A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites

RNA 5-methylcytosine (m(5)C) is one of the pillars of post-transcriptional modification (PTCM). A growing body of evidence suggests that m(5)C plays a vital role in RNA metabolism. Accurate localization of RNA m(5)C sites in tissue cells is the premise and basis for the in-depth understanding of the...

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Autores principales: Li, Jianwei, Huang, Yan, Zhou, Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bentham Science Publishers 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324889/
https://www.ncbi.nlm.nih.gov/pubmed/32655293
http://dx.doi.org/10.2174/2213346107666200219124951
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author Li, Jianwei
Huang, Yan
Zhou, Yuan
author_facet Li, Jianwei
Huang, Yan
Zhou, Yuan
author_sort Li, Jianwei
collection PubMed
description RNA 5-methylcytosine (m(5)C) is one of the pillars of post-transcriptional modification (PTCM). A growing body of evidence suggests that m(5)C plays a vital role in RNA metabolism. Accurate localization of RNA m(5)C sites in tissue cells is the premise and basis for the in-depth understanding of the functions of m(5)C. However, the main experimental methods of detecting m(5)C sites are limited to varying degrees. Establishing a computational model to predict modification sites is an excellent complement to wet experiments for identifying m(5)C sites. In this review, we summarized some available m(5)C predictors and discussed the characteristics of these methods.
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spelling pubmed-73248892020-07-10 A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites Li, Jianwei Huang, Yan Zhou, Yuan Curr Genomics Article RNA 5-methylcytosine (m(5)C) is one of the pillars of post-transcriptional modification (PTCM). A growing body of evidence suggests that m(5)C plays a vital role in RNA metabolism. Accurate localization of RNA m(5)C sites in tissue cells is the premise and basis for the in-depth understanding of the functions of m(5)C. However, the main experimental methods of detecting m(5)C sites are limited to varying degrees. Establishing a computational model to predict modification sites is an excellent complement to wet experiments for identifying m(5)C sites. In this review, we summarized some available m(5)C predictors and discussed the characteristics of these methods. Bentham Science Publishers 2020-01 2020-01 /pmc/articles/PMC7324889/ /pubmed/32655293 http://dx.doi.org/10.2174/2213346107666200219124951 Text en © 2020 Bentham Science Publishers https://creativecommons.org/licenses/by-nc/4.0/legalcode This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
spellingShingle Article
Li, Jianwei
Huang, Yan
Zhou, Yuan
A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites
title A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites
title_full A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites
title_fullStr A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites
title_full_unstemmed A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites
title_short A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites
title_sort mini-review of the computational methods used in identifying rna 5-methylcytosine sites
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324889/
https://www.ncbi.nlm.nih.gov/pubmed/32655293
http://dx.doi.org/10.2174/2213346107666200219124951
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