iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning.
RNA 5-hydroxymethylcytosine modification
feature representation
machine learning
sequence analysis
web server
Journal
Frontiers in bioengineering and biotechnology
ISSN: 2296-4185
Titre abrégé: Front Bioeng Biotechnol
Pays: Switzerland
ID NLM: 101632513
Informations de publication
Date de publication:
2020
2020
Historique:
received:
15
01
2020
accepted:
05
03
2020
entrez:
17
4
2020
pubmed:
17
4
2020
medline:
17
4
2020
Statut:
epublish
Résumé
RNA 5-hydroxymethylcytosine (5hmC) modification plays an important role in a series of biological processes. Characterization of its distributions in transcriptome is fundamentally important to reveal the biological functions of 5hmC. Sequencing-based technologies allow the high-throughput identification of 5hmC; however, they are labor-intensive, time-consuming, as well as expensive. Thus, there is an urgent need to develop more effective and efficient computational methods, at least complementary to the high-throughput technologies. In this study, we developed iRNA5hmC, a computational predictive protocol to identify RNA 5hmC sites using machine learning. In this predictor, we introduced a sequence-based feature algorithm consisting of two feature representations, (1)
Identifiants
pubmed: 32296686
doi: 10.3389/fbioe.2020.00227
pmc: PMC7137033
doi:
Types de publication
Journal Article
Langues
eng
Pagination
227Informations de copyright
Copyright © 2020 Liu, Chen, Su, Chen and Wei.
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