communication with the authors. All are time-series data with 6 time points. Each dataset was independently processed. Briefly, they were first transformed into folds of change relative to the first time point, and then subjected to log transformation. Log transformed data for each time point was found to be normally distributed. Finally, the log- transformed data were normalized so that the mean of all the genes, for each time point, would be zero and the standard deviation one. This was done by finding the Z-scores ((x-m)/s). Genes displaying flat patterns across all the time points were removed by dropping those profiles that had a standard deviation less than 0.50. Resultant data was used in our analysis to compare two genes’ profiles in transcription rate and mRNA steady-state analysis. Results A. Transcription rate and mRNA abundance profile similarity between genes are bi-modally distributed The Pearson correlation coefficient was used to measure similarity between two genes’ profiles. For every condition, the set of correlations between all genes in the respective dataset was computed. Two correlations were computed for each gene pair in each of the three conditions: one between their transcription rate profiles and one between their steady-state mRNA abundance profiles. The distribution of the resulting correlations was examined to determine a strategy for further analysis. None of the correlations is normally distributed. Instead, both correlations in all three conditions, displayed bi-modal distribution, with peaks at high negative and positive correlation. Figure 1 displays the distribution of steady-state mRNA abundance correlation (panel A) and transcription rate correlations (panel B) in oxidative stress condition. The distribution of correlations was also examined for the subset of S. cerevisiae genes that have bacterial orthologs to see whether they would be significantly different from the bi-modal distribution discussed above. They were all found to be bimodal, with peaks at high negative and positive correlations. Figure 2 displays the distribution of steady-state mRNA abundance correlation (panel A) for genes with bacterial ortholgs and the corresponding transcription rate correlations (panel B) in oxidative stress Network Dynamic Analysis condition. These bimodal distributions, with two peaks not significantly different from each other in their sizes, serve as a good background model for our analysis. When analyzing only those S. cerevisiae gene pairs whose bacterial orthologs co-occur in the same operons, the mass of the distribution should shift to the right, enlarging the positive correlation peak while reducing the negative correlation peak (see Results section D). B. The bimodal distribution was explained by simulation analysis To investigate why the background distributions discussed above are bimodal, a simulation was performed. The simulation started with a simplistic assumption that all gene profiles consist of linear increases or decreases and could be modeled with a linear function of timeyðtÞ ~ st. Gene profiles were generated random- ly, each with a random slope, s, drawn from a uniform distribution from the interval½{50,50 . Values for time points were computed directly from the line equation using consecutive values for t. In such a model, every gene profile is either increasing or decreasing, so all correlations are necessarily either 21 or 1. Not all profiles in a micro-array time-series datasets are mono-tone; many fluctuate. To account for this observation and the intrinsic noise in a micro- array experiment, an error term was added to every time point. The error was modeled by a Gaussian distribution with parameters m = 0 and s. As the noise (s) was increased, the resulting distributions came to resemble the distributions obtained from experimental data (Figure 3). When the noise is small relative to the signal (the range of the slope), there are peaks in the histogram at -1 and 1 and the probability mass decreases monotonically in both directions toward 0 (Figure 3A). As the noise is increased relative to the signal, the peaks shift toward zero in both directions (Figure 3B). Eventually, the distribution resembled a Gaussian distribution, where only one peak can be identified (Data not shown). C. Discrepancy between transcription rate and mRNA steady-state abundance Steady-state mRNA abundance is determined, as mentioned earlier, by both transcription rate and mRNA degradation rate. Figure 1. Bimodal distribution of transcription rate and mRNA abundance profile similarity. A) Distribution of correlations for mRNA abundance profiles under oxidative stress is shown; and B) distribution of correlations for transcription rate profiles under oxidative stress. doi:10.1371/journal.pone.0009908.g001 PLoS ONE | www.plosone.org 3 March 2010 | Volume 5 | Issue 3 | e9908