Abstract
BackgroundCodon usage bias has been widely reported to correlate with GC composition. However, the quantitative relationship between codon usage bias and GC composition across species has not been reported.ResultsBased on an informatics method (SCUO) we developed previously using Shannon informational theory and maximum entropy theory, we investigated the quantitative relationship between codon usage bias and GC composition. The regression based on 70 bacterial and 16 archaeal genomes showed that in bacteria, SCUO = -2.06 * GC3 + 2.05*(GC3)2 + 0.65, r = 0.91, and that in archaea, SCUO = -1.79 * GC3 + 1.85*(GC3)2 + 0.56, r = 0.89. We developed an analytical model to quantify synonymous codon usage bias by GC compositions based on SCUO. The parameters within this model were inferred by inspecting the relationship between codon usage bias and GC composition across 70 bacterial and 16 archaeal genomes. We further simplified this relationship using only GC3. This simple model was supported by computational simulation.ConclusionsThe synonymous codon usage bias could be simply expressed as 1+ (p/2)log2(p/2) + ((1-p)/2)log2((l-p)/2), where p = GC3. The software we developed for measuring SCUO (codonO) is available at .
Highlights
Codon usage bias has been widely reported to correlate with GC composition
We presented an analytical model to quantify the non-linear relationship between GC3 and a measurement of codon usage bias, which reveals that GC3 is the key factor driving synonymous codon usage and that this mechanism is independent of species
We recently developed an informatics method [13] to provide an estimate for the orderliness of synonymous codon usage (SCUO) and the amount of synonymous codon usage bias
Summary
Codon usage bias has been widely reported to correlate with GC composition. The quantitative relationship between codon usage bias and GC composition across species has not been reported. Previous codon usage analyses showed that codon usage bias is very complicated and is associated with various biological factors, such as gene expression level [7,8,9,10], gene length [11,12,13], gene translation initiation signal [14], protein amino acid composition [6,15], protein structure [16,17], tRNA abundance [18,19,20,21], mutation frequency and patterns [22,23], and GC composition [24,25,26,27]. GC composition may be described at three levels: 1) Overall GC content. Local GC composition is defined based on (page number not for citation purposes)
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