@article{hipr2020csbj,
	Abstract = {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.},
	Author = {Pavel P. Kuksa and Fan Li and Sampath Kannan and Brian D. Gregory and Yuk Yee Leung and Li-San Wang},
	Date-Added = {2020-06-28 10:23:12 -0400},
	Date-Modified = {2020-06-28 10:31:56 -0400},
	Doi = {https://doi.org/10.1016/j.csbj.2020.06.004},
	Issn = {2001-0370},
	Journal = {Computational and Structural Biotechnology Journal},
	Keywords = {High-throughput structure-sensitive sequencing, RNA structure inference, Probabilistic modeling, DMS-seq, DMS-MaPseq},
	Pages = {1539 - 1547},
	Title = {{HiPR}: {High-throughput} probabilistic {RNA} structure inference},
	Url = {https://doi.org/10.1016/j.csbj.2020.06.004},
	Volume = {18},
	Year = {2020},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S2001037020302932},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.csbj.2020.06.004}}