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DIANA-LncBase: experimentally verified and computationally predicted microRNA targets on long non-coding RNAs

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Auteur
Paraskevopoulou, M. D.; Georgakilas, G.; Kostoulas, N.; Reczko, M.; Maragkakis, M.; Dalamagas, T. M.; Hatzigeorgiou, A. G.
Date
2013
DOI
10.1093/nar/gks1246
Sujet
INTEGRATIVE ANNOTATION
TRANSCRIPTOME
EXPRESSION
DIFFERENTIATION
REVEALS
PROTEIN
SITES
CLIP
Biochemistry & Molecular Biology
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Résumé
Recently, the attention of the research community has been focused on long non-coding RNAs (lncRNAs) and their physiological/pathological implications. As the number of experiments increase in a rapid rate and transcriptional units are better annotated, databases indexing lncRNA properties and function gradually become essential tools to this process. Aim of DIANA-LncBase (www.microrna.gr/LncBase) is to reinforce researchers' attempts and unravel microRNA (miRNA)-lncRNA putative functional interactions. This study provides, for the first time, a comprehensive annotation of miRNA targets on lncRNAs. DIANA-LncBase hosts transcriptome-wide experimentally verified and computationally predicted miRNA recognition elements (MREs) on human and mouse lncRNAs. The analysis performed includes an integration of most of the available lncRNA resources, relevant high-throughput HITS-CLIP and PAR-CLIP experimental data as well as state-of-the-art in silico target predictions. The experimentally supported entries available in DIANA-LncBase correspond to > 5000 interactions, while the computationally predicted interactions exceed 10 million. DIANA-LncBase hosts detailed information for each miRNA-lncRNA pair, such as external links, graphic plots of transcripts' genomic location, representation of the binding sites, lncRNA tissue expression as well as MREs conservation and prediction scores.
URI
http://hdl.handle.net/11615/32033
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