Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA.
Keck Foundation Biotechnology Resource Laboratory, Yale School of Medicine, New Haven, CT 06510, USA.
Biology (Basel). 2014 Jun 5;3(2):383-402. doi: 10.3390/biology3020383.
Multiple Reaction Monitoring (MRM) conducted on a triple quadrupole mass spectrometer allows researchers to quantify the expression levels of a set of target proteins. Each protein is often characterized by several unique peptides that can be detected by monitoring predetermined fragment ions, called transitions, for each peptide. Concatenating large numbers of MRM transitions into a single assay enables simultaneous quantification of hundreds of peptides and proteins. In recognition of the important role that MRM can play in hypothesis-driven research and its increasing impact on clinical proteomics, targeted proteomics such as MRM was recently selected as the Nature Method of the Year. However, there are many challenges in MRM applications, especially data pre‑processing where many steps still rely on manual inspection of each observation in practice. In this paper, we discuss an analysis pipeline to automate MRM data pre‑processing. This pipeline includes data quality assessment across replicated samples, outlier detection, identification of inaccurate transitions, and data normalization. We demonstrate the utility of our pipeline through its applications to several real MRM data sets.
三重四极杆质谱仪上进行的多重反应监测 (MRM) 允许研究人员定量表达一组目标蛋白的水平。每个蛋白通常由几个独特的肽段组成，通过监测每个肽段的预定片段离子（称为转换）可以检测到这些肽段。将大量的 MRM 转换串联到单个测定中，可实现数百种肽段和蛋白的同时定量。鉴于 MRM 在假设驱动的研究中可以发挥重要作用，并且对临床蛋白质组学的影响越来越大，靶向蛋白质组学（如 MRM）最近被选为《自然》年度方法。然而，MRM 应用存在许多挑战，尤其是数据预处理，在实践中许多步骤仍然依赖于对每个观察结果的手动检查。在本文中，我们讨论了一个用于自动化 MRM 数据预处理的分析流程。该流程包括跨重复样本进行数据质量评估、异常值检测、不准确转换的识别以及数据标准化。我们通过将其应用于几个真实的 MRM 数据集来展示我们的流程的实用性。
