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PLoS One. 2016 Oct 10;11(10):e0164535. doi: 10.1371/journal.pone.0164535. eCollection 2016.

ORTI: An Open-Access Repository of Transcriptional Interactions for Interrogating Mammalian Gene Expression Data.

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Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia.
Diabetes and Metabolism Division, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW, Australia.
Sydney Medical School, The University of Sydney, Sydney, NSW, Australia.
School of Physics, The University of Sydney, Sydney, NSW, Australia.


Transcription factors (TFs) play a fundamental role in coordinating biological processes in response to stimuli. Consequently, we often seek to determine the key TFs and their regulated target genes (TGs) amidst gene expression data. This requires a knowledge-base of TF-TG interactions, which would enable us to determine the topology of the transcriptional network and predict novel regulatory interactions. To address this, we generated an Open-access Repository of Transcriptional Interactions, ORTI, by integrating available TF-TG interaction databases. These databases rely on different types of experimental evidence, including low-throughput assays, high-throughput screens, and bioinformatics predictions. We have subsequently categorised TF-TG interactions in ORTI according to the quality of this evidence. To demonstrate its capabilities, we applied ORTI to gene expression data and identified modulated TFs using an enrichment analysis. Combining this with pairwise TF-TG interactions enabled us to visualise temporal regulation of a transcriptional network. Additionally, ORTI enables the prediction of novel TF-TG interactions, based on how well candidate genes co-express with known TGs of the target TF. By filtering out known TF-TG interactions that are unlikely to occur within the experimental context, this analysis predicts context-specific TF-TG interactions. We show that this can be applied to experimental designs of varying complexities. In conclusion, ORTI is a rich and publicly available database of experimentally validated mammalian transcriptional interactions which is accompanied with tools that can identify and predict transcriptional interactions, serving as a useful resource for unravelling the topology of transcriptional networks.

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