{"id":157469,"date":"2007-01-01T00:00:00","date_gmt":"2007-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/spacer-identification-of-cis-regulatory-elements-with-non-contiguous-critical-residues\/"},"modified":"2018-10-16T20:45:38","modified_gmt":"2018-10-17T03:45:38","slug":"spacer-identification-of-cis-regulatory-elements-with-non-contiguous-critical-residues","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/spacer-identification-of-cis-regulatory-elements-with-non-contiguous-critical-residues\/","title":{"rendered":"SPACER: identification of cis-regulatory elements with non-contiguous critical residues"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Motivation: Many transcription factors bind to sites that are long and loosely related to each other. De novo identification of such motifs is computationally challenging. In this article, we propose a novel semi-greedy algorithm over the space of all IUPAC degenerate strings to identify the most over-represented highly degenerate motifs. Results: We present an implementation of this algorithm, named SPACER (Separated Pattern-based Algorithm for cis-Element Recognition) and demonstrate its effectiveness in identifying gapped&#8217; and highly degenerate motifs. We compare SPACER&#8217;s performance against ten motif finders on 42 experimentally defined regulons from Bacillus subtilis, Escherichia coli and Saccharomyces cerevisiae. These motif finders cover a wide range of both enumerative and statistical approaches, including programs specifically designed for prokaryotic and gapped&#8217; motifs. Availability: A Java 1.4 implementation is freely available on the Web at http:\/\/genie.Dartmouth.edu\/SPACER\/ Contact: robert.h.gross@dartmouth.edu Supplementary information: Supplementary data are available at Bioinformatics online.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Motivation: Many transcription factors bind to sites that are long and loosely related to each other. De novo identification of such motifs is computationally challenging. In this article, we propose a novel semi-greedy algorithm over the space of all IUPAC degenerate strings to identify the most over-represented highly degenerate motifs. Results: We present an implementation [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"8","msr_journal":"Bioinformatics","msr_number":"","msr_organization":"","msr_pages_string":"1029-1031","msr_page_range_start":"1029","msr_page_range_end":"1031","msr_series":"","msr_volume":"23","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Arijit Chakravarty, Radhika S. Khetani, Charles E. DeZiel, Robert H. 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