Bernard et al. BMC Genomics 2010, 11:166 http://www.biomedcentral.com/1471-2164/11/166 sequences [55-57]. The three classes of core-promoter elements observed in the [-39, -26] region are each pre- sent in about 20% of the promoters. In this study, we used “clean” classes of promoters, i.e. those containing only one of the three classes of elements. However in several other promoters, more than one element from a given class may be present. Therefore many functional combinations are expected. Experimental data have shown that the TATA-box is involved in the direct binding of TBP [5] and at least some TC- and TATAΔ- elements might be involved in the recognition by other TFs recruiting TBP to form the transcription initiation complex at the right position. Consistent with this notion, both TC- and TATAΔ-elements are linked to the specificity of expression that could be mediated through different TFs under the control of various sig- nals. An alternative would be that TATA-boxes, TATAΔ-elements and TC-elements are all responsible for DNA instability around -30 relative to the TSS but at different levels [25] promoting a continuous range of control on gene transcription. Either way, we believe that our observations call for further biological analysis and that our predictions will be useful for future assess- ment of the relationships between promoter architecture and gene expression. In this respect, future studies in model plants should be based on a better description of the different organization of core-promoters and include a more precise definition of the location of alternative TSSs based on new sequencing technologies. Methods Promoter sets Arabidopsis thaliana and Oryza sativa promoters were built from transcripts, Expressed Sequence Tags and full-length cDNA (The Arabidopsis Information Resource - TAIR R.6 [41] and The Institute for Geno- mic Research - TIGR R.3 [58] respectively). We used FLAGdb++ [59], an integrative database of plant model genomes, to define the transcriptional units by aligning all the available transcripts to gene models, excluding pseudogenes. We excluded promoters with a 5’UTR smaller than 50 bases in order to avoid truncated UTRs. Our promoter sets included 14927 A. thaliana and 18012 O. sativa sequences extending 1 kb upstream of the predicted TSS and containing the whole 5’UTR. The 15802 Homo sapiens and the 15833 Mus musculus pro- moters were retrieved from the DBTSS [39]. To main- tain a consistent approach to that used to construct the plant promoter set, we selected only the TSSs located the most upstream of genes. Identification of Preferentially Located Motifs For each motif analysed, we extracted all occurrences within a promoter set. The motif position corresponds Page 12 of 15 to the position of the first base of the sequence. Motif distributions were determined using a one-base-long sliding-window with a one-base shift. For each win- dow, we counted the number of promoters containing the motif rather than the number of occurrences to avoid favouring repeated motifs. The promoter sequences were then divided into two regions. First, the [-1000, -300] region was used to learn the distribu- tion model using a simple linear regression and deter- mine a 99% confidence interval. Second, within the [-300, 500] region, we searched for non-evenly distrib- uted motifs, i.e. those exhibiting a peak above the con- fidence interval. We increased the sliding-window size from 1 to 100 bases step-by-step when a second peak was detected or when the learning area contained at least one window within which no motif was detected, in order to avoid non-accurate learning of the distribu- tion model. For each PLM we recorded: (i) the motif distribution, (ii) the window size, (iii) the preferential position, i.e. the position of the peak top in the distribution, (iv) the peak boundaries allowing the functional window to be located, (v) the list of promoters containing the motif and (vi) the Score of Maximal Square relative to the base line or SMS, i.e. the ratio (peak top minus base line)/(upper bound of the confidence interval minus base line). We only considered PLMs characterized by an SMS greater than 1. Sequence distance graph and seed motifs Out of all [-39,-26]-PLMs, we searched for those differing by a single substitution and represented the links by means of a graph. For both TA-PLM and TC-PLM graphs we identified the seed(s), i.e. PLM(s) only con- nected in the graph to PLMs less present in promoters than itself. The graph is thus oriented relative to the seed(s). The TA-PLM graph comprises the TATA-box and all the 15 TATAΔ-PLMs. The TC[-39,-26]-PLM graph was first constructed with the PLMs observed in more than 200 promoters. Then, remaining PLMs were added considering only links with the PLMs making up the graph and not those with the remaining PLMs. Extended motif analyses PLM relevance is ranked by the SMS value. For each PLM, we analyzed the SMS of all motifs extended by one base upstream or downstream of the initial PLM sequence. We referred to these motifs as extended- motifs. Selected extended-motifs were only those that (i) were a PLM, (ii) shared the initial PLM constraints, i.e. a sharp peak in the [-39, -26] region and (iii) were char- acterized by an SMS 1.1-fold higher than that of the initial PLM, i.e. exhibited a stronger topological constraint.