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dc.contributor.authorKhamis, Abdullah M.
dc.contributor.authorMotwalli, Olaa Amin
dc.contributor.authorOliva, Romina
dc.contributor.authorJankovic, Boris R.
dc.contributor.authorMedvedeva, Yulia
dc.contributor.authorAshoor, Haitham
dc.contributor.authorEssack, Magbubah
dc.contributor.authorGao, Xin
dc.contributor.authorBajic, Vladimir B.
dc.date.accessioned2018-04-16T11:27:40Z
dc.date.available2018-04-16T11:27:40Z
dc.date.issued2018-04-02
dc.identifier.citationKhamis AM, Motwalli O, Oliva R, Jankovic BR, Medvedeva YA, et al. (2018) A novel method for improved accuracy of transcription factor binding site prediction. Nucleic Acids Research. Available: http://dx.doi.org/10.1093/nar/gky237.
dc.identifier.issn0305-1048
dc.identifier.issn1362-4962
dc.identifier.pmid29617876
dc.identifier.doi10.1093/nar/gky237
dc.identifier.urihttp://hdl.handle.net/10754/627486
dc.description.abstractIdentifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.
dc.description.sponsorshipThe computational analysis for this study was performed on Dragon and Snapdragon compute clusters of the Computational Bioscience Research Center at KAUST. King Abdullah University of Science and Technology (KAUST) [BAS/1/1606-01-01 to V.B.B.]. Funding for open access charge: KAUST [BAS/1/1606-01-01].
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/nar/advance-article/doi/10.1093/nar/gky237/4958206
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectProtein-nucleic acid interaction
dc.subjectComputational Methods
dc.titleA novel method for improved accuracy of transcription factor binding site prediction
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.identifier.journalNucleic Acids Research
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Sciences and Technologies, University ‘Parthenope’ of Naples, Centro Direzionale Isola C4 80143, Naples, Italy
dc.contributor.institutionDepartment of Biological and Medical Physics, Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Moscow Region, Russia
dc.contributor.institutionDepartment of Computational Biology, Vavilov Institute of General Genetics, Russian Academy of Science, 119991 Moscow, Russia
dc.contributor.institutionInstitute of Bioengineering, Research Centre of Biotechnology, Russian Academy of Science, 117312 Moscow, Russia
kaust.personKhamis, Abdullah M.
kaust.personMotwalli, Olaa Amin
kaust.personOliva, Romina
kaust.personJankovic, Boris R.
kaust.personMedvedeva, Yulia
kaust.personAshoor, Haitham
kaust.personEssack, Magbubah
kaust.personGao, Xin
kaust.personBajic, Vladimir B.
kaust.grant.numberBAS/1/1606-01-01
kaust.grant.numberBAS/1/1606-01-01
refterms.dateFOA2018-06-14T04:21:49Z
dc.date.published-online2018-04-02
dc.date.published-print2018-07-06


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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com