MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes
MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes
复制标题
MAP：基于模型的蛋白质组数据分析，以检测具有显着丰度变化的蛋白质
DOI：
10.1038/s41421-019-0107-9
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发表时间：
2019-08-13
期刊：
影响因子：
33.5
通讯作者：
Shao, Zhen
中科院分区：
文献类型：
--
作者：
Li, Mushan;Tu, Shiqi;Shao, Zhen
基于同位素标记的质谱仪(MS)广泛应用于定量蛋白质组学研究。有了这项技术，数千种蛋白质的相对丰度可以有效地并行分析，极大地促进了对不同样本差异表达蛋白质的检测。然而，这项任务在计算上仍然具有挑战性。在这里，我们提出了一种新的方法，称为蛋白质组数据的基于模型的分析(MAP)。与许多现有方法不同，MAP不需要技术重复来模拟技术和系统错误，而是利用一种新的逐步回归分析来直接评估观察到的蛋白质丰度变化的重要性。我们应用MAP对未分化和分化的小鼠胚胎干细胞(MESCs)的蛋白质组进行了比较，发现它在检测mESC分化过程中差异表达的蛋白质方面比现有的工具具有更好的性能。Http://bioinfo.sibs.ac.cn/shaolab/MAP.提供了一个基于Web的地图应用程序，用于在线数据处理
Isotope-labeling-based mass spectrometry (MS) is widely used in quantitative proteomic studies. With this technique, the relative abundance of thousands of proteins can be efficiently profiled in parallel, greatly facilitating the detection of proteins differentially expressed across samples. However, this task remains computationally challenging. Here we present a new approach, termed Model-based Analysis of Proteomic data (MAP), for this task. Unlike many existing methods, MAP does not require technical replicates to model technical and systematic errors, and instead utilizes a novel step-by-step regression analysis to directly assess the significance of observed protein abundance changes. We applied MAP to compare the proteomic profiles of undifferentiated and differentiated mouse embryonic stem cells (mESCs), and found it has superior performance compared with existing tools in detecting proteins differentially expressed during mESC differentiation. A web-based application of MAP is provided for online data processing at http://bioinfo.sibs.ac.cn/shaolab/MAP.