Yuki Kato1, Tomoya Mori2, Kengo Sato3, Shingo Maegawa4, Hiroshi Hosokawa4, Tatsuya Akutsu5. 1. Department of RNA Biology and Neuroscience, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan. 2. Center for iPS Cell Research and Application (CiRA), Kyoto University, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan. 3. Faculty of Science and Technology, Keio University, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan. 4. Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan. 5. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.
Abstract
MOTIVATION: RNA-RNA interactions via base pairing play a vital role in the post-transcriptional regulation of gene expression. Efficient identification of targets for such regulatory RNAs needs not only discriminative power for positive and negative RNA-RNA interacting sequence data but also accurate prediction of interaction sites from positive data. Recently, a few studies have incorporated interaction site accessibility into their prediction methods, indicating the enhancement of predictive performance on limited positive data. RESULTS: Here we show the efficacy of our accessibility-based prediction model RactIPAce on newly compiled datasets. The first experiment in interaction site prediction shows that RactIPAce achieves the best predictive performance on the newly compiled dataset of experimentally verified interactions in the literature as compared with the state-of-the-art methods. In addition, the second experiment in discrimination between positive and negative interacting pairs reveals that the combination of accessibility-based methods including our approach can be effective to discern real interacting RNAs. Taking these into account, our prediction model can be effective to predict interaction sites after screening for real interacting RNAs, which will boost the functional analysis of regulatory RNAs. AVAILABILITY AND IMPLEMENTATION: The program RactIPAce along with data used in this work is available at https://github.com/satoken/ractip/releases/tag/v1.0.1 CONTACT: : ykato@rna.med.osaka-u.ac.jp or shingo@i.kyoto-u.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.
MOTIVATION: RNA-RNA interactions via base pairing play a vital role in the post-transcriptional regulation of gene expression. Efficient identification of targets for such regulatory RNAs needs not only discriminative power for positive and negative RNA-RNA interacting sequence data but also accurate prediction of interaction sites from positive data. Recently, a few studies have incorporated interaction site accessibility into their prediction methods, indicating the enhancement of predictive performance on limited positive data. RESULTS: Here we show the efficacy of our accessibility-based prediction model RactIPAce on newly compiled datasets. The first experiment in interaction site prediction shows that RactIPAce achieves the best predictive performance on the newly compiled dataset of experimentally verified interactions in the literature as compared with the state-of-the-art methods. In addition, the second experiment in discrimination between positive and negative interacting pairs reveals that the combination of accessibility-based methods including our approach can be effective to discern real interacting RNAs. Taking these into account, our prediction model can be effective to predict interaction sites after screening for real interacting RNAs, which will boost the functional analysis of regulatory RNAs. AVAILABILITY AND IMPLEMENTATION: The program RactIPAce along with data used in this work is available at https://github.com/satoken/ractip/releases/tag/v1.0.1 CONTACT: : ykato@rna.med.osaka-u.ac.jp or shingo@i.kyoto-u.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.