Predicting gene expression level from codon usage bias

Abstract

The "expression measure" of a gene, E(g), is a statistic devised to predict the level of gene expression from codon usage bias. E(g) has been used extensively to analyze prokaryotic genome sequences. We discuss 2 problems with this approach. First, the formulation of E(g) is such that genes with the strongest selected codon usage bias are not likely to have the highest predicted expression levels; indeed the correlation between E(g) and expression level is weak among moderate to highly expressed genes. Second, in some species, highly expressed genes do not have unusual codon usage, and so codon usage cannot be used to predict expression levels. We outline a simple approach, first to check whether a genome shows evidence of selected codon usage bias and then to assess the strength of bias in genes as a guide to their likely expression level; we illustrate this with an analysis of Shewanella oneidensis. © The Author 2006. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved.