our estimates ofcodon-specific mutation rates correlate highly with empirical mutation rates inS. cerevisiae (, fig. 3).Fig. 3.—

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