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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.

journal_name

Comput Struct Biotechnol J

journal_title

Computational and structural biotechnology journal

authors

Kuksa PP,Li F,Kannan S,Gregory BD,Leung YY,Wang LS

doi

10.1016/j.csbj.2020.06.004

subject

Has Abstract

pub_date

2020-06-08 00:00:00

pages

1539-1547

issn

2001-0370

pii

S2001-0370(20)30293-2

journal_volume

18

pub_type

杂志文章

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