Gambardella, G., Peluso, I., Montefusco, S. et al. (4 more authors) (2015) A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data. BMC Bioinformatics, 16. 279. ISSN 1471-2105
Abstract
Background Transcription factors (TFs) act downstream of the major signalling pathways functioning as master regulators of cell fate. Their activity is tightly regulated at the transcriptional, post-transcriptional and post-translational level. Proteins modifying TF activity are not easily identified by experimental high-throughput methods.
Results We developed a computational strategy, called Differential Multi-Information (DMI), to infer post-translational modulators of a transcription factor from a compendium of gene expression profiles (GEPs). DMI is built on the hypothesis that the modulator of a TF (i.e. kinase/phosphatases), when expressed in the cell, will cause the TF target genes to be co-expressed. On the contrary, when the modulator is not expressed, the TF will be inactive resulting in a loss of co-regulation across its target genes. DMI detects the occurrence of changes in target gene co-regulation for each candidate modulator, using a measure called Multi-Information. We validated the DMI approach on a compendium of 5,372 GEPs showing its predictive ability in correctly identifying kinases regulating the activity of 14 different transcription factors.
Conclusions DMI can be used in combination with experimental approaches as high-throughput screening to efficiently improve both pathway and target discovery. An on-line web-tool enabling the user to use DMI to identify post-transcriptional modulators of a transcription factor of interest che be found at http://dmi.tigem.it.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Gambardella et al. 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. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 RADIANT - 305626 EUROPEAN COMMISSION - FP6/FP7 BIOPREDYN - 289434 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Dec 2016 16:38 |
Last Modified: | 07 Dec 2016 16:44 |
Published Version: | http://dx.doi.org/10.1186/s12859-015-0700-3 |
Status: | Published |
Publisher: | BioMed Central |
Refereed: | Yes |
Identification Number: | 10.1186/s12859-015-0700-3 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:108788 |