An accessibility-incorporated method for accurate prediction of RNA-RNA interactions from sequence data.
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.



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.



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.
Department of RNA Biology and Neuroscience, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan.
Yuki
Y
Kato
Center for iPS Cell Research and Application (CiRA), Kyoto University, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
Tomoya
T
Mori
Faculty of Science and Technology, Keio University, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan.
Kengo
K
Sato
Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan.
Shingo
S
Maegawa
Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan.
Hiroshi
H
Hosokawa
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.
Tatsuya
T
Akutsu
D020029:Base Pairing; D019295:Computational Biology / Q000379:methods*; D062105:Molecular Docking Simulation; D012313:RNA / Q000737:chemistry / Q000378:metabolism*; D012984:Software
D012313:RNA