dc.contributor.author Han, Namshik en dc.contributor.author Noyes, HA en dc.contributor.author Brass, A en dc.date.accessioned 2017-04-21T07:33:49Z dc.date.available 2017-04-21T07:33:49Z dc.identifier.issn 1471-2105 dc.identifier.uri https://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.sponsorship This 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.language eng en dc.language.iso en en dc.publisher BioMed Central dc.subject machine learning en dc.subject transcriptional regulatory network en dc.subject transcription factor binding site en dc.subject gene expression en dc.title TIGERi: Modeling and visualizing the responses to perturbation of a transcription factor network en dc.type Article prism.publicationName BMC Bioinformatics en dc.identifier.doi 10.17863/CAM.9102 dcterms.dateAccepted 2017-01-06 en rioxxterms.version AM en rioxxterms.licenseref.uri http://www.rioxx.net/licenses/all-rights-reserved en rioxxterms.licenseref.startdate 2017-01-06 en dc.contributor.orcid Han, Namshik [0000-0002-7741-6384] dc.identifier.eissn 1471-2105 rioxxterms.type Journal Article/Review en pubs.funder-project-id Wellcome Trust (092096/Z/10/Z) pubs.funder-project-id Cancer Research UK (A14492) cam.orpheus.success Thu Jan 30 12:56:37 GMT 2020 - The item has an open VoR version. * rioxxterms.freetoread.startdate 2100-01-01
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