PRIblast: A highly efficient parallel application for comprehensive lncRNA–RNA interaction prediction

Abstract

Long non-coding RNAs (lncRNAs) play a key role in several biological processes and scientists are constantly trying to come up with new strategies to elucidate their functions. One common approach to characterize these sequences consists in predicting their interactions with other RNA fragments. Nevertheless, the high computational cost of the bioinformatics tools developed for this purpose prevents their application to large-scale datasets. This paper presents pRIblast, a highly efficient parallel application for comprehensive lncRNA–RNA interaction prediction based on the state-of-the-art RIblast tool, which has been proved to show superior biological accuracy compared to other counterparts in previous experimental evaluations. Benchmarking on a multicore CPU cluster shows that pRIblast is able to compute in a few hours analyses that would need more than three months to complete with the original RIblast algorithm, always achieving the same level of prediction accuracy. Furthermore, this novel application can process large input datasets that cannot be processed with the former tool. pRIblast is free software publicly available to download at https://github.com/UDC-GAC/pRIblast under the MIT license.