Title | HiPR: High-throughput probabilistic RNA structure inference. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Kuksa PP, Li F, Kannan S, Gregory BD, Leung YYee, San Wang L- |
Journal | Comput Struct Biotechnol J |
Volume | 18 |
Pagination | 1539-1547 |
Date Published | 2020 |
ISSN | 2001-0370 |
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. |
DOI | 10.1016/j.csbj.2020.06.004 |
Pubmed Link | https://www.ncbi.nlm.nih.gov/pubmed/32637050?dopt=Abstract |
page_expo | External |
Alternate Journal | Comput Struct Biotechnol J |
PubMed ID | 32637050 |
PubMed Central ID | PMC7327253 |
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