genome wide transcription analysis methods such as RNA-seq can also be employed to detect TSSs in bacteria [31,39,40], but here we confirm that high-resolution tiling array data are a valuable resource to infer TSS locations [12,21,22]. Of the 1167 putative TSSs selected for validation, 568 TSS were positioned within proximity of the s70 promoters that were predicted with high stringency, while an additional 276 TSSs could be co-localized with predicted promoters using the lower stringency promoter prediction (1024#p-values,1023). However, the latter co-occurrence of TSSs and low-stringency predicted promoters was not significant (Fisher exact p-value = 0.16), which is likely due to a substantial overprediction (false-positives) of promoter sequences. In general, promoters function more efficiently with sequence elements that resemble the consensus sequences. However, most promoters have non-consensus sequences and their activity is further modulated by trans-acting factors (including alternative sigma factors, small ligands and transcription factors) [34]. To discover conserved transcription initiation motifs other than the s70 promoter-related sequences, DNA sequences upstream to TSSs with no s70 promoter were searched for other conserved motifs (applying MEME searches as described for the initial search, see Method). These searches did not reveal additional highly-conserved motifs (p-value cut-off,1024; data not shown). Our results show that motif-based approaches can be used to recover conserved sequence elements of the s70 promoter. However, the discovery of more degenerate promoter elements is hampered by many false-positive identifications [3,28–30] and validation of predicted promoters (e.g. using transcriptome data) is required but limited by the statistical significance of the obtained results. Our findings suggest that these more degenerate s70 promoters may constitute a considerable proportion of all s70 promoters in a given genome (in our case about 24% of the validated s70 promoters have p-values$1024), which has been described for other bacterial genomes (such as E. coli) [34]. In this study, transcription data under a single growth condition was used limiting the validation to the active portion of s70 promoter- related genes that are clearly transcribed. Transcription data obtained under a variety of growth conditions would still be required to validate the full complement of functional s70 promoters and distinguish them from false-positive predictions. Materials and Methods Transcription analysis of Lactobacillus plantarum WCFS1 For genome wide transcriptome analysis L. plantarum WCFS1 [18] was grown until mid-exponential growth phase in MRS, cells were harvested and RNA was isolated and labeled as described before [41,42]. 1.5 mg of labeled targets were hybridized and analyzed on WCFS1 DNA microarrays that cover the whole genome in a tiling fashion as described (GSE36898, GPL15385, GPL15386). The gene expression data are MIAME compliant and deposited in the gene expression omnibus (GEO) database (accession numbers GSE36898, GPL15385, GPL15386). The hybridization intensities of two biological replicates were obtained. Hybridization intensities were log2-transformed, back- ground corrected and normalized by quantile normalization [43]. After normalization, intensities of the two replicates were averaged yielding two strand-specific transcript-level data sets. Building s70-promoter models using PWMs Position weight matrices were constructed from high-scoring motifs identified by MEME [24] searches on a selected subset of intergenic DNA sequences of the L. plantarum WCFS1 genome. Prediction and Validation of Sigma70 Promoters Initially, all 100 nt regions upstream of all protein encoding genes were selected from the L. plantarum WCFS1 genome (Refseq NC_004567). Subsequently, these sequences were evaluated for their potential overlap with a preceding gene, and in such cases, only the intergenic sequence was used for analysis, provided they were 25 nt or longer. The resulting set of selected intergenic sequences was searched for conserved motifs using MEME [24], applying standard DNA parameter settings. Only motifs reported by MEME with E-values below 1024 were considered relevant for further analysis. Additional parameters used for selection of candidate sequences were: 1) Zero or One Occurrence Per Sequence (ZOOPS mode), 2) a maximum of ten different motifs per sequence, and 3) each motif should be found in at least thirty-five different sequences. PWM models were constructed for the most abundantly encoun- tered motifs, including those resembling the canonical 235 and 210 elements known from general s70-dependent promoters [6]. Predicting s70 promoters The PWM models were used in a MAST [26] search against the complete genomic sequence of L. plantarum WCFS1 including the coding regions to predict potential s70 promoters. MAST hits with relatively high significance (p-values,1024) were used in further analysis, unless indicated otherwise. Because the spacer region between the 235 and 210 boxes of the s70-promoter varies in length (16–20 nt) [27], PWM models were manually altered by either copying or deleting the columns in the position weight matrix that were part of the spacer region and had the least functional information (low overall specificity and low sequence conservation) of this region. These new matrices were used in genome-wide MAST searches to predict additional promoter occurrences with different spacer-lengths. All predicted promoters (including those predicted on basis of the extended 210 box motif) were filtered for overlapping promoter elements. In case of overlap, the hit with the highest significance was used for further analysis. All non-intergenic promoter elements were removed. Detecting transcriptionally active regions A global threshold approach was used to detect significantly transcribed genomic regions. For each strand-specific dataset the global threshold was determined from the log distribution of the probe signals. A background normal distribution was estimated from the left-of-the-mode data [43] and used to estimate the background probability of a probe signal. The global threshold was set to a signal level at which the probability of a probe signal being a background signal was less than 1026. Next, transcriptionally active regions (TARs; [33]) were determined by applying a segmentation algorithm (comparable to transfrags as described in [32] and [44]) using the previously estimated global threshold. Segments of adjacent probes with signal intensities above the threshold were identified. Adjacent segments with a gap of maximal 60 nt (corresponding to the length of a single probe on the tiled micro-array) were joined and, segments shorter than 120 nt were discarded. Transcription start site determination and statistical analysis The 59-end of a TAR was used to estimate the corresponding TSS position. Manual inspection of signal patterns of different TARs in the tiling array data revealed that highly expressed regions display a rather sharp, approximately linear rise in signal intensity while in lowly expressed regions the signal change PLOS ONE | www.plosone.org 6 September 2012 | Volume 7 | Issue 9 | e45097