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dc.contributor.authorKalkatawi, Manal M.
dc.contributor.authorMagana-Mora, Arturo
dc.contributor.authorJankovic, Boris R.
dc.contributor.authorBajic, Vladimir B.
dc.date.accessioned2018-09-12T21:07:35Z
dc.date.available2018-09-12T21:07:35Z
dc.date.issued2018-09-01
dc.identifier.citationKalkatawi M, Magana-Mora A, Jankovic B, Bajic VB (2018) DeepGSR: An optimized deep-learning structure for the recognition of genomic signals and regions. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/bty752.
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.doi10.1093/bioinformatics/bty752
dc.identifier.urihttp://hdl.handle.net/10754/628696
dc.description.abstractMotivation \nRecognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have been developed to recognize GSRs, their pure computational identification remains challenging. Moreover, various GSRs usually require a specialized set of features for developing robust recognition models. Recently, deep-learning (DL) methods have been shown to generate more accurate prediction models than ‘shallow’ methods without the need to develop specialized features for the problems in question. Here, we explore the potential use of DL for the recognition of GSRs. \nResults \nWe developed DeepGSR, an optimized DL architecture for the prediction of different types of GSRs. The performance of the DeepGSR structure is evaluated on the recognition of polyadenylation signals (PAS) and translation initiation sites (TIS) of different organisms: human, mouse, bovine, and fruit fly. The results show that DeepGSR outperformed the state-of-the-art methods, reducing the classification error rate of the PAS and TIS prediction in the human genome by up to 29% and 86%, respectively. Moreover, the cross-organisms and genome-wide analyses we performed, confirmed the robustness of DeepGSR and provided new insights into the conservation of examined GSRs across species.
dc.description.sponsorshipWe are grateful to Mohammad Shoaib Amini for helping with the data extraction. This research made use of the resources of the compute and GPU clusters at King Abdullah University of Science & Technology (KAUST), Thuwal, Saudi Arabia. This work was supported by the King Abdullah University of Science and Technology (KAUST) through the baseline research fund BAS/1/1606-01-01 for Vladimir B. Bajic. The open access charges for this article are covered from the same fund.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty752/5089227
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.titleDeepGSR: An optimized deep-learning structure for the recognition of genomic signals and regions
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBioinformatics
dc.eprint.versionPost-print
dc.contributor.institutionKing Abdulaziz University, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia
dc.contributor.institutionSaudi Aramco, EXPEC-ARC, Drilling Technology Team, Dhahran, Saudi Arabia
kaust.personKalkatawi, Manal M.
kaust.personMagana-Mora, Arturo
kaust.personJankovic, Boris R.
kaust.personBajic, Vladimir B.
kaust.grant.numberBAS/1/1606-01-01
refterms.dateFOA2018-09-13T11:58:11Z


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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