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HiPR: High-throughput probabilistic RNA structure inference.

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.

SUBMITTER: Kuksa PP

PROVIDER: S-EPMC7327253 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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HiPR: High-throughput probabilistic RNA structure inference.

Kuksa Pavel P PP Li Fan F Kannan Sampath S Gregory Brian D BD Leung Yuk Yee YY Wang Li-San LS

Computational and structural biotechnology journal 20200608

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 pro ...[more]

PMID: 32637050

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