HiPR: High-throughput probabilistic RNA structure inference

Comput Struct Biotechnol J. 2020 Jun 8:18:1539-1547. doi: 10.1016/j.csbj.2020.06.004. eCollection 2020.


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.