Long non-coding RNAs (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 characterised. One approach to understanding the functions of lncRNAs is the detection of the interacting RNA target of each lncRNA. Because experimental detection 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 prevents their application to large-scale lncRNA datasets.
Researchers from Waseda University have developed 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 63 times faster than existing programs.
(A) A schematic illustration of the effect of accessible energies. While a segment with low accessible energy tends to form inter-molecular base pairs, a segment with high accessible energy tends not to form inter-molecular base pairs because such a segment tends to form intra- molecular base pairs. (B) Example of hybridization energy calculation. Hybridization energy can be calculated as the sum of stacking energies and loop energies in the formed base-paired structure. Generally, stacking energies stabilise RNA-RNA interactions but loop energies destabilize interactions. This calculation is based on Turner’s energy parameter. (C) Overview of the RIblast algorithm. The interaction energy is defined as the sum of hybridization energy and two accessible energies.
Availability – The source code of RIblast is freely available at: https://github.com/fukunagatsu/RIblast