appeared to be sigmoidal (data not shown). Based on this observation, we developed the following procedure to determine TSS: Probe signals from the 59-end of a TAR and its upstream region were used to determine the genomic position at which the slope of increase in signal intensities reached its maximum. This position was used to linearly extrapolate the position of the TSS using the difference between the probe signal of the maximum slope position and the background signal (derived from the mode of the distribution of the probe signals). Using the gene annotation of L. plantarum WCFS1 (Refseq NC_004567, [23]), TSSs were labelled anti sense if the transcribed strand (TAR) was opposite to a gene-containing strand. In addition, if a TAR had no overlap with known genes (based on the current gene annotation) the assigned TSS and its corre- sponding TAR were annotated as non-coding. All other TSSs were assigned to the closest downstream located gene positioned on the same strand. To estimate the probability for randomly selecting a TSS in a 40 nucleotide range from a promoter, we used the genomic positions of the TSSs, randomly selected genomics positions of promoters (10.000 times) and calculated for each TSS the minimum distance to the 39end of a promoter. TSSs were grouped according to the increase in signal intensity by (1) calculating the differences between the means of expression signal upstream and downstream of the TSS and (2) division of the TSS into five groups according to their rank position (inter- percentile ranges 0–20%, 21–40%, 41–60%, 61–80% and 81– 100%). Supporting Information Results S1 Conserved motifs detected by MEME in the upstream regions of protein-encoding genes in L. plantarum WCFS1. (DOCX) Results S2 Interpretation of TAR detection results. (DOCX) Evaluation of results of the TSS prediction method. Results S3 (DOCX) Figure S1 Motifs detected by MEME in L. plantarum WCFS1. Motifs are displayed as Seqlogo’s that contain at each position stacks of letters. The height of an individual letter in a stack represents the probability of the letter at that position in an occurrence of the motif. The position-specific probabilities are retrieved from the position-specific weight matrices calculated by MEME. (TIF) Figure S2 Inferred signal intensity of a genomic region containing two genes. Inferred signal intensity plotted on part of the L. plantarum WCFS1 chromosome (genome location: 1156200–1159500 of the forward strand). At this location two References 1. Baxevanis AD (2004) An overview of gene identification: approaches, strategies, and considerations. Curr Protoc Bioinformatics Chapter 4: Unit4 1. 2. Mira A, Ochman H, Moran (2001) Deletional bias and the evolution of bacterial genomes. Trends Genet 17: 589–596. 3. Huerta AM, Collado-Vides J (2003) Sigma70 promoters in Escherichia coli: specific transcription in dense regions of overlapping promoter-like signals. J Mol Biol 333: 261–278. 4. Gruber TM, Gross CA (2003) Multiple sigma subunits and the partitioning of bacterial transcription space. Annu Rev Microbiol 57: 441–466. 5. Kroos L, Yu YT (2000) Regulation of sigma factor activity during Bacillus subtilis development. Current opinion in microbiology 3: 553–560. Prediction and Validation of Sigma70 Promoters genes are located (lp_1273 and lp_1274) which are considered to form an operon. The tiling microarray signal intensities measured confirms that both genes are transcribed at an approximately equal level and suggest that the inferred transcription start is 537 nucleotides upstream of lp_1273. The black box indicates the position of a predicted s70-promoter. The grey box indicates the position of a second predicted s70-promoter with no close TSS. (TIF) Figure S3 Inferred signal intensity of a genomic region containing two genes. Inferred signal intensity plotted on part of the L. plantarum WCFS1 chromosome (genome location: 1407149–1410217 of the forward strand). At this location two genes are located (lp_1541 and lp_1543) expressed at different levels. For lp_1541 a putative TSS and a predicted s70-promoter co-localized (2 nt apart) upstream to the annotated translation start of the gene. For lp_1543 a putative TSS is located 145 nt downstream of the annotated translation start of lp_1543 and a s70-promoter is predicted 31 nt upstream of this translation start. The low signal expression of lp_1543 makes it difficult to infer a clear TSS position. The black boxes indicate the positions of predicted s70-promoters. (TIF) Figure S4 Distribution of distances between promoters and TSSs. Distribution of distances (in nt) for 568 cases where a predicted s70-promoter (1024#p-values,1023) 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). (TIF) Figure S5 Frequency distribution of validated promot- ers. Frequency distribution of s70-promoters (1024#p-val- ues,1023) 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 (Ran- dom). 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). (TIF) Author Contributions Conceived and designed the experiments: MK. Performed the experi- ments: RSB. Analyzed the data: TJT MW SvH. Contributed reagents/ materials/analysis tools: RSB MW TJT. Wrote the paper: TJT MW SvH. 6. deHaseth PL, Zupancic ML, Record MT Jr (1998) RNA polymerase-promoter interactions: the comings and goings of RNA polymerase. J Bacteriol 180: 3019– 3025. 7. Lonetto M, Gribskov M, Gross CA (1992) The sigma 70 family: sequence conservation and evolutionary relationships. J Bacteriol 174: 3843–3849. 8. Paget MS, Helmann JD (2003) The sigma70 family of sigma factors. Genome Biol 4: 203. 9. Stevens MJ, Molenaar D, de Jong A, De Vos WM, Kleerebezem M (2010) sigma54-Mediated control of the mannose phosphotransferase sytem in Lactobacillus plantarum impacts on carbohydrate metabolism. Microbiology 156: 695–707. PLOS ONE | www.plosone.org 7 September 2012 | Volume 7 | Issue 9 | e45097