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m6Acomet: large-scale functional prediction of individual m(6)A RNA methylation sites from an RNA co-methylation network

BACKGROUND: Over one hundred different types of post-transcriptional RNA modifications have been identified in human. Researchers discovered that RNA modifications can regulate various biological processes, and RNA methylation, especially N6-methyladenosine, has become one of the most researched top...

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Autores principales: Wu, Xiangyu, Wei, Zhen, Chen, Kunqi, Zhang, Qing, Su, Jionglong, Liu, Hui, Zhang, Lin, Meng, Jia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6498663/
https://www.ncbi.nlm.nih.gov/pubmed/31046660
http://dx.doi.org/10.1186/s12859-019-2840-3
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author Wu, Xiangyu
Wei, Zhen
Chen, Kunqi
Zhang, Qing
Su, Jionglong
Liu, Hui
Zhang, Lin
Meng, Jia
author_facet Wu, Xiangyu
Wei, Zhen
Chen, Kunqi
Zhang, Qing
Su, Jionglong
Liu, Hui
Zhang, Lin
Meng, Jia
author_sort Wu, Xiangyu
collection PubMed
description BACKGROUND: Over one hundred different types of post-transcriptional RNA modifications have been identified in human. Researchers discovered that RNA modifications can regulate various biological processes, and RNA methylation, especially N6-methyladenosine, has become one of the most researched topics in epigenetics. RESULTS: To date, the study of epitranscriptome layer gene regulation is mostly focused on the function of mediator proteins of RNA methylation, i.e., the readers, writers and erasers. There is limited investigation of the functional relevance of individual m(6)A RNA methylation site. To address this, we annotated human m(6)A sites in large-scale based on the guilt-by-association principle from an RNA co-methylation network. It is constructed based on public human MeRIP-Seq datasets profiling the m(6)A epitranscriptome under 32 independent experimental conditions. By systematically examining the network characteristics obtained from the RNA methylation profiles, a total of 339,158 putative gene ontology functions associated with 1446 human m(6)A sites were identified. These are biological functions that may be regulated at epitranscriptome layer via reversible m(6)A RNA methylation. The results were further validated on a soft benchmark by comparing to a random predictor. CONCLUSIONS: An online web server m6Acomet was constructed to support direct query for the predicted biological functions of m(6)A sites as well as the sites exhibiting co-methylated patterns at the epitranscriptome layer. The m6Acomet web server is freely available at: www.xjtlu.edu.cn/biologicalsciences/m6acomet. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2840-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-64986632019-05-09 m6Acomet: large-scale functional prediction of individual m(6)A RNA methylation sites from an RNA co-methylation network Wu, Xiangyu Wei, Zhen Chen, Kunqi Zhang, Qing Su, Jionglong Liu, Hui Zhang, Lin Meng, Jia BMC Bioinformatics Research Article BACKGROUND: Over one hundred different types of post-transcriptional RNA modifications have been identified in human. Researchers discovered that RNA modifications can regulate various biological processes, and RNA methylation, especially N6-methyladenosine, has become one of the most researched topics in epigenetics. RESULTS: To date, the study of epitranscriptome layer gene regulation is mostly focused on the function of mediator proteins of RNA methylation, i.e., the readers, writers and erasers. There is limited investigation of the functional relevance of individual m(6)A RNA methylation site. To address this, we annotated human m(6)A sites in large-scale based on the guilt-by-association principle from an RNA co-methylation network. It is constructed based on public human MeRIP-Seq datasets profiling the m(6)A epitranscriptome under 32 independent experimental conditions. By systematically examining the network characteristics obtained from the RNA methylation profiles, a total of 339,158 putative gene ontology functions associated with 1446 human m(6)A sites were identified. These are biological functions that may be regulated at epitranscriptome layer via reversible m(6)A RNA methylation. The results were further validated on a soft benchmark by comparing to a random predictor. CONCLUSIONS: An online web server m6Acomet was constructed to support direct query for the predicted biological functions of m(6)A sites as well as the sites exhibiting co-methylated patterns at the epitranscriptome layer. The m6Acomet web server is freely available at: www.xjtlu.edu.cn/biologicalsciences/m6acomet. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2840-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-02 /pmc/articles/PMC6498663/ /pubmed/31046660 http://dx.doi.org/10.1186/s12859-019-2840-3 Text en © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Wu, Xiangyu
Wei, Zhen
Chen, Kunqi
Zhang, Qing
Su, Jionglong
Liu, Hui
Zhang, Lin
Meng, Jia
m6Acomet: large-scale functional prediction of individual m(6)A RNA methylation sites from an RNA co-methylation network
title m6Acomet: large-scale functional prediction of individual m(6)A RNA methylation sites from an RNA co-methylation network
title_full m6Acomet: large-scale functional prediction of individual m(6)A RNA methylation sites from an RNA co-methylation network
title_fullStr m6Acomet: large-scale functional prediction of individual m(6)A RNA methylation sites from an RNA co-methylation network
title_full_unstemmed m6Acomet: large-scale functional prediction of individual m(6)A RNA methylation sites from an RNA co-methylation network
title_short m6Acomet: large-scale functional prediction of individual m(6)A RNA methylation sites from an RNA co-methylation network
title_sort m6acomet: large-scale functional prediction of individual m(6)a rna methylation sites from an rna co-methylation network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6498663/
https://www.ncbi.nlm.nih.gov/pubmed/31046660
http://dx.doi.org/10.1186/s12859-019-2840-3
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