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dc.contributor.authorRapakoulia, Trisevgeni
dc.contributor.authorGao, Xin
dc.contributor.authorHuang, Yi
dc.contributor.authorde Hoon, Michiel
dc.contributor.authorOkada-Hatakeyama, Mariko
dc.contributor.authorSuzuki, Harukazu
dc.contributor.authorArner, Erik
dc.date.accessioned2017-08-21T06:28:04Z
dc.date.available2017-08-21T06:28:04Z
dc.date.issued2017-08-14
dc.identifier.citationRapakoulia T, Gao X, Huang Y, de Hoon M, Okada-Hatakeyama M, et al. (2017) Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/btx503.
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.doi10.1093/bioinformatics/btx503
dc.identifier.urihttp://hdl.handle.net/10754/625365
dc.description.abstractMotivation: Drug combination therapy for treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect, while reducing the likelihood of drug resistance. In order to reduce time and cost spent in comprehensive screens, methods are needed which can model additive effects of possible drug combinations. Results: We here show that the transcriptional response to combinatorial drug treatment at promoters, as measured by single molecule CAGE technology, is accurately described by a linear combination of the responses of the individual drugs at a genome wide scale. We also find that the same linear relationship holds for transcription at enhancer elements. We conclude that the described approach is promising for eliciting the transcriptional response to multidrug treatment at promoters and enhancers in an unbiased genome wide way, which may minimize the need for exhaustive combinatorial screens.
dc.description.sponsorshipThe research reported in this work was supported by RIKEN CLST Center Director’s Strategic Program MNC. Additional funding was provided by King Abdullah University of Science and Technology (KAUST), JSPS KAKENHI Grant No.15KT0084 and RIKEN Epigenome and Single Cell Project Grants to MO-H.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx503/4082269/Genomescale-regression-analysis-reveals-a-linear
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.titleGenome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBioinformatics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionRIKEN Center for Life Science Technologies, Molecular Network Control Genomics Unit, Yokohama, Kanagawa 230-0045, Japan
dc.contributor.institutionRIKEN Center for Life Science Technologies (Division of Genomic Technologies) (CLST (DGT)), Yokohama, Kanagawa 230-0045, Japan
dc.contributor.institutionInstitute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
dc.contributor.institutionRIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa 230-0045, Japan
kaust.personRapakoulia, Trisevgeni
kaust.personRapakoulia, Trisevgeni
kaust.personGao, Xin
kaust.personGao, Xin
refterms.dateFOA2018-06-14T04:35:51Z
dc.date.published-online2017-08-14
dc.date.published-print2017-12-01


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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