Institute of Physiology, Czech Academy of Sciences, Videnska 1083, 14220 Prague, Czechia.
Institute of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria.
Anal Chem. 2023 Aug 29;95(34):12600-12604. doi: 10.1021/acs.analchem.3c02039. Epub 2023 Aug 16.
With the increasing number of lipidomic studies, there is a need for an efficient and automated analysis of lipidomic data. One of the challenges faced by most existing approaches to lipidomic data analysis is lipid nomenclature. The systematic nomenclature of lipids contains all available information about the molecule, including its hierarchical representation, which can be used for statistical evaluation. The Lipid Over-Representation Analysis (LORA) web application (https://lora.metabolomics.fgu.cas.cz) analyzes this information using the Java-based Goslin framework, which translates lipid names into a standardized nomenclature. Goslin provides the level of lipid hierarchy, including information on headgroups, acyl chains, and their modifications, up to the "complete structure" level. LORA allows the user to upload the experimental query and reference data sets, select a grammar for lipid name normalization, and then process the data. The user can then interactively explore the results and perform lipid over-representation analysis based on selected criteria. The results are graphically visualized according to the lipidome hierarchy. The lipids present in the most over-represented terms (lipids with the highest number of enriched shared structural features) are defined as Very Important Lipids (VILs). For example, the main result of a demo data set is the information that the query is significantly enriched with "glycerophospholipids" containing "acyl 20:4" at the "-2 position". These terms define a set of VILs (e.g., PC 18:2/20:4;O and PE 16:0/20:4(5,8,10,14);OH). All results, graphs, and visualizations are summarized in a report. LORA is a tool focused on the smart mining of epilipidomics data sets to facilitate their interpretation at the molecular level.
随着脂质组学研究的不断增加，人们需要一种高效且自动化的脂质组学数据分析方法。大多数现有的脂质组学数据分析方法面临的挑战之一是脂质命名。脂质的系统命名包含有关分子的所有可用信息，包括其层次表示形式，可用于统计评估。脂质过表达分析（LORA）网络应用程序（https://lora.metabolomics.fgu.cas.cz）使用基于 Java 的 Goslin 框架分析此信息，该框架将脂质名称转换为标准化命名。Goslin 提供脂质层次结构的级别，包括有关头基、酰基链及其修饰的信息，最高可达“完整结构”级别。LORA 允许用户上传实验查询和参考数据集，选择脂质名称标准化的语法，然后处理数据。然后，用户可以根据所选标准交互式地探索结果并执行脂质过表达分析。结果根据脂质组学层次结构以图形方式可视化。在最过表达的术语（具有最高数量的富集共享结构特征的脂质）中存在的脂质被定义为非常重要的脂质（VILs）。例如，演示数据集的主要结果是查询以“酰基 20:4”在“-2 位”的“甘油磷脂”显著富集的信息。这些术语定义了一组 VILs（例如，PC 18:2/20:4；O 和 PE 16:0/20:4(5,8,10,14)；OH）。所有结果、图形和可视化结果都汇总在一份报告中。LORA 是一种专注于挖掘外脂质组学数据集的工具，以促进其在分子水平上的解释。
