The dosage compensation complex (DCC) in Drosophila melanogaster is responsible for up-regulating transcription from the single male X chromosome to equal the transcription from the two X chromosomes in females. Visualization of the DCC, a large ribonucleoprotein complex, on male larval polytene chromosomes reveals that the complex binds selectively to many interbands on the X chromosome. The targeting of the DCC is thought to be in part determined by DNA sequences that are enriched on the X. So far, lack of knowledge about DCC binding sites has prevented the identification of sequence determinants. Only three binding sites have been identified to date, but analysis of their DNA sequence did not allow the prediction of further binding sites. We have used chromatin immunoprecipitation to identify a number of new DCC binding fragments and characterized them in vivo by visualizing DCC binding to autosomal insertions of these fragments, and we have demonstrated that they possess a wide range of potential to recruit the DCC. By varying the in vivo concentration of the DCC, we provide evidence that this range of recruitment potential is due to differences in affinity of the complex to these sites. We were also able to establish that DCC binding to ectopic high-affinity sites can allow nearby low-affinity sites to recruit the complex. Using the sequences of the newly identified and previously characterized binding fragments, we have uncovered a number of short sequence motifs, which in combination may contribute to DCC recruitment. Our findings suggest that the DCC is recruited to the X via a number of binding sites of decreasing affinities, and that the presence of high- and moderate-affinity sites on the X may ensure that lower-affinity sites are occupied in a context-dependent manner. Our bioinformatics analysis suggests that DCC binding sites may be composed of variable combinations of degenerate motifs.

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: thomas.wolf@leibniz-hki.de or ekaterina.shelest@leibniz-hki.deSupplementary information: Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

The tool is available at https://www.omnifung.hki-jena.de/Rpad/Distance_Scan/index.htm.

Genomic clustering of functionally interrelated genes is not unusual in eukaryotes. In such clusters, co-localized genes are coregulated and often belong to the same pathway. However, biochemical details are still unknown in many cases, hence computational prediction of clusters' structures is beneficial for understanding their functions. Yet, in silico detection of eukaryotic gene clusters (eGCs) remains a challenging task. We suggest a novel method for eGC detection based on consideration of cluster-specific regulatory patterns. The basic idea is to differentiate cluster from non-cluster genes by regulatory elements within their promoter sequences using the density of cluster-specific motifs' occurrences (which is higher within the cluster region) as an additional distinguishing feature. The effectiveness of the method was demonstrated by successful re-identification of functionally characterized clusters. It is also applicable to the detection of yet unknown eGCs. Additionally, the method provides valuable information about the binding sites for cluster-specific regulators.