Ing data is likely to enhance our process also as other TFBS predictionbased strategies. In conclusion,our FR strategy circumvents biases which former methodology suffers from,and we could identify some meaningful cooccurring TFBS pairs,certainly one of which was experimentally supported. We think this approach can assist us detect combinatorial interactions in between TFs inside the regulation of transcription,and we also believe that this sets a basis for future developments in computational identification of combinatorial gene regulation. A web-based application of our process,which we get in touch with REgulatory MOtif Combination Detector (REMOCOD),is readily available at our web site .(A),and absolutely artificial sequences (B),semiartificial CpGhigh sequences (C),and semiartificial CpGlow sequences (D). Additional file : Figure S (PPT,Powerpoint file) Genomewide tendencies of Frequency Ratios for randomly selected mers in human and mouse promoter sequences. Plots of GC content differences (Yaxis) versus FR values (Xaxis) are shown for all human promoters (A),all mouse promoters (B),human CpGhigh promoters (C),mouse CpGhigh promoters (D),human CpGlow promoters (E),and mouse CpGlow promoters (F). Added file PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/21526200 : Figure S (PPT,Powerpoint file) Heatmap representation of the average expression values for each in the clusters obtained in the GNF GeneAtlas mouse information. Extra file : Table S (XLS,Excel Spreadsheet) Summary of principal tissues for the clusters obtained in the GNF GeneAtlas data. Further file : Table S (XLS,Excel Spreadsheet) Summary of overrepresented PWM motifs in tissuespecific sets of mouse promoters (GNF GeneAtlas information and Amit et al. data) Additional file : Figure S (PPT,Powerpoint file) Histogram of your PWMtoPWM GC content material variations of cooccurring motifs ZL006 biological activity predicted by three approaches. Cooccurrences predicted by the FR measure are least impacted by PWMtoPWM GC content material variations. The distribution of GC content variations of predicted cooccurring pairs of PWMs is shown for the PWMs located to be drastically cooccurring with an overrepresented motif as outlined by FR values (“cooccurring motifs,FR”),for the PWMs located to be cooccurring with an overrepresented motif as outlined by Pocc (“cooccurring motifs,Pocc”),and for the PWMs discovered to be cooccurring with an overrepresented motif according to the strategy of Sudarsanam et al. (“cooccurring motifs,Sudarsanam”). For the latter two approaches the pairs with the most significant cooccurrence have been used. Extra file : Figure S (PPT,Powerpoint file) Heatmap representation of clusters of TLRstimulated DC gene expression data referred to inside the key text. Added file : Table S (XLS,Excel Spreadsheet) Summary for the cooccurrences in tissuespecific sets of mouse promoters (GNF GeneAtlas data and Amit et al. data).Added materialAdditional file : Figure S (PPT,Powerpoint file) Workflow of our framework for the detection of cooccurring motifs. The analysis of genomewide tendencies begins with a set of TFBSs,predicted in promoter sequences plus a set of PWMs. For each pair of motifs,FR values are calculated,and utilized for further evaluation of genomewide tendencies. The evaluation of cooccurrences in sets of coregulated genes similarly starts with the prediction of TFBSs. Employing these,considerably overrepresented TFBSs are detected,and for every motif the tendency to cooccur with each and every on the overrepresented motifs is analysed. The significance with the cooccurrences is evaluated using a random sampling a.