Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
Japan Society for the Promotion of Science, Tokyo 102-0083, Japan.
Bioinformatics. 2017 Sep 1;33(17):2666-2674. doi: 10.1093/bioinformatics/btx287.
LncRNAs play important roles in various biological processes. Although more than 58 000 human lncRNA genes have been discovered, most known lncRNAs are still poorly characterized. One approach to understanding the functions of lncRNAs is the detection of the interacting RNA target of each lncRNA. Because experimental detections of comprehensive lncRNA-RNA interactions are difficult, computational prediction of lncRNA-RNA interactions is an indispensable technique. However, the high computational costs of existing RNA-RNA interaction prediction tools prevent their application to large-scale lncRNA datasets.
Here, we present 'RIblast', an ultrafast RNA-RNA interaction prediction method based on the seed-and-extension approach. RIblast discovers seed regions using suffix arrays and subsequently extends seed regions based on an RNA secondary structure energy model. Computational experiments indicate that RIblast achieves a level of prediction accuracy similar to those of existing programs, but at speeds over 64 times faster than existing programs.
The source code of RIblast is freely available at https://github.com/fukunagatsu/RIblast .
t.fukunaga@kurenai.waseda.jp or mhamada@waseda.jp.
Supplementary data are available at Bioinformatics online.
长链非编码 RNA 在各种生物过程中发挥着重要作用。尽管已经发现了超过 58000 个人类长链非编码 RNA 基因，但大多数已知的长链非编码 RNA 仍未得到充分表征。了解长链非编码 RNA 功能的一种方法是检测每个长链非编码 RNA 的相互作用 RNA 靶标。由于全面检测长链非编码 RNA-RNA 相互作用具有挑战性，因此计算预测长链非编码 RNA-RNA 相互作用是一项不可或缺的技术。然而，现有 RNA-RNA 相互作用预测工具的高计算成本阻止了它们在大规模长链非编码 RNA 数据集上的应用。
在这里，我们提出了“RIblast”，这是一种基于种子和扩展方法的超快速 RNA-RNA 相互作用预测方法。RIblast 使用后缀数组发现种子区域，然后根据 RNA 二级结构能量模型扩展种子区域。计算实验表明，RIblast 达到了与现有程序相似的预测精度水平，但速度比现有程序快 64 倍以上。
RIblast 的源代码可在 https://github.com/fukunagatsu/RIblast 上免费获得。
t.fukunaga@kurenai.waseda.jp 或 mhamada@waseda.jp。
补充数据可在 Bioinformatics 在线获得。
