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dc.contributor.authorUmarov, Ramzan
dc.contributor.authorKuwahara, Hiroyuki
dc.contributor.authorLi, Yu
dc.contributor.authorGao, Xin
dc.contributor.authorSolovyev, Victor
dc.date.accessioned2019-01-09T14:02:44Z
dc.date.available2019-01-09T14:02:44Z
dc.date.issued2019-01-02
dc.identifier.citationUmarov R, Kuwahara H, Li Y, Gao X, Solovyev V (2019) Promoter analysis and prediction in the human genome using sequence-based deep learning models. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/bty1068.
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.doi10.1093/bioinformatics/bty1068
dc.identifier.urihttp://hdl.handle.net/10754/630783
dc.description.abstractMotivation:Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliable tool to analyze long genomic sequences. \nResults:In this work we further develop our deep learning approach that was relatively successful to discriminate short promoter and non-promoter sequences. Instead of focusing on the classification accuracy, in this work we predict the exact positions of the TSS inside the genomic sequences testing every possible location. We studied human promoters to find effective regions for discrimination and built corresponding deep learning models. These models use adaptively constructed negative set, which iteratively improves the model's discriminative ability. Our method significantly outperforms the previously developed promoter prediction programs by considerably reducing the number of false positive predictions. We have achieved error-per-1000-bp rate of 0.02 and have 0.31 errors per correct prediction, which is significantly better than the results of other human promoter predictors. \nAvailability:The developed method is available as a web server at http://www.cbrc.kaust.edu.sa/PromID/.
dc.description.sponsorshipThis work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No. FCC/1/1976-17-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, and URF/1/3412-01.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty1068/5270663
dc.rightsThis is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The version of record is available online at: https://doi.org/10.1093/bioinformatics/bty1068
dc.titlePromoter analysis and prediction in the human genome using sequence-based deep learning models
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.identifier.journalBioinformatics
dc.eprint.versionPost-print
dc.contributor.institutionInstitute of Cytology and Genetics SB RAS, 10 Lavrentiev Ave., Novosibirsk, Russia
kaust.personUmarov, Ramzan
kaust.personKuwahara, Hiroyuki
kaust.personLi, Yu
kaust.personGao, Xin
kaust.grant.numberFCC/1/1976-17-01
kaust.grant.numberFCC/1/1976-18-01
kaust.grant.numberFCC/1/1976-23-01
kaust.grant.numberFCC/1/1976-25-01
kaust.grant.numberFCC/1/1976-26-01
kaust.grant.numberURF/1/3412-01.
refterms.dateFOA2020-01-02T00:00:00Z


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