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DoRiNA: a Database of RNA Interactions in Post-transcriptional Regulation

Overview
Specialty Biochemistry
Date 2011 Nov 17
PMID 22086949
Citations 106
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Abstract

In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.

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References
1.
Lebedeva S, Jens M, Theil K, Schwanhausser B, Selbach M, Landthaler M . Transcriptome-wide analysis of regulatory interactions of the RNA-binding protein HuR. Mol Cell. 2011; 43(3):340-52. DOI: 10.1016/j.molcel.2011.06.008. View

2.
Khorshid M, Rodak C, Zavolan M . CLIPZ: a database and analysis environment for experimentally determined binding sites of RNA-binding proteins. Nucleic Acids Res. 2010; 39(Database issue):D245-52. PMC: 3013791. DOI: 10.1093/nar/gkq940. View

3.
Krol J, Loedige I, Filipowicz W . The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet. 2010; 11(9):597-610. DOI: 10.1038/nrg2843. View

4.
Li X, Quon G, Lipshitz H, Morris Q . Predicting in vivo binding sites of RNA-binding proteins using mRNA secondary structure. RNA. 2010; 16(6):1096-107. PMC: 2874161. DOI: 10.1261/rna.2017210. View

5.
Filipowicz W, Bhattacharyya S, Sonenberg N . Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight?. Nat Rev Genet. 2008; 9(2):102-14. DOI: 10.1038/nrg2290. View