MOTIVATION: 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.
RESULTS: 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.
AVAILABILITY AND IMPLEMENTATION: The source code of RIblast is freely available 
at https://github.com/fukunagatsu/RIblast .
CONTACT: t.fukunaga@kurenai.waseda.jp or mhamada@waseda.jp.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.