title: Using binding profiles to predict binding sites of target RNAs.
abstract: Prediction of RNA-RNA interaction is a key to elucidating possible functions of small non-coding RNAs, and a number of computational methods have been proposed to analyze interacting RNA secondary structures. In this article, we focus on predicting binding sites of target RNAs that are expected to interact with regulatory antisense RNAs in a general form of interaction. For this purpose, we propose bistaRNA, a novel method for predicting multiple binding sites of target RNAs. bistaRNA employs binding profiles that represent scores for hybridized structures, leading to reducing the computational cost for interaction prediction. bistaRNA considers an ensemble of equilibrium interacting structures and seeks to maximize expected accuracy using dynamic programming. Experimental results on real interaction data validate good accuracy and fast computation time of bistaRNA as compared with several competitive methods. Moreover, we aim to find new targets given specific antisense RNAs, which provides interesting insights into antisense RNA regulation. bistaRNA is implemented in C++. The program and Supplementary Material are available at http://rna.naist.jp/program/bistarna/.
authors.affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. unyanee@is.naist.jp
authors.forename: Unyanee
authors.initials: U
authors.lastname: Poolsap
authors.forename: Yuki
authors.initials: Y
authors.lastname: Kato
authors.forename: Kengo
authors.initials: K
authors.lastname: Sato
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Akutsu
mesh_terms: D001665:Binding Sites; D019295:Computational Biology; D030541:Databases, Genetic; D012313:RNA / Q000737:chemistry* / Q000378:metabolism; D016372:RNA, Antisense / Q000737:chemistry / Q000378:metabolism
chemical_list: D016372:RNA, Antisense; D012313:RNA