Poolsap Unyanee, Kato Yuki, Sato Kengo, Akutsu Tatsuya
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.
J Bioinform Comput Biol. 2011 Dec;9(6):697-713. doi: 10.1142/s0219720011005628.
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/.
预测RNA与RNA之间的相互作用是阐明小型非编码RNA可能功能的关键，并且已经提出了许多计算方法来分析相互作用的RNA二级结构。在本文中，我们专注于预测靶RNA的结合位点，这些靶RNA有望以一般的相互作用形式与调控性反义RNA相互作用。为此，我们提出了bistaRNA，这是一种预测靶RNA多个结合位点的新方法。bistaRNA采用代表杂交结构得分的结合谱，从而降低相互作用预测的计算成本。bistaRNA考虑平衡相互作用结构的集合，并试图使用动态规划使预期准确性最大化。与几种竞争方法相比，在真实相互作用数据上的实验结果验证了bistaRNA具有良好的准确性和快速的计算时间。此外，我们旨在给定特定反义RNA的情况下找到新的靶标，这为反义RNA调控提供了有趣的见解。bistaRNA用C++实现。该程序和补充材料可在http://rna.naist.jp/program/bistarna/获取。
