An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics
An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics
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DOI：
10.1021/pr300992u
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发表时间：
2013-04-01
影响因子：
4.4
通讯作者：
Malinstroemt, Lars
中科院分区：
文献类型：
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作者：
Weisser, Hendrik;Nahnsen, Sven;Malinstroemt, Lars
我们提出了一种用于定量无标记 LC-MS/MS 数据集中肽和蛋白质的计算管道。该管道由OpenMS软件框架中的工具组成，适用于大型实验（50+样本）的处理。我们描述了为实现该管道的实施而引入 OpenMS 的多项增强功能。它们包括用于原始数据质心、特征检测、多个相关测量对齐的新算法，以及用于计算肽和蛋白质丰度的新工具。在可能的情况下，我们将新算法的性能与 OpenMS 中已建立的对应算法的性能进行比较。我们根据两个小数据集验证管道，这些数据集为量化提供了基本事实。在那里，我们还将我们的结果与 MaxQuant 和 Progenesis LC-MS 的结果进行比较，这两种用于分析无标记数据的流行替代方案。然后，我们展示了如何将我们的软件应用于包含 58 次 LC-MS/MS 运行的大型异构数据集。
We present a computational pipeline for the quantification of peptides and proteins in label-free LC-MS/MS data sets. The pipeline is composed of tools from the OpenMS software framework and is applicable to the processing of large experiments (50+ samples). We describe several enhancements that we have introduced to OpenMS to realize the implementation of this pipeline. They include new algorithms for centroiding of raw data, for feature detection, for the alignment of multiple related measurements, and a new tool for the calculation of peptide and protein abundances. Where possible, we compare the performance of the new algorithms to that of their established counterparts in OpenMS. We validate the pipeline on the basis of two small data sets that provide ground truths for the quantification. There, we also compare our results to those of MaxQuant and Progenesis LC-MS, two popular alternatives for the analysis of label-free data. We then show how our software can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.