dynamic transcription regulation profiles. Steady-state mRNA abundance, however, is determined by two factors: transcription rate and mRNA degradation rate; and discrepancies between steady-state mRNA abundance and transcription rate have been frequently reported [3]. This raises the issue whether steady-state mRNA abundance is the best, and a sufficient, measurement of functional relatedness. Functional relatedness is an important parameter for the construction and dynamic analysis of biochemical networks. To some degree, genes are organized into distinct, though often overlapping, functional groups. Transcription of genes in a group are activated and inactivated together. This observation is captured in the ‘‘regulon’’ concept to describe the strategy used by the cells to orchestrate the activity of the collection of its biochemical pathways [13]. In bacteria, a poly-cistronic operon can be considered as a regulon, as proteins belong to the same pathway are often organized into one operon [14]. Additionally, for genes that are highly functionally related, operons in which they co-occur tend to be conserved across bacterial genomes [15,16]. Regulons or functional relatedness of a pair of genes, however, is difficult to identify in eukaryotes. Genes in eukaryotes are independent transcription units. Computational analysis of regulatory sequences to predict similar expression profiles, and functional relatedness, between a pair of genes remains technically un-practical. On the other hand, many eukaryote genes, at least in the yeast S. cerevisiae, have orthologs in bacteria genomes. Occurrence in the same operon by their bacteria orthologs has been shown to be a good indicator of functional relatedness for a pair of such genes in S. cerevisiae [13]. Moreover, genome-wide determination of transcription rates have been reported for several physiological processes in the yeast S. cerevisiae [3,17,18]. Here, we report our efforts to take advantage of these datasets to compare transcription rate and steady-state mRNA abundance as measurements of functional relatedness. Our analysis was performed on a group of S. cerevisiae genes that have bacterial orthologs as identified by reciprocal BLAST analysis. Functional relatedness of a gene pair is measured by the frequency at which their bacterial orthologs occur in the same operon in the collection of NCBI bacterial genomes. Materials and Methods A. Genome data and prediction of Saccharomyces cerevisiae orthologs in bacteria The proteomic sequences, protein genomic locations, and complete genomic sequences of 765 bacteria species and the yeast Saccharomyces cerevisiae were downloaded from NCBI ftp site (ftp:// ftp.ncbi.nih.gov/genomes/). Proteomes of the plasmids in all these species were not included in our analysis. In order to identify protein orthology between S. cerevisiae proteomes and the collection of bacterial proteomes, bi-directional BLASTp analysis was performed. Briefly, each of annotated open reading frames (ORFs) of S. cerevisiae was used as a query to BLAST against each of the bacterial proteomes, with 1024 as the cut-off BLAST E-value [19]. Conversely, each protein sequence in a bacterial proteomes was used as a query in BLAST against S. cerevisiae proteome, with 1024 as the cut-off E-value. Orthology between a S. cerevisiae protein and a bacterial protein was identified only when the pair is the best hit in both direction of the BLAST analysis. Additionally, redundancy exists in the bacterial proteome collection; many of the 765 bacterial proteomes are highly homologous, sometimes almost identical, to each other. It is known that such genome redundancy adversely affect computational prediction of functional relatedness between genes [20,21]. Therefore, in these cases, we selected only Network Dynamic Analysis the one that shared the maximum number of orthologs with S. cerevisiae to reduce redundancy. Finally, we identified a total of 284 non-redundant bacterial species that share orthologs with S. cerevisiae. A total of 2030 S. cerevisiae genes were identified to have bacterial orthologs. B. Bacterial operon prediction and co-operon frequency calculation A support vector machine (SVM), as described previously [22], was used. Briefly, experimentally proven operons downloaded from the EcoCyc database were used to create a set of gene pairs, in which the genes in a pair are adjacent to each other and belong to the same operon. Additionally, we collected a set of gene pairs, in which the genes in a pair are adjacent to each other, transcribed in the same direction, but belong to different operons. Intergenic distances between the genes in the former gene pairs were taken as positive data set, whereas those in the latter were taken as the negative data set. The two datasets were then used to train a Support Vector Machine to predict poly-cistronic operons in the 284 species of bacteria. For any pair of S. cerevisiae genes, both of which have bacterial orthologs, we counted how many times their orthologs occurred in the same operon — the co-operon frequency. Using this method, we identified 8636 gene pairs, among 1491 S. cerevisiae genes, to have their bacterial orthologs co- occur at least once in predicted operons. The co-operon frequency dataset generated above was further processed in two steps. First, the data were normalized using the number of bacterial species that has an ortholog to the corresponding S. cerevisiae gene. This bacterial species count varies among the 1491 S. cerevisiae genes. In cases where the counts are different for the two genes in a pair, the normalization process should reflect the observation that the degree of co-presence/co- absence by two genes in bacterial genomes, also known as phylogenetic profile similarity, is a good indicator of functional relatedness between two bacterial genes [23]. Normalization with the lower bacterial species count would discard this useful information, because only those bacterial species with orthologs to both S. cerevisiae genes were considered. The higher count is therefore chosen for the normalization. The data was then subjected to a second step of processing. Even though redundancy in the set of bacterial species has been reduced, as discussed earlier in the gene orthology prediction step, it is still possible that a gene pair can co-occur, potentially in the same operon, in a small number of related bacterial species. It has been reported that such situations adversely affect comparative- genomics-based analysis of functional relatedness between bacte- rial genes [21]. This problem was therefore alleviated by eliminating those gene pairs in which the higher count of bacterial species with orthologs is less than 50. The remaining S. cerevisiae gene pairs were considered to be functionally related (see Results). Genes that are highly function- ally related tend to have their operons conserved across bacterial genomes [15,16] and display similar phylogenetic profile [21]. Therefore, the higher the normalized co-operon frequency, the more functionally related they are considered to be. C. Micro-array data and data processing Three S. cerevisiae datasets for parallel measurement of transcription rate and steady-state mRNA abundance were used. The three physiological processes studied are glucose-galactose carbon source shift [3], oxidative stress [17] and osmotic stress [18], respectively. The first two datasets were downloaded from the researcher’s website (http://scsie.uv.es/chipsdna/chipsdna-e. html#datos). The last one was obtained through personal PLoS ONE | www.plosone.org 2 March 2010 | Volume 5 | Issue 3 | e9908