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dc.contributor.authorHan, Namshiken
dc.contributor.authorNoyes, HAen
dc.contributor.authorBrass, Aen
dc.date.accessioned2017-04-21T07:33:49Z
dc.date.available2017-04-21T07:33:49Z
dc.identifier.issn1471-2105
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/263736
dc.description.abstract$\textbf{Background}$ Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimental and theoretical methodologies. Less work has focused on using these models to examine how TF networks respond to changes in the cellular environment. $\textbf{Methods}$ In this paper , we have developed a simple, pragmatic methodology, TIGERi ($\textbf{T}$ranscription-factor-activity $\textbf{I}$llustrator for $\textbf{G}$lobal $\textbf{E}$xplanation of $\textbf{R}$egulatory $\textbf{i}$nteraction), to model the response of an inferred TF network to changes in cellular environment. The methodology was tested using publicly available data comparing gene expression profiles of a mouse p38α (Mapk14) knock-out line to the original wild-type. $\textbf{Results}$ Using the model, we have examined changes in the TF network resulting from the presence or absence of p38α. A part of this network was confirmed by experimental work in the original paper. Additional relationships were identified by our analysis, for example between p38 α and HNF3, and between p38 α and SOX9, and these are strongly supported by published evidence. FXR and MYC were also discovered in our analysis as two novel links of p38α. To provide a computational methodology to the biomedical communities that has more user-friendly interface, we also developed a standalone GUI (graphical user interface) software for TIGERi and it is freely available at https://gith ub.com/namshik/tigeri/. $\textbf{Conclusions}$ We therefore believe that our computational approach can identify new members of networks and new interactions between members that are supported by published data but have not been integrated into the existing network models. Moreover, ones who want to analyze their own data with TIGER i could use the software without any command line experience. This work could therefore accelerate researches in transcriptional gene regulation in higher eukaryotes.
dc.description.sponsorshipThis work was supported by European Research Council CRIPTON Grant(RG59701), and Institutional funding to the Gurdon Institute by Wellcome Trust Core Grant (092096) and Cancer Research UK Grant (C6946/A14492). The publication charges for this article was funded by European Research Council CRIPTON Grant.
dc.languageengen
dc.language.isoenen
dc.publisherBioMed Central
dc.subjectmachine learningen
dc.subjecttranscriptional regulatory networken
dc.subjecttranscription factor binding siteen
dc.subjectgene expressionen
dc.titleTIGERi: Modeling and visualizing the responses to perturbation of a transcription factor networken
dc.typeArticle
prism.publicationNameBMC Bioinformaticsen
dc.identifier.doi10.17863/CAM.9102
dcterms.dateAccepted2017-01-06en
rioxxterms.versionAMen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-01-06en
dc.contributor.orcidHan, Namshik [0000-0002-7741-6384]
dc.identifier.eissn1471-2105
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idWellcome Trust (092096/Z/10/Z)
pubs.funder-project-idCancer Research UK (A14492)
cam.orpheus.successThu Jan 30 12:56:37 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


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