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6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning

With the accumulation of data on 6mA modification sites, an increasing number of scholars have begun to focus on the identification of 6mA sites. Despite the recognized importance of 6mA sites, methods for their identification remain lacking, with most existing methods being aimed at their identific...

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Detalles Bibliográficos
Autores principales: Huang, Qianfei, Zhou, Wenyang, Guo, Fei, Xu, Lei, Zhang, Lichao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866889/
https://www.ncbi.nlm.nih.gov/pubmed/33604189
http://dx.doi.org/10.7717/peerj.10813
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author Huang, Qianfei
Zhou, Wenyang
Guo, Fei
Xu, Lei
Zhang, Lichao
author_facet Huang, Qianfei
Zhou, Wenyang
Guo, Fei
Xu, Lei
Zhang, Lichao
author_sort Huang, Qianfei
collection PubMed
description With the accumulation of data on 6mA modification sites, an increasing number of scholars have begun to focus on the identification of 6mA sites. Despite the recognized importance of 6mA sites, methods for their identification remain lacking, with most existing methods being aimed at their identification in individual species. In the present study, we aimed to develop an identification method suitable for multiple species. Based on previous research, we propose a method for 6mA site recognition. Our experiments prove that the proposed 6mA-Pred method is effective for identifying 6mA sites in genes from taxa such as rice, Mus musculus, and human. A series of experimental results show that 6mA-Pred is an excellent method. We provide the source code used in the study, which can be obtained from http://39.100.246.211:5004/6mA_Pred/.
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spelling pubmed-78668892021-02-17 6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning Huang, Qianfei Zhou, Wenyang Guo, Fei Xu, Lei Zhang, Lichao PeerJ Bioinformatics With the accumulation of data on 6mA modification sites, an increasing number of scholars have begun to focus on the identification of 6mA sites. Despite the recognized importance of 6mA sites, methods for their identification remain lacking, with most existing methods being aimed at their identification in individual species. In the present study, we aimed to develop an identification method suitable for multiple species. Based on previous research, we propose a method for 6mA site recognition. Our experiments prove that the proposed 6mA-Pred method is effective for identifying 6mA sites in genes from taxa such as rice, Mus musculus, and human. A series of experimental results show that 6mA-Pred is an excellent method. We provide the source code used in the study, which can be obtained from http://39.100.246.211:5004/6mA_Pred/. PeerJ Inc. 2021-02-03 /pmc/articles/PMC7866889/ /pubmed/33604189 http://dx.doi.org/10.7717/peerj.10813 Text en © 2021 Huang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Huang, Qianfei
Zhou, Wenyang
Guo, Fei
Xu, Lei
Zhang, Lichao
6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning
title 6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning
title_full 6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning
title_fullStr 6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning
title_full_unstemmed 6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning
title_short 6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning
title_sort 6ma-pred: identifying dna n6-methyladenine sites based on deep learning
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866889/
https://www.ncbi.nlm.nih.gov/pubmed/33604189
http://dx.doi.org/10.7717/peerj.10813
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