Kuksa Pavel P, Li Fan, Kannan Sampath, Gregory Brian D, Leung Yuk Yee, Wang Li-San
Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Children's Hospital Los Angeles, Los Angeles, CA 90027, USA.
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.
最近基于高通量结构敏感全基因组测序的检测方法使得对RNA结构进行大规模研究成为可能，并且通过这些检测对各类RNA的单个RNA结构进行全转录组范围的稳健计算预测，有潜力进一步提高预测准确性。在此，我们描述了HiPR，这是一种用于单核苷酸分辨率RNA结构预测的新方法，它将高通量结构探测数据（DMS-seq、DMS-MaPseq）与一种新的概率折叠算法相结合。在涵盖多种RNA类别的验证数据上，HiPR通常能提高预测RNA结构的准确性，为研究人员提供了研究RNA结构的新工具。
