HiPR: High-throughput probabilistic RNA structure inference.
Comput Struct Biotechnol J
; 18: 1539-1547, 2020.
Article
em En
| MEDLINE
| ID: mdl-32637050
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.
Texto completo:
1
Bases de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Comput Struct Biotechnol J
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos