Jiang, Jie, Song, Bowen, Tang, Yujiao, Chen, Kunqi, Wei, Zhen and Meng, Jia 
ORCID: 0000-0003-3455-205X
(2020)
m5UPred: A Web Server for the Prediction of RNA 5-Methyluridine Sites from Sequences.
MOLECULAR THERAPY-NUCLEIC ACIDS, 22.
pp. 742-747.
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m5UPred A Web Server for the Prediction of RNA 5-Methyluridine Sites from Sequences.pdf - Published version Download (482kB) | Preview |
Abstract
As one of the widely occurring RNA modifications, 5-methyluridine (m<sup>5</sup>U) has recently been shown to play critical roles in various biological functions and disease pathogenesis, such as under stress response and during breast cancer development. Precise identification of m<sup>5</sup>U sites on RNA is vital for the understanding of the regulatory mechanisms of RNA life. We present here m5UPred, the first web server for <i>in silico</i> identification of m<sup>5</sup>U sites from the primary sequences of RNA. Built upon the support vector machine (SVM) algorithm and the biochemical encoding scheme, m5UPred achieved reasonable prediction performance with the area under the receiver operating <b>c</b>haracteristic curve (AUC) greater than 0.954 by 5-fold cross-validation and independent testing datasets. To critically test and validate the performance of our newly proposed predictor, the experimentally validated m<sup>5</sup>U sites were further separated by high-throughput sequencing techniques (miCLIP-Seq and FICC-Seq) and cell types (HEK293 and HAP1). When tested on cross-technique and cross-cell-type validation using independent datasets, m5UPred achieved an average AUC of 0.922 and 0.926 under mature mRNA mode, respectively, showing reasonable accuracy and reliability. The m5UPred web server is freely accessible now and it should make a useful tool for the researchers who are interested in m<sup>5</sup>U RNA modification.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 5-methyluridine, SVM, m5U, sequence-derived features, support vector machine |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 16 Aug 2021 13:59 |
| Last Modified: | 18 Jan 2023 21:33 |
| DOI: | 10.1016/j.omtn.2020.09.031 |
| Open Access URL: | https://www.cell.com/molecular-therapy-family/nucl... |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3133695 |
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