| Autor: |
Pavel P. Kuksa, Fan Li, Sampath Kannan, Brian D. Gregory, Yuk Yee Leung, Li-San Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Computational and Structural Biotechnology Journal, Vol 18, Iss , Pp 1539-1547 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2001-0370 |
| DOI: |
10.1016/j.csbj.2020.06.004 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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