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用于大规模选择反应监测实验的自动化工作流程。

Automated workflow for large-scale selected reaction monitoring experiments.

机构信息

Institute of Molecular Systems Biology , ETH Zurich, Zurich, Switzerland.

出版信息

J Proteome Res. 2012 Mar 2;11(3):1644-53. doi: 10.1021/pr200844d. Epub 2012 Feb 10.

Abstract

Targeted proteomics allows researchers to study proteins of interest without being drowned in data from other, less interesting proteins or from redundant or uninformative peptides. While the technique is mostly used for smaller, focused studies, there are several reasons to conduct larger targeted experiments. Automated, highly robust software becomes more important in such experiments. In addition, larger experiments are carried out over longer periods of time, requiring strategies to handle the sometimes large shift in retention time often observed. We present a complete proof-of-principle software stack that automates most aspects of selected reaction monitoring workflows, a targeted proteomics technology. The software allows experiments to be easily designed and carried out. The steps automated are the generation of assays, generation of mass spectrometry driver files and methods files, and the import and analysis of the data. All data are normalized to a common retention time scale, the data are then scored using a novel score model, and the error is subsequently estimated. We also show that selected reaction monitoring can be used for label-free quantification. All data generated are stored in a relational database, and the growing resource further facilitates the design of new experiments. We apply the technology to a large-scale experiment studying how Streptococcus pyogenes remodels its proteome under stimulation of human plasma.

摘要

靶向蛋白质组学使研究人员能够研究感兴趣的蛋白质,而不会被其他不那么有趣的蛋白质或冗余或无信息的肽的数据所淹没。虽然该技术主要用于较小的、集中的研究,但有几个原因需要进行更大规模的靶向实验。在这种实验中,自动化、高度稳健的软件变得更加重要。此外,更大的实验持续时间更长,需要策略来处理通常观察到的保留时间有时较大的偏移。我们提出了一个完整的原理验证软件栈,该软件栈自动化了靶向蛋白质组学技术——选择反应监测工作流程的大多数方面。该软件允许轻松设计和执行实验。自动化的步骤包括检测的生成、质谱驱动文件和方法文件的生成,以及数据的导入和分析。所有数据都被归一化为共同的保留时间尺度,然后使用新的评分模型对数据进行评分,并随后估计误差。我们还表明,选择反应监测可用于无标记定量。生成的所有数据都存储在关系数据库中,随着资源的不断增长,进一步促进了新实验的设计。我们将该技术应用于一项大规模实验中,该实验研究了化脓性链球菌在人血浆刺激下如何重塑其蛋白质组。

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