Comprehensive prediction of lncRNA–RNA interactions in human transcriptome

• Published in: BMC genomics Vol. 17; no. Suppl 1; p. 12
• Main Authors: Terai, Goro, Iwakiri, Junichi, Kameda, Tomoshi, Hamada, Michiaki, Asai, Kiyoshi
• Format: Journal Article
• Language: English
• Published: London BioMed Central 11.01.2016; BioMed Central Ltd
• Subjects: 3' Untranslated Regions; Algorithms; Animal Genetics and Genomics; Biomedical and Life Sciences; Databases, Genetic; Genetic transcription; Humans; Immune system; Internet; Life Sciences; Microarrays; Microbial Genetics and Genomics; MicroRNAs - metabolism; Plant Genetics and Genomics; Proceedings; Proteomics; RNA; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; RNA, Messenger - metabolism; Transcriptome; Tumor Suppressor Protein p53 - genetics; User-Computer Interface; 1/2-sbsRNA; TINCR; RNA–RNA interactions; Interaction energy
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-015-2307-5

Motivation Recent studies have revealed that large numbers of non-coding RNAs are transcribed in humans, but only a few of them have been identified with their functions. Identification of the interaction target RNAs of the non-coding RNAs is an important step in predicting their functions. The current experimental methods to identify RNA–RNA interactions, however, are not fast enough to apply to a whole human transcriptome. Therefore, computational predictions of RNA–RNA interactions are desirable, but this is a challenging task due to the huge computational costs involved. Results Here, we report comprehensive predictions of the interaction targets of lncRNAs in a whole human transcriptome for the first time. To achieve this, we developed an integrated pipeline for predicting RNA–RNA interactions on the K computer, which is one of the fastest super-computers in the world. Comparisons with experimentally-validated lncRNA–RNA interactions support the quality of the predictions. Additionally, we have developed a database that catalogs the predicted lncRNA–RNA interactions to provide fundamental information about the targets of lncRNAs.