MOTIVATION: Secondary metabolites (SM) are structurally diverse natural products of high pharmaceutical importance. Genes involved in their biosynthesis are often organized in clusters, i.e., are co-localized and co-expressed. In silico cluster prediction in eukaryotic genomes remains problematic mainly due to the high variability of the clusters' content and lack of other distinguishing sequence features. RESULTS: We present Cluster Assignment by Islands of Sites (CASSIS), a method for SM cluster prediction in eukaryotic genomes, and Secondary Metabolites by InterProScan (SMIPS), a tool for genome-wide detection of SM key enzymes ('anchor' genes): polyketide synthases, non-ribosomal peptide synthetases and dimethylallyl tryptophan synthases. Unlike other tools based on protein similarity, CASSIS exploits the idea of co-regulation of the cluster genes, which assumes the existence of common regulatory patterns in the cluster promoters. The method searches for 'islands' of enriched cluster-specific motifs in the vicinity of anchor genes. It was validated in a series of cross-validation experiments and showed high sensitivity and specificity. AVAILABILITY AND IMPLEMENTATION: CASSIS and SMIPS are freely available at https://sbi.hki-jena.de/cassis CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Prediction of transcription factor binding sites (TFBSs) is crucial for promoter modeling and network inference. Quality of the predictions is spoiled by numerous false positives, which persist as the main problem for all presently available TFBS search methods. RESULTS: We suggest a novel approach, which is alternative to widely used position weight matrices (PWMs) and Hidden Markov Models. Each motif of the input set is used as a search template to scan a query sequence. Found motifs are assigned scores depending on the non-randomness of the motif's occurrence, the number of matching searching motifs and the number of mismatches. The non-randomness is estimated by comparison of observed numbers of matching motifs with those predicted to occur by chance. The latter can be calculated given the base compositions of the motif and the query sequence. The method does not require preliminary alignment of the input motifs, hence avoiding uncertainties introduced by the alignment procedure. In comparison with PWM-based tools, our method demonstrates higher precision by the same sensitivity and specificity. It also tends to outperform methods combining pattern and PWM search. Most important, it allows reducing the number of false positive predictions significantly. AVAILABILITY: The method is implemented in a tool called SiTaR (Site Tracking and Recognition) and is available at http://sbi.hki-jena.de/sitar/index.php. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The state of the art in promoter modeling for higher eukaryotes is predicting not single transcription factor binding sites (TFBSs), but their combinations. The new tool utilizes a previously developed method of distance distributions of TFBS pairs. We model the random distribution of distances and compare it with the distribution observed in the query sequences. Comparison of the profiles allows filtering out the 'noise' and retaining the potentially functional combinations. This approach has proved its usefulness as a filtering technique for the selection of TFBS pairs for promoter modeling and is now implemented as a tool in R. As an input, it can use the outputs of three different TFBS- and motif-predictive tools (Gibbs Sampler for motifs, Match and MEME/FIMO for PWM-based search). The output is a list of predicted pairs on overrepresented distances with assigned scores, P-values and plots showing the distribution of pairs in the input sequences. AVAILABILITY: The tool is available at https://www.omnifung.hki-jena.de/Rpad/Distance_Scan/index.htm.