Metabolomics Association of North America (MANA), NMR Special Interest Group, Edmonton, Canada.
Institute for Bioscience and Biotechnology Research, National Institute of Standards and Technology, University of Maryland, Gudelsky Drive, Rockville, MD, 20850, USA.
Metabolomics. 2024 Mar 13;20(2):41. doi: 10.1007/s11306-024-02090-6.
The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse.
The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies.
We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.
美国国立癌症研究所于 2022 年 10 月发布了一份信息请求（RFI；NOT-CA-23-007），征求有关使用和再利用代谢组学数据的意见。该 RFI 旨在收集有关代谢组学数据存储、管理和使用/再利用的最佳实践的意见。
北美代谢组学协会（MANA）的核磁共振（NMR）兴趣小组制定了一套关于基于 NMR 和在较小程度上基于质谱（MS）的代谢组学数据集的存储、归档、使用和再利用的建议。这些建议是基于 MANA 内代谢组学研究人员的集体经验制定的，他们正在生成、处理和分析各种代谢组学数据集，涵盖实验（样品处理和准备、NMR/MS 代谢组学数据采集、处理和光谱分析）到计算（光谱处理的自动化、单变量和多变量统计分析、代谢物预测和鉴定、多组学数据集成等）研究。
我们提供了我们对代谢组学数据使用和再利用的集体观点的摘要，并阐述了一些关于最佳实践的建议，旨在鼓励研究人员加强努力，最大限度地利用代谢组学数据、多组学数据集成，并提高代谢组学研究的整体科学影响力。
