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LncRRIsearch: A Web Server for lncRNA-RNA Interaction Prediction Integrated With Tissue-Specific Expression and
Tsukasa Fukunaga1,2, Junichi Iwakiri3,4, Yukiteru Ono5
1Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, Tokyo, Japan.
Frontiers in Genetics
|June 14, 2019
Summary
Researchers developed LncRRIsearch, a web tool predicting interactions for long non-coding RNAs (lncRNAs) with other RNAs. This aids in understanding lncRNA functions by identifying their molecular targets.
Area of Science:
- Molecular Biology
- Bioinformatics
- Genomics
Background:
- Long non-coding RNAs (lncRNAs) are crucial in biological processes, yet their functions remain largely unknown.
- Identifying lncRNA interaction targets is a key strategy to elucidate their functions, as these are often mediated through interactions with other biomolecules.
Purpose of the Study:
- To develop a comprehensive web server, LncRRIsearch, for predicting interactions between human and mouse long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs).
- To provide a user-friendly platform for investigating lncRNA-lncRNA and lncRNA-mRNA interactions and their potential functional roles.
Main Methods:
- Developed LncRRIsearch, a web server utilizing RIblast, a rapid and accurate tool for RNA-RNA interaction prediction.
- Integrated tissue-specific expression and subcellular localization data into the web server to refine interaction predictions.
Main Results:
- LncRRIsearch enables comprehensive prediction of lncRNA-lncRNA and lncRNA-mRNA interactions for human and mouse.
- The server allows users to explore interaction targets of specific lncRNAs via a web interface.
- Integrated data facilitates the examination of tissue-specific or subcellular localized lncRNA interactions.
Conclusions:
- LncRRIsearch provides a valuable resource for researchers studying lncRNA functions and interactions.
- The tool aids in understanding the biological roles of lncRNAs by predicting their interaction partners.
- The integration of expression and localization data enhances the biological relevance of predicted interactions.
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