Algorithms for the global mapping of RNA-RNA interactomes

Abstract

RNA-RNA intra- and intermolecular interactions are fundamental for numerous biological processes. While there are reasonable approaches to map RNA secondary structures genome-wide, understanding how different RNAs interact to carry out their regulatory functions requires mapping of intermolecular base pairs. RNA-RNA interaction prediction algorithms alone are not capable to consider all biological factors, thus, they suffer from low accuracy. Recently, different strategies to detect RNA-RNA duplexes in living cells, so called direct duplex detection (DDD) methods, have been developed. Common to all is the psoralen-mediated in vivo RNA crosslinking followed by RNA Proximity Ligation to join the two interacting RNA strands. Sequencing of the RNA via classical RNA-Seq and subsequent specialised bioinformatic analyses, which results in the prediction of intra- and intermolecular RNA-RNA interactions. Existing approaches adapt standard RNA-seq analysis pipelines but often neglect inherent features of RNA-RNA interactions that are useful for filtering and statistical assessment. In this work, RNAnue is presented, a general pipeline for the inference of RNA-RNA interactions from DDD experiments that takes into account hybridisation potential and statistical significance to improve prediction accuracy. RNAnue was applied to data from different DDD studies, and the results were compared to those of the original methods. This showed that RNAnue performs better in terms of prediction quantity and quality.