{"id":268,"date":"2013-05-21T20:41:36","date_gmt":"2013-05-21T18:41:36","guid":{"rendered":"http:\/\/jloliverlab.wordpress.com\/?page_id=268"},"modified":"2013-05-21T20:41:36","modified_gmt":"2013-05-21T18:41:36","slug":"wordcluster","status":"publish","type":"page","link":"https:\/\/alu.ugr.es\/bioinfo\/?page_id=268","title":{"rendered":"WordCluster"},"content":{"rendered":"<p><a href=\"http:\/\/bioinfo2.ugr.es\/wordCluster\/wordCluster.php\" target=\"_blank\">WordCluster website<\/a><\/p>\n<div>\n<p>Many <em>k-<\/em>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.<\/p>\n<p>The WordCluster algorithm [1] is able to detect clusters of DNA words (<em>k-<\/em>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 <em>WordCluster <\/em>to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome.<\/p>\n<p><em>WordCluster <\/em>seems to predict biological meaningful clusters of DNA words (<em>k-<\/em>mers) and genomic entities. The implementation of the method into a web server is available at <a href=\"http:\/\/bioinfo2.ugr.es\/wordCluster\/wordCluster.php\">http:\/\/bioinfo2.ugr.es\/wordCluster\/wordCluster.php<\/a> <a title=\"Archive copy of webpage\" href=\"http:\/\/www.webcitation.org\/query.php?url=http:\/\/bioinfo2.ugr.es\/wordCluster\/wordCluster.php&amp;refdoi=10.1186\/1748-7188-6-2\">webcite<\/a> including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes.<\/p>\n<p>[1] Michael Hackenberg, Pedro Carpena, Pedro Bernaola-Galv\u00e1n, Guillermo Barturen, \u00c1ngel M. Alganza and Jos\u00e9 L. Oliver. 2011.<br \/>\nWordCluster: detecting clusters of DNA words and genomic elements<br \/>\nAlgorithms for Molecular Biology <strong>6<\/strong><strong>:<\/strong>2<br \/>\n<a href=\"http:\/\/dx.doi.org\/10.1186\/1748-7188-6-2\">http:\/\/dx.doi.org\/10.1186\/1748-7188-6-2<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>WordCluster website 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 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":32,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-268","page","type-page","status-publish","hentry"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/alu.ugr.es\/bioinfo\/index.php?rest_route=\/wp\/v2\/pages\/268","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/alu.ugr.es\/bioinfo\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/alu.ugr.es\/bioinfo\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/alu.ugr.es\/bioinfo\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/alu.ugr.es\/bioinfo\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=268"}],"version-history":[{"count":0,"href":"https:\/\/alu.ugr.es\/bioinfo\/index.php?rest_route=\/wp\/v2\/pages\/268\/revisions"}],"wp:attachment":[{"href":"https:\/\/alu.ugr.es\/bioinfo\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=268"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}