Received 4 April 1986; Accepted 23 May 1986 | Paul M.Sharp*, Therese M.F.Tuohy and Krzysztof R.Mosurski1
The study investigates codon usage in yeast genes and its relationship to gene expression levels. Codon usage data for 110 yeast genes were compiled and analyzed using cluster analysis, which revealed two distinct groups of genes based on their relative synonymous codon usage. One group consists of highly expressed genes, characterized by more extreme synonymous codon preference, while the other group includes genes with lower expression levels. The highly expressed genes show a higher correlation with tRNA abundance, greater third base pyrimidine bias, and less A+T richness compared to the yeast genome average. The cluster analysis can predict the likely level of gene expression based on codon usage patterns. The findings suggest that codon usage is influenced by factors such as tRNA abundance and the interaction energies between tRNA and mRNA, which may affect translation efficiency. This analysis has implications for optimizing gene expression in yeast and designing synthetic genes for biotechnological applications.The study investigates codon usage in yeast genes and its relationship to gene expression levels. Codon usage data for 110 yeast genes were compiled and analyzed using cluster analysis, which revealed two distinct groups of genes based on their relative synonymous codon usage. One group consists of highly expressed genes, characterized by more extreme synonymous codon preference, while the other group includes genes with lower expression levels. The highly expressed genes show a higher correlation with tRNA abundance, greater third base pyrimidine bias, and less A+T richness compared to the yeast genome average. The cluster analysis can predict the likely level of gene expression based on codon usage patterns. The findings suggest that codon usage is influenced by factors such as tRNA abundance and the interaction energies between tRNA and mRNA, which may affect translation efficiency. This analysis has implications for optimizing gene expression in yeast and designing synthetic genes for biotechnological applications.