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. 2013 Jan;41(Database issue):D239-45.
doi: 10.1093/nar/gks1246. Epub 2012 Nov 28.

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

Maria D Paraskevopoulou  1 , Georgios Georgakilas, Nikos Kostoulas, Martin Reczko, Manolis Maragkakis, Theodore M Dalamagas, Artemis G Hatzigeorgiou
Affiliations

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

Maria D Paraskevopoulou et al. Nucleic Acids Res. 2013 Jan.

Abstract

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.

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Figures

Figure 1.
Figure 1.
DIANA-LncBase analysis pipeline. The best available lncRNA resources have been collected for human and mouse species. Transcripts shorter than 200 nt as well as transcripts presenting high similarity (>90% overlap) have been removed. lncRNAs have been subsequently categorized in sense, antisense, bidirectional and intergenic with the use of a protein-coding reference set consisting of UCSC and Ensembl genes. Additionally, MREs on lncRNAs have been experimentally verified with the use of high-throughput HITS/PAR-CLIP data and in silico predicted with a state-of-the-art algorithm, DIANA-microT-CDS. Integration of miRNA targets, as well as other lncRNA/miRNA-related information, such as transcripts tissue expression and MREs evolutionary conservation, has been the final step for DIANA-LncBase population.
Figure 2.
Figure 2.
Results of a submitted query in DIANA-LncBase experimental module. The interface presents information regarding the specified miRNA–lncRNA interaction. miRNA and gene-related information, as well as the advanced search options have been expanded. This interaction supported by the PAR-CLIP high-throughput data is also predicted from DIANA-microT-CDS.
Figure 3.
Figure 3.
Screenshot of the predicted module in DIANA-LncBase interface. The results of the query regarding a specific miRNA–lncRNA interacting pair are depicted in the form of an expandable list.
See this image and copyright information in PMC

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