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Pavel P. Kuksa, Fan Li, Sampath Kannan, Brian D. Gregory, Yuk Yee Leung, and Li-San Wang. HiPR: High-throughput probabilistic RNA structure inference. Computational and Structural Biotechnology Journal, 18:1539 – 1547, 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.
@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}}
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