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ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome

Metabolic syndrome is a cluster of the most dangerous heart attack risk factors (diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure), and has become a major global threat to human health. A number of studies have demonstrated that hundreds of non-...

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Autores principales: Yao, Dengju, Zhan, Xiaojuan, Zhan, Xiaorong, Kwoh, Chee Keong, Sun, Yuezhongyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798904/
https://www.ncbi.nlm.nih.gov/pubmed/31637139
http://dx.doi.org/10.7717/peerj.7909
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author Yao, Dengju
Zhan, Xiaojuan
Zhan, Xiaorong
Kwoh, Chee Keong
Sun, Yuezhongyi
author_facet Yao, Dengju
Zhan, Xiaojuan
Zhan, Xiaorong
Kwoh, Chee Keong
Sun, Yuezhongyi
author_sort Yao, Dengju
collection PubMed
description Metabolic syndrome is a cluster of the most dangerous heart attack risk factors (diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure), and has become a major global threat to human health. A number of studies have demonstrated that hundreds of non-coding RNAs, including miRNAs and lncRNAs, are involved in metabolic syndrome-related diseases such as obesity, type 2 diabetes mellitus, hypertension, etc. However, these research results are distributed in a large number of literature, which is not conducive to analysis and use. There is an urgent need to integrate these relationship data between metabolic syndrome and non-coding RNA into a specialized database. To address this need, we developed a metabolic syndrome-associated non-coding RNA database (ncRNA2MetS) to curate the associations between metabolic syndrome and non-coding RNA. Currently, ncRNA2MetS contains 1,068 associations between five metabolic syndrome traits and 627 non-coding RNAs (543 miRNAs and 84 lncRNAs) in four species. Each record in ncRNA2MetS database represents a pair of disease-miRNA (lncRNA) association consisting of non-coding RNA category, miRNA (lncRNA) name, name of metabolic syndrome trait, expressive patterns of non-coding RNA, method for validation, specie involved, a brief introduction to the association, the article referenced, etc. We also developed a user-friendly website so that users can easily access and download all data. In short, ncRNA2MetS is a complete and high-quality data resource for exploring the role of non-coding RNA in the pathogenesis of metabolic syndrome and seeking new treatment options. The website is freely available at http://www.biomed-bigdata.com:50020/index.html
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spelling pubmed-67989042019-10-21 ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome Yao, Dengju Zhan, Xiaojuan Zhan, Xiaorong Kwoh, Chee Keong Sun, Yuezhongyi PeerJ Bioinformatics Metabolic syndrome is a cluster of the most dangerous heart attack risk factors (diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure), and has become a major global threat to human health. A number of studies have demonstrated that hundreds of non-coding RNAs, including miRNAs and lncRNAs, are involved in metabolic syndrome-related diseases such as obesity, type 2 diabetes mellitus, hypertension, etc. However, these research results are distributed in a large number of literature, which is not conducive to analysis and use. There is an urgent need to integrate these relationship data between metabolic syndrome and non-coding RNA into a specialized database. To address this need, we developed a metabolic syndrome-associated non-coding RNA database (ncRNA2MetS) to curate the associations between metabolic syndrome and non-coding RNA. Currently, ncRNA2MetS contains 1,068 associations between five metabolic syndrome traits and 627 non-coding RNAs (543 miRNAs and 84 lncRNAs) in four species. Each record in ncRNA2MetS database represents a pair of disease-miRNA (lncRNA) association consisting of non-coding RNA category, miRNA (lncRNA) name, name of metabolic syndrome trait, expressive patterns of non-coding RNA, method for validation, specie involved, a brief introduction to the association, the article referenced, etc. We also developed a user-friendly website so that users can easily access and download all data. In short, ncRNA2MetS is a complete and high-quality data resource for exploring the role of non-coding RNA in the pathogenesis of metabolic syndrome and seeking new treatment options. The website is freely available at http://www.biomed-bigdata.com:50020/index.html PeerJ Inc. 2019-10-15 /pmc/articles/PMC6798904/ /pubmed/31637139 http://dx.doi.org/10.7717/peerj.7909 Text en ©2019 Yao 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
Yao, Dengju
Zhan, Xiaojuan
Zhan, Xiaorong
Kwoh, Chee Keong
Sun, Yuezhongyi
ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome
title ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome
title_full ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome
title_fullStr ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome
title_full_unstemmed ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome
title_short ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome
title_sort ncrna2mets: a manually curated database for non-coding rnas associated with metabolic syndrome
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798904/
https://www.ncbi.nlm.nih.gov/pubmed/31637139
http://dx.doi.org/10.7717/peerj.7909
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