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MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization
MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319475/ https://www.ncbi.nlm.nih.gov/pubmed/32324217 http://dx.doi.org/10.1093/nar/gkaa275 |
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author | Wang, Duolin Liu, Dongpeng Yuchi, Jiakang He, Fei Jiang, Yuexu Cai, Siteng Li, Jingyi Xu, Dong |
author_facet | Wang, Duolin Liu, Dongpeng Yuchi, Jiakang He, Fei Jiang, Yuexu Cai, Siteng Li, Jingyi Xu, Dong |
author_sort | Wang, Duolin |
collection | PubMed |
description | MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web. |
format | Online Article Text |
id | pubmed-7319475 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-73194752020-07-01 MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization Wang, Duolin Liu, Dongpeng Yuchi, Jiakang He, Fei Jiang, Yuexu Cai, Siteng Li, Jingyi Xu, Dong Nucleic Acids Res Web Server Issue MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web. Oxford University Press 2020-07-02 2020-04-23 /pmc/articles/PMC7319475/ /pubmed/32324217 http://dx.doi.org/10.1093/nar/gkaa275 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Web Server Issue Wang, Duolin Liu, Dongpeng Yuchi, Jiakang He, Fei Jiang, Yuexu Cai, Siteng Li, Jingyi Xu, Dong MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization |
title | MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization |
title_full | MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization |
title_fullStr | MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization |
title_full_unstemmed | MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization |
title_short | MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization |
title_sort | musitedeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319475/ https://www.ncbi.nlm.nih.gov/pubmed/32324217 http://dx.doi.org/10.1093/nar/gkaa275 |
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