The use of retention time is often critical for the identification of compounds in metabolomic and lipidomic studies. Standards are frequently unavailable for the retention time measurement of many metabolites, thus the ability to predict retention time for these compounds is highly valuable. A number of studies have applied machine learning to predict retention times, but applying a published machine learning model to different lab conditions is difficult. This is due to variation between chromatographic equipment, methods, and columns used for analysis. Recreating a machine learning model is likewise difficult without a dedicated bioinformatician. Herein we present QSRR Automator, a software package to automate retention time prediction model creation and demonstrate its utility by testing data from multiple chromatography columns from previous publications and in-house work. Analysis of these data sets shows similar accuracy to published models, demonstrating the software’s utility in metabolomic and lipidomic studies.

中文翻译：

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QSRR Automator：在脂质组学和代谢组学中用于自动预测保留时间的工具。

在代谢组学和脂质组学研究中，保留时间的使用通常对于鉴定化合物至关重要。标准品经常无法用于许多代谢物的保留时间测量，因此预测这些化合物的保留时间的能力非常有价值。许多研究已经将机器学习应用于预测保留时间，但是将已发布的机器学习模型应用于不同的实验室条件是困难的。这是由于色谱设备，方法和用于分析的色谱柱之间存在差异。没有专门的生物信息学家，重建机器学习模型也同样困难。在这里，我们介绍QSRR自动化器，一个软件包，用于自动化保留时间预测模型的创建并通过测试来自先前出版物和内部工作的多个色谱柱中的数据来证明其实用性。对这些数据集的分析显示出与已发布模型相似的准确性，从而证明了该软件在代谢组学和脂质组学研究中的效用。