Automated Data Processing Workflows for Non-Expert Users of NMR Facilities.

IF 1.9 3区 化学 Q3 CHEMISTRY, MULTIDISCIPLINARY
Armin Afrough, Maria Pérez-Mendigorri, Thomas Vosegaard
{"title":"Automated Data Processing Workflows for Non-Expert Users of NMR Facilities.","authors":"Armin Afrough, Maria Pérez-Mendigorri, Thomas Vosegaard","doi":"10.1002/mrc.5540","DOIUrl":null,"url":null,"abstract":"<p><p>The cost and complexity of modern NMR spectrometers have led to the establishment of centralized, ultrahigh-field facilities with multiple instruments that benefit from shared infrastructure and expertise. Many users have no NMR background, as they come from diverse scientific fields. This requires either heavy involvement of NMR experts in the data treatment or that data processing workflows are made user-friendly, robust, and amenable to automation. This paper discusses how at the Danish Center for Ultrahigh Field NMR Spectroscopy at Aarhus University we develop automated-or guided-data processing workflows to serve the broad community of users of the Center. By providing consistency checks in the algorithms and reporting intermediate results, our data analysis tools raise flags if they are-or are likely-failing. We illustrate this approach with two examples: an automated quantitative lipidomics workflow and a semi-automated multi-exponential relaxation analysis in food matrices. The lipidomics workflow uses <sup>1</sup>H-<sup>31</sup>P TOCSY spectra, database matching, and quantitative <sup>31</sup>P measurements, while color-coded reliability labels highlight potential pitfalls. The multi-exponential relaxation analysis automatically determines an appropriate value for the regularization parameter via the L-curve. Both examples show how guided automation reduces expert supervision and accelerates data processing. We plan to further refine these automated workflows, share our software openly, and explore additional application areas to foster a semi-automated NMR facility.</p>","PeriodicalId":18142,"journal":{"name":"Magnetic Resonance in Chemistry","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic Resonance in Chemistry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1002/mrc.5540","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

The cost and complexity of modern NMR spectrometers have led to the establishment of centralized, ultrahigh-field facilities with multiple instruments that benefit from shared infrastructure and expertise. Many users have no NMR background, as they come from diverse scientific fields. This requires either heavy involvement of NMR experts in the data treatment or that data processing workflows are made user-friendly, robust, and amenable to automation. This paper discusses how at the Danish Center for Ultrahigh Field NMR Spectroscopy at Aarhus University we develop automated-or guided-data processing workflows to serve the broad community of users of the Center. By providing consistency checks in the algorithms and reporting intermediate results, our data analysis tools raise flags if they are-or are likely-failing. We illustrate this approach with two examples: an automated quantitative lipidomics workflow and a semi-automated multi-exponential relaxation analysis in food matrices. The lipidomics workflow uses 1H-31P TOCSY spectra, database matching, and quantitative 31P measurements, while color-coded reliability labels highlight potential pitfalls. The multi-exponential relaxation analysis automatically determines an appropriate value for the regularization parameter via the L-curve. Both examples show how guided automation reduces expert supervision and accelerates data processing. We plan to further refine these automated workflows, share our software openly, and explore additional application areas to foster a semi-automated NMR facility.

NMR设施的非专家用户的自动数据处理工作流。
现代核磁共振光谱仪的成本和复杂性导致建立了集中式的超高场设施,其中包含多个仪器,这些仪器受益于共享的基础设施和专业知识。许多用户没有核磁共振背景,因为他们来自不同的科学领域。这要么需要NMR专家大量参与数据处理,要么需要数据处理工作流程对用户友好、健壮且易于自动化。本文讨论了如何在奥胡斯大学的丹麦超高场核磁共振波谱中心开发自动化或引导数据处理工作流程,以服务于该中心的广泛用户社区。通过在算法中提供一致性检查并报告中间结果,我们的数据分析工具会在它们失败或可能失败时发出标记。我们用两个例子来说明这种方法:一个自动化的定量脂质组学工作流程和一个半自动化的食品矩阵多指数松弛分析。脂质组学工作流程使用1H-31P TOCSY光谱、数据库匹配和定量31P测量,而颜色编码的可靠性标签突出了潜在的缺陷。多指数松弛分析通过l曲线自动确定正则化参数的合适值。这两个例子都展示了引导自动化如何减少专家监督并加速数据处理。我们计划进一步完善这些自动化工作流程,公开分享我们的软件,并探索其他应用领域,以培育半自动核磁共振设施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.70
自引率
10.00%
发文量
99
审稿时长
1 months
期刊介绍: MRC is devoted to the rapid publication of papers which are concerned with the development of magnetic resonance techniques, or in which the application of such techniques plays a pivotal part. Contributions from scientists working in all areas of NMR, ESR and NQR are invited, and papers describing applications in all branches of chemistry, structural biology and materials chemistry are published. The journal is of particular interest not only to scientists working in academic research, but also those working in commercial organisations who need to keep up-to-date with the latest practical applications of magnetic resonance techniques.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信