ORTI: An Open-Access Repository of Transcriptional Interactions for Interrogating Mammalian Gene Expression Data.
Where this comes from
- Record sourced from PubMed, PMID 27723773.
- Also identified by DOI 10.1371/journal.pone.0164535 and PMC identifier 5056720.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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.
Medical subject headings
- Databases, Genetic
- Gene Expression Profiling
- Gene Expression Regulation
- Gene Regulatory Networks