https://www.socictopen.socict.org/files/original/720684482aa99af7abd1a8d197de5fee.pdf 74b0e3a081177df8edd168d0838d0a66 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource <it>WordCluster</it>: detecting clusters of DNA words and genomic elements Creator An entity primarily responsible for making the resource Oliver José L, Alganza Ángel M, Bernaola-Galván Pedro, Barturen Guillermo, Carpena Pedro, Hackenberg Michael Description An account of the resource Abstract Background Many k-mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds. Results We introduce here an algorithm to detect clusters of DNA words (k-mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used WordCluster to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome. Conclusions WordCluster seems to predict biological meaningful clusters of DNA words (k-mers) and genomic entities. The implementation of the method into a web server is available at http://bioinfo2.ugr.es/wordCluster/wordCluster.php including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes. Date A point or period of time associated with an event in the lifecycle of the resource 2011 Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/1748-7188-6-2 Source A related resource from which the described resource is derived Algorithms for Molecular Biology Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General), Genetics Language A language of the resource EN