Estimating Gene Expression and Codon-Specific Translational Efficiencies, Mutation Biases, and Selection Coefficients...

Area of Science:

• Genomics
• Evolutionary Biology
• Computational Biology

Background:

• Genomic data presents challenges for extracting biologically meaningful information.
• Codon usage bias (CUB) is influenced by natural selection for translation efficiency and mutation bias.

Purpose of the Study:

• To present a mechanistically interpretable Bayesian model, ROC SEMPPR, for analyzing CUB.
• To separate contributions of mutation bias and selection against translational inefficiency.
• To demonstrate that gene expression estimates can be generated, not just required.

Main Methods:

• Developed the ribosome overhead costs Stochastic Evolutionary Model of Protein Production Rate (ROC SEMPPR).
• Applied a population genetics-based Bayesian approach.
• Validated the model using the Saccharomyces cerevisiae S288c genome.

Main Results:

• ROC SEMPPR accurately estimates codon-specific mutation biases, translational efficiencies, and gene expression levels.
• Model fits show high agreement with previous approaches and alternative datasets.
• Found that mutation bias can significantly influence codon usage in highly expressed genes.

Conclusions:

• Biologically important information is encoded in genome-scale CUB patterns.
• Accessing this information does not require gene expression measurements.
• Carefully formulated, biologically interpretable models are key to unlocking CUB insights.