Quantification of codon selection for comparative bacterial genomics

Abstract

Background: Statistics measuring codon selection seek to compare genes by their sensitivity to selection for translational efficiency, but existing statistics lack a model for testing the significance of differences between genes. Here, we introduce a new statistic for measuring codon selection, the Adaptive Codon Enrichment (ACE).

Results: This statistic represents codon usage bias in terms of a probabilistic distribution, quantifying the extent that preferred codons are over-represented in the gene of interest relative to the mean and variance that would result from stochastic sampling of codons. Expected codon frequencies are derived from the observed codon usage frequencies of a broad set of genes, such that they are likely to reflect nonselective, genome wide influences on codon usage (e.g. mutational biases). The relative adaptiveness of synonymous codons is deduced from the frequency of codon usage in a pre-selected set of genes relative to the expected frequency. The ACE can predict both transcript abundance during rapid growth and the rate of synonymous substitutions, with accuracy comparable to or greater than existing metrics. We further examine how the composition of reference gene sets affects the accuracy of the statistic, and suggest methods for selecting appropriate reference sets for any genome, including bacteriophages. Finally, we demonstrate that the ACE may naturally be extended to quantify the genome-wide influence of codon selection in a manner that is sensitive to a large fraction of codons in the genome. This reveals substantial variation among genomes, correlated with the tRNA gene number, even among groups of bacteria where previously proposed whole-genome measures show little variation.

Conclusions: The statistical framework of the ACE allows rigorous comparison of the level of codon selection acting on genes, both within a genome and between genomes.

Figure 1

Correlations coefficients of five different codon selection statistics with transcript abundance data (see text). The set of genes contributing to f_o was systematically increased, 20 genes at a time, using the most highly expressed genes; typical f_o tables use 5000-15000 codons. All ORFs were used to construct f_n.

Figure 2

Distribution of ACE_z values for orthologs of Escherichia coli and Buchnera aphidicola. ACE_z values were calculated using the Translation40 gene set to construct f_o and all ORFs to construct f_n. A. All genes from E. coli (4144 ORFs) and B. aphidicola (564 ORFs). B. Putative orthologs (499 ORFs) shared between E. coli and B. aphidicola.

Figure 3

Codon selection as a function of tRNA gene number in Enterobacteriaceae. All codon statistics were calculated using the Translation40 gene set to construct f_o and the shared set of ORFs to construct f_n. A. ACEχ² calculated on all shared genes, for the set of 14 free-living bacteria (1060 genes; open circles, thick line) and a set of 4 endosymbionts along with 11 free-living bacteria (201 genes; filled circles, thin line). B. ACEχ², like other statistics, calculated on Translation40 gene set.