Gene expression profile of the cynobacterium synechocystis genome

The expression of functional proteins plays a crucial role in modern biotechnology. The free-living cynobacterium Synechocystis PCC 6803 is an interesting model organism to study oxygenic photosynthesis as well as other metabolic processes. Here we analyze a gene expression profiling methodology, RCBS (the scores of relative codon usage bias) to elucidate expression patterns of genes in the Synechocystis genome. To assess the predictive performance of the methodology, we propose a simple algorithm to calculate the threshold score to identify the highly expressed genes in a genome. Analysis of differential expression of the genes of this genome reveals that most of the genes in photosynthesis and respiration belong to the highly expressed category. The other genes with the higher predicted expression level include ribosomal proteins, translation processing factors and many hypothetical proteins. Only 9.5% genes are identified as highly expressed genes and we observe that highly expressed genes in Synechocystis genome often have strong compositional bias in terms of codon usage. An important application concerns the automatic detection of a set of impact codons and genes that are highly expressed tend to use this narrow set of preferred codons and display high codon bias .We further observe a strong correlation between RCBS and protein length indicating natural selection in favor of shorter genes to be expressed at higher level. The better correlations of RCBS with 2D electrophoresis and microarray data for heat shock proteins compared to the expression measure based on codon usage difference, E(g) and codon adaptive index, CAI indicate that the genomic expression profile available in our method can be applied in a meaningful way to study the mRNA expression patterns, which are by themselves necessary for the quantitative description of the biological states.