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dc.contributor.authorDeyneko, Igor V
dc.contributor.authorKel, Alexander E
dc.contributor.authorKel-Margoulis, Olga V
dc.contributor.authorDeineko, Elena V
dc.contributor.authorWingender, Edgar
dc.contributor.authorWeiss, Siegfried
dc.date.accessioned2013-10-08T10:40:39Zen
dc.date.available2013-10-08T10:40:39Zen
dc.date.issued2013en
dc.identifier.citationMatrixCatch--a novel tool for the recognition of composite regulatory elements in promoters. 2013, 14:241 BMC Bioinformaticsen
dc.identifier.issn1471-2105en
dc.identifier.pmid23924163en
dc.identifier.doi10.1186/1471-2105-14-241en
dc.identifier.urihttp://hdl.handle.net/10033/302967en
dc.description.abstractAccurate recognition of regulatory elements in promoters is an essential prerequisite for understanding the mechanisms of gene regulation at the level of transcription. Composite regulatory elements represent a particular type of such transcriptional regulatory elements consisting of pairs of individual DNA motifs. In contrast to the present approach, most available recognition techniques are based purely on statistical evaluation of the occurrence of single motifs. Such methods are limited in application, since the accuracy of recognition is greatly dependent on the size and quality of the sequence dataset. Methods that exploit available knowledge and have broad applicability are evidently needed.
dc.language.isoenen
dc.rightsArchived with thanks to BMC bioinformaticsen
dc.titleMatrixCatch--a novel tool for the recognition of composite regulatory elements in promoters.en
dc.typeArticleen
dc.contributor.departmentDepartment of Molecular Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany. Igor.Deyneko@helmholtz-hzi.deen
dc.identifier.journalBMC bioinformaticsen
refterms.dateFOA2018-06-12T22:05:54Z
html.description.abstractAccurate recognition of regulatory elements in promoters is an essential prerequisite for understanding the mechanisms of gene regulation at the level of transcription. Composite regulatory elements represent a particular type of such transcriptional regulatory elements consisting of pairs of individual DNA motifs. In contrast to the present approach, most available recognition techniques are based purely on statistical evaluation of the occurrence of single motifs. Such methods are limited in application, since the accuracy of recognition is greatly dependent on the size and quality of the sequence dataset. Methods that exploit available knowledge and have broad applicability are evidently needed.


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