Abstract
Long non-coding RNAs (lncRNAs) play critical roles in various biological processes, but the function of the majority of lncRNAs is still unclear. One approach for estimating a function of a lncRNA is the identification of its interaction target because functions of lncRNAs are expressed through interaction with other biomolecules in quite a few cases. In this paper, we developed “LncRRIsearch,” which is a web server for comprehensive prediction of human and mouse lncRNA-lncRNA and lncRNA-mRNA interaction. The prediction was conducted using RIblast, which is a fast and accurate RNA-RNA interaction prediction tool. Users can investigate interaction target RNAs of a particular lncRNA through a web interface. In addition, we integrated tissue-specific expression and subcellular localization data for the lncRNAs with the web server. These data enable users to examine tissue-specific or subcellular localized lncRNA interactions. LncRRIsearch is publicly accessible at http://rtools.cbrc.jp/LncRRIsearch/.
Highlights
Long non-coding RNAs were initially considered to be transcriptional noise or experimental artifacts, but recent research has revealed that lncRNAs play important roles in various biological processes, such as cell differentiation (Fatica and Bozzoni, 2014) and functioning of the immune system (Carpenter et al, 2013)
LncRRIsearch supports GENCODE gene/transcript names or IDs as input type, and either query lncRNA or target lncRNA/mRNA is required as input RNA
After specifying a gene of interest, several transcript isoforms derived from the gene are listed for selection of a single lncRNA transcript if multiple isoforms are encoded in the gene
Summary
Long non-coding RNAs (lncRNAs) were initially considered to be transcriptional noise or experimental artifacts, but recent research has revealed that lncRNAs play important roles in various biological processes, such as cell differentiation (Fatica and Bozzoni, 2014) and functioning of the immune system (Carpenter et al, 2013). Several databases or web services have been developed for the function prediction based on lncRNA-mRNA interactions, but there are no web services for comprehensive prediction of human and mouse lncRNA interaction. RISE includes experimentally validated lncRNA-RNA interactions based on high-throughput sequencing methods (Lu et al, 2016; Nguyen et al, 2016), but the number of lncRNA interactions is limited (Gong et al, 2017). The database compiled by Terai et al (2016) contains predicted lncRNA-mRNA and lncRNA-lncRNA interaction data at transcriptome scale, but the database does not store more than one local base-pairing interaction for each lncRNA-RNA interaction. The database includes only human lncRNA-RNA interactions
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