localizations with corresponding promoter predictions. Overall, TSSs and predicted promoters displayed significant co-occurrence (Fisher exact p-value,102263) based on the detected TSSs matching with predicted promoters in comparison with randomly generated TSSs. The expected co-occurrence frequencies of TSSs and promoters within a range of 40 nucleotides are predicted to be 4% (see Figure 4, group Random). As s70 target sequences may exhibit quite extensive sequence variation, this approach may have missed recognizable, but less conserved, s70-promoter sequence elements. To expand our analyses to also encompass s70-promoters with more deviating sequences, the stringency of promoter detection was relieved (1024#p-value,1023), allowing MAST-based detection of an additional 14962 sequence elements with s70-promoters similarity. Of these predicted elements 276 co-localized within a distance of 40 nt of TSSs for which no conserved s70 promoters (p- value,1024) could be found (see Figure 2 and Figure S4). However, TSSs and these lower stringency predicted promoters displayed no significant co-occurrence (Fisher exact p-val- ue = 0.16) and co-occurrence frequencies of TSSs and promoters within a range of 40 nucleotides are predicted to be 19% (see Figure S5). Discussion In this study we determined the consensus motifs of the s70 promoters in L. plantarum WCFS1 using different models and approaches, including the validation of predicted promoters using tiling array transcriptome datasets. Searching for conserved motifs in the intergenic regions of L. plantarum WCFS1 revealed highly conserved motifs (see Figure 1). Analogous to their anticipated sequence conservation, the two elements of the general s70 promoter, the so-called 235 and 210 elements, were among the most conserved motifs (p-values,1024). Figure 3. Distribution of distances between promoters and TSSs. Distribution of distances (in nt) for 568 cases where a predicted s70-promoter (genome-wide search, p-values,1024) and a TSS were found in proximity (#60 nt) of each other. TSSs are divided into 5 groups (A, B, C, D, E) defined by the differences between the means of signal intensities upstream and downstream of the TSS. The 5 groups are the inter-percentile ranges (E: 0–20%, D: 21–40%, C:41–60%, B: 61– 80%, A: 81–100%) of the ranked mean differences (see Methods). At distances above 40 nt the observed number of co-localized TSSs and promoters (#10 counts) is equal to the expected number co-localized TSSs and promoters (see Methods). doi:10.1371/journal.pone.0045097.g003 Prediction and Validation of Sigma70 Promoters Figure 4. Frequency distribution of validated promoters. Frequency distribution of s70-promoters (genome-wide search, p- values,1024) with TSSs found in proximity (#40 nt) of each other. Frequencies are given for 5 groups (A, B, C, D, E) and the expected frequency of co-localized TSSs and promoters (Random). The 5 groups are the inter-percentile ranges (E: 0–20%, D: 21–40%, C:41–60%, B: 61– 80%, A: 81–100%) of the ranked mean differences between the means of signal intensities upstream and downstream of the TSS (see Methods). doi:10.1371/journal.pone.0045097.g004 The s70 promoter related motifs were used to generate position weight matrix (PWM) based models. PWM models enabled the genome-wide detection of 3874 and additional 14962 putative s70 promoters in the L. plantarum WCFS1 genome at p-value cut-offs of 1024 and 1023, respectively. Removing all s70 promoters that were intergenic but overlapped partly with the 59- or 39-ends of genes yielded 1763 and 4093 s70 promoters at p-value cut-offs of 1024 and 1023, respectively. As shown in previous studies on lactic acid bacteria [16], the average number of transcriptional units is about 1077 with an average of 1.77 genes per unit suggesting that the number of s70 promoters reflects the number of transcripts that would be expected on basis of the number of predicted operons on the genome of L. plantarum [16]. The additional s70 promoters partly overlapping with coding parts of genes might be alternative s70 promoters (see for example Results S2) which are also found in other bacterial genomes [34]. PWMs have been used to predict s70-promoters with high sensitivity in other species [3,35,36], but were hampered by many false-positive identifications [3,28–30], illustrating the difficulty to model the general s70-promoter sequence elements, which are bipartite motifs composed of 2 conserved sequence motifs separated by a non-conserved spacer sequences of variable length [27,37,38]. Analogous to what was described in previous studies [12,21,31], transcription start sites (TSS), determined by high-resolution tiling arrays, can be used to corroborate predicted s70-promoter locations. The analysis of transcriptome datasets from L. plantarum WCFS1 obtained with strand-specific genome-wide tiling arrays revealed 1167 transcriptionally active regions (TARs). The 59-ends of TARs were used to infer putative TSSs (see Figure 2). To confirm the TSSs inferred from the 59-ends of TARs these TSSs were compared to TSSs that have previously been determined by primer extension analysis (see Results S3). Our predicted TSSs co- localised nicely with the previously described TSS positions. With the available TSS information, we were able to show that our approach gives highly reliable results. Similarly, alternative PLOS ONE | www.plosone.org 5 September 2012 | Volume 7 | Issue 9 | e45097