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用于数据质量评估的脂质组学评分系统介绍。

Introduction of a Lipidomics Scoring System for data quality assessment.

作者信息

Hoffmann Nils, Ahrends Robert, Baker Erin S, Ekroos Kim, Han Xianlin, Holčapek Michal, Liebisch Gerhard, Wenk Markus R, Xia Yu, Köfeler Harald C

机构信息

Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences (IBG-5), Jülich, Germany; Institute of Analytical Chemistry, University of Vienna, Vienna, Austria.

Institute of Analytical Chemistry, University of Vienna, Vienna, Austria.

出版信息

J Lipid Res. 2025 Apr 30:100817. doi: 10.1016/j.jlr.2025.100817.

Abstract

The scientific field of lipidomics has shown a constantly growing publication number in recent years, which is accompanied by an increasing need for quality standards. While the official shorthand nomenclature of lipids is a first and important step towards a reporting quality tool, an additional point score would reflect the quality of reported data at an even more detailed granularity. Thus, we propose a lipidomics scoring scheme that considers all the different layers of analytical information to be obtained by mass spectrometry, chromatography, and ion mobility spectrometry and awards scoring points for each of them. Furthermore, the scoring scheme is integrated with the annotation levels as proposed by the official shorthand nomenclature, with a point score, which roughly correlates with the annotated compound details. The merit of such a scoring system is the fact that it abstracts evidence for structural information into a number, which gives even the non-lipidomics expert an idea about the reporting, and by extension, data quality at first glance. Additionally, it could serve as an aid for internal quality control and for data quality assessment in the peer review process.

摘要

近年来,脂质组学这一科学领域的出版物数量持续增长,与此同时,对质量标准的需求也日益增加。虽然脂质的官方简写命名法是迈向报告质量工具的首要且重要的一步,但额外的评分将以更详细的粒度反映报告数据的质量。因此,我们提出了一种脂质组学评分方案,该方案考虑了通过质谱、色谱和离子淌度谱获得的所有不同层次的分析信息,并为每个信息授予评分点。此外,该评分方案与官方简写命名法提出的注释级别相结合,其评分大致与注释的化合物细节相关。这种评分系统的优点在于,它将结构信息的证据抽象为一个数字,这使得即使是非脂质组学专家也能一眼了解报告情况,并进而了解数据质量。此外,它还可作为内部质量控制和同行评审过程中数据质量评估的辅助工具。

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