The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.

中文翻译：

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三种用于评估LC-HRMS代谢组学数据的非目标数据处理流程的比较。

液相色谱高分辨率质谱法（LC-HRMS）原始数据的评估是非目标代谢组学研究中至关重要的一步，可最大程度地减少假阳性结果。多种商业或开源软件解决方案可用于此类数据处理。这项研究旨在比较三种不同的数据处理工作流程（Compound Discoverer 3.1，结合MetaboAnalyst 4.0的XCMS Online和使用R的手动编程工具），以研究非靶向代谢组学研究的LC-HRMS数据。通过将pHLM（合并的人肝微粒体）与合成大麻素A-CHMINACA进行孵育，可以制备出简单但高度标准化的评估数据集。LC-HRMS分析使用正相和反相色谱法进行，然后在正负模式下进行全扫描MS。随后在单独的运行中记录了重要特征的MS / MS光谱。通过其重要特征的数量，峰形质量和多元统计结果来评估每个工作流程的结果。Compound Discoverer作为一种多合一解决方案的特点是易于使用，因此似乎适合于简单和小型的代谢组学研究。这两个开源解决方案允许进行广泛的自定义，但是特别是在R的情况下，必须具备高级编程技能。然而，两者都提供了很高的灵活性，并且可能适合于更复杂的研究和问题。Compound Discoverer作为一种多合一解决方案的特点是易于使用，因此似乎适合于简单和小型的代谢组学研究。这两个开源解决方案允许进行广泛的自定义，但是特别是在R的情况下，必须具备高级编程技能。然而，两者都提供了很高的灵活性，并且可能适合于更复杂的研究和问题。Compound Discoverer作为一种多合一解决方案的特点是易于使用，因此似乎适合于简单和小型的代谢组学研究。这两个开源解决方案允许进行广泛的自定义，但是特别是在R的情况下，必须具备高级编程技能。然而，两者都提供了很高的灵活性，并且可能适合于更复杂的研究和问题。