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HiPR: High-throughput probabilistic RNA structure inference.

TitleHiPR: High-throughput probabilistic RNA structure inference.
Publication TypeJournal Article
Year of Publication2020
AuthorsKuksa PP, Li F, Kannan S, Gregory BD, Leung YYee, San Wang L-
JournalComput Struct Biotechnol J
Date Published2020

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.

Pubmed Link
Alternate JournalComput Struct Biotechnol J
PubMed ID32637050
PubMed Central IDPMC7327253

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