Recent high-throughput structure-sensitive genome-wide sequencing-based assays have enabled large-scale studies of RNA structure, and robust transcriptome-wide computational prediction of individual RNA structures across RNA classes from these assays has potential to further improve the prediction accuracy. Here, we describe HiPR, a novel method for RNA structure prediction at single-nucleotide resolution that combines high-throughput structure probing data (DMS-seq, DMS-MaPseq) with a novel probabilistic folding algorithm. On validation data spanning a variety of RNA classes, HiPR often increases accuracy for predicting RNA structures, giving researchers new tools to study RNA structure.
Keywords: DMS-MaPseq; DMS-seq; High-throughput structure-sensitive sequencing; Probabilistic modeling; RNA structure inference.
© 2020 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.