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The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites
The identification of unknown chemicals has emerged as a significant issue in untargeted metabolome analysis owing to the limited availability of purified standards for identification; this is a major bottleneck for the accumulation of reusable metabolome data in systems biology. Public resources fo...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825447/ https://www.ncbi.nlm.nih.gov/pubmed/36417935 http://dx.doi.org/10.1093/nar/gkac1058 |
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author | Sakurai, Nozomu Yamazaki, Shinichi Suda, Kunihiro Hosoki, Ai Akimoto, Nayumi Takahashi, Haruya Shibata, Daisuke Aoki, Yuichi |
author_facet | Sakurai, Nozomu Yamazaki, Shinichi Suda, Kunihiro Hosoki, Ai Akimoto, Nayumi Takahashi, Haruya Shibata, Daisuke Aoki, Yuichi |
author_sort | Sakurai, Nozomu |
collection | PubMed |
description | The identification of unknown chemicals has emerged as a significant issue in untargeted metabolome analysis owing to the limited availability of purified standards for identification; this is a major bottleneck for the accumulation of reusable metabolome data in systems biology. Public resources for discovering and prioritizing the unknowns that should be subject to practical identification, as well as further detailed study of spending costs and the risks of misprediction, are lacking. As such a resource, we released databases, Food-, Plant- and Thing-Metabolome Repository (http://metabolites.in/foods, http://metabolites.in/plants, and http://metabolites.in/things, referred to as XMRs) in which the sample-specific localization of unknowns detected by liquid chromatography–mass spectrometry in a wide variety of samples can be examined, helping to discover and prioritize the unknowns. A set of application programming interfaces for the XMRs facilitates the use of metabolome data for large-scale analysis and data mining. Several applications of XMRs, including integrated metabolome and genome analyses, are presented. Expanding the concept of XMRs will accelerate the identification of unknowns and increase the discovery of new knowledge. |
format | Online Article Text |
id | pubmed-9825447 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98254472023-01-10 The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites Sakurai, Nozomu Yamazaki, Shinichi Suda, Kunihiro Hosoki, Ai Akimoto, Nayumi Takahashi, Haruya Shibata, Daisuke Aoki, Yuichi Nucleic Acids Res Database Issue The identification of unknown chemicals has emerged as a significant issue in untargeted metabolome analysis owing to the limited availability of purified standards for identification; this is a major bottleneck for the accumulation of reusable metabolome data in systems biology. Public resources for discovering and prioritizing the unknowns that should be subject to practical identification, as well as further detailed study of spending costs and the risks of misprediction, are lacking. As such a resource, we released databases, Food-, Plant- and Thing-Metabolome Repository (http://metabolites.in/foods, http://metabolites.in/plants, and http://metabolites.in/things, referred to as XMRs) in which the sample-specific localization of unknowns detected by liquid chromatography–mass spectrometry in a wide variety of samples can be examined, helping to discover and prioritize the unknowns. A set of application programming interfaces for the XMRs facilitates the use of metabolome data for large-scale analysis and data mining. Several applications of XMRs, including integrated metabolome and genome analyses, are presented. Expanding the concept of XMRs will accelerate the identification of unknowns and increase the discovery of new knowledge. Oxford University Press 2022-11-24 /pmc/articles/PMC9825447/ /pubmed/36417935 http://dx.doi.org/10.1093/nar/gkac1058 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Database Issue Sakurai, Nozomu Yamazaki, Shinichi Suda, Kunihiro Hosoki, Ai Akimoto, Nayumi Takahashi, Haruya Shibata, Daisuke Aoki, Yuichi The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites |
title | The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites |
title_full | The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites |
title_fullStr | The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites |
title_full_unstemmed | The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites |
title_short | The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites |
title_sort | thing metabolome repository family (xmrs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825447/ https://www.ncbi.nlm.nih.gov/pubmed/36417935 http://dx.doi.org/10.1093/nar/gkac1058 |
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