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dc.contributor.authorKleftogiannis, Dimitrios A.
dc.contributor.authorAshoor, Haitham
dc.contributor.authorZarokanellos, Nikolaos
dc.contributor.authorBajic, Vladimir B.
dc.date.accessioned2017-09-21T09:25:34Z
dc.date.available2017-09-21T09:25:34Z
dc.date.issued2017-09-14
dc.identifier.citationKleftogiannis D, Ashoor H, Zarokanellos N, Bajic VB (2017) Motif signatures of transcribed enhancers. Available: http://dx.doi.org/10.1101/188557.
dc.identifier.doi10.1101/188557
dc.identifier.urihttp://hdl.handle.net/10754/625498
dc.description.abstractIn mammalian cells, transcribed enhancers (TrEn) play important roles in the initiation of gene expression and maintenance of gene expression levels in spatiotemporal manner. One of the most challenging questions in biology today is how the genomic characteristics of enhancers relate to enhancer activities. This is particularly critical, as several recent studies have linked enhancer sequence motifs to specific functional roles. To date, only a limited number of enhancer sequence characteristics have been investigated, leaving space for exploring the enhancers genomic code in a more systematic way. To address this problem, we developed a novel computational method, TELS, aimed at identifying predictive cell type/tissue specific motif signatures. We used TELS to compile a comprehensive catalog of motif signatures for all known TrEn identified by the FANTOM5 consortium across 112 human primary cells and tissues. Our results confirm that distinct cell type/tissue specific motif signatures characterize TrEn. These signatures allow discriminating successfully a) TrEn from random controls, proxy of non-enhancer activity, and b) cell type/tissue specific TrEn from enhancers expressed and transcribed in different cell types/tissues. TELS codes and datasets are publicly available at http://www.cbrc.kaust.edu.sa/TELS.
dc.description.sponsorshipThe research reported in this manuscript was supported by the base funding grant No. BAS/1/1606-01-01 of the King Abdullah University of Science and Technology (KAUST) to VBB.
dc.publisherCold Spring Harbor Laboratory
dc.relation.urlhttp://www.biorxiv.org/content/early/2017/09/13/188557
dc.rightsThe copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.titleMotif signatures of transcribed enhancers
dc.typePreprint
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentMarine Science Program
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.eprint.versionPre-print
dc.contributor.institutionCentre for Evolution and Cancer, Division of Molecular Pathology, The Institute of Cancer Research (ICR), London, SW7 3R, United Kingdom
kaust.personAshoor, Haitham
kaust.personZarokanellos, Nikolaos
kaust.personBajic, Vladimir B.
refterms.dateFOA2018-06-13T12:15:58Z


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The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Except where otherwise noted, this item's license is described as The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.