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dc.contributor.authorMinas, Giorgos
dc.contributor.authorMomiji, Hiroshi
dc.contributor.authorJenkins, Dafyd J
dc.contributor.authorCosta, Maria J
dc.contributor.authorRand, David A
dc.contributor.authorFinkenstädt, Bärbel
dc.date.accessioned2018-08-03T09:30:09Z
dc.date.available2018-08-03T09:30:09Z
dc.date.issued2017-06-26
dc.identifier.citationMinas , G , Momiji , H , Jenkins , D J , Costa , M J , Rand , D A & Finkenstädt , B 2017 , ' ReTrOS : a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression data ' , BMC Bioinformatics , vol. 18 , 316 . https://doi.org/10.1186/s12859-017-1695-8en
dc.identifier.issn1471-2105
dc.identifier.otherPURE: 255153781
dc.identifier.otherPURE UUID: 8942ed41-e70f-4fc1-bc76-ef0037b40761
dc.identifier.otherPubMed: 28651569
dc.identifier.otherPubMedCentral: PMC5485715
dc.identifier.otherScopus: 85021190946
dc.identifier.otherORCID: /0000-0001-7953-706X/work/47136659
dc.identifier.urihttp://hdl.handle.net/10023/15759
dc.descriptionThis work was supported through providing funds by the Biotechnology and Biological Sciences Research Council [BB/F005806/1, BB/F005237/1]; and the Engineering and Physical Sciences Research Council [EP/C544587/1 to DAR].en
dc.description.abstractBACKGROUND: Given the development of high-throughput experimental techniques, an increasing number of whole genome transcription profiling time series data sets, with good temporal resolution, are becoming available to researchers. The ReTrOS toolbox (Reconstructing Transcription Open Software) provides MATLAB-based implementations of two related methods, namely ReTrOS-Smooth and ReTrOS-Switch, for reconstructing the temporal transcriptional activity profile of a gene from given mRNA expression time series or protein reporter time series. The methods are based on fitting a differential equation model incorporating the processes of transcription, translation and degradation. RESULTS: The toolbox provides a framework for model fitting along with statistical analyses of the model with a graphical interface and model visualisation. We highlight several applications of the toolbox, including the reconstruction of the temporal cascade of transcriptional activity inferred from mRNA expression data and protein reporter data in the core circadian clock in Arabidopsis thaliana, and how such reconstructed transcription profiles can be used to study the effects of different cell lines and conditions. CONCLUSIONS: The ReTrOS toolbox allows users to analyse gene and/or protein expression time series where, with appropriate formulation of prior information about a minimum of kinetic parameters, in particular rates of degradation, users are able to infer timings of changes in transcriptional activity. Data from any organism and obtained from a range of technologies can be used as input due to the flexible and generic nature of the model and implementation. The output from this software provides a useful analysis of time series data and can be incorporated into further modelling approaches or in hypothesis generation.
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofBMC Bioinformaticsen
dc.rights© The Author(s) 2017. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.subjectGene transcriptionen
dc.subjectTime seriesen
dc.subjectTranscriptional switchesen
dc.subjectCircadian timingen
dc.subjectQA76 Computer softwareen
dc.subjectQH426 Geneticsen
dc.subjectDASen
dc.subject.lccQA76en
dc.subject.lccQH426en
dc.titleReTrOS : a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression dataen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.identifier.doihttps://doi.org/10.1186/s12859-017-1695-8
dc.description.statusPeer revieweden


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