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正确估计质谱分析中假差异丰度蛋白质率的五个简单而必要的步骤。

Five simple yet essential steps to correctly estimate the rate of false differentially abundant proteins in mass spectrometry analyses.

机构信息

Univ. Grenoble Alpes, CEA, INSERM, BIG-BGE, 38000 Grenoble, France.

Bioinformatics and Biostatistics Hub, C3BI, Institut Pasteur, USR 3756 IP CNRS, 75015 Paris, France; Proteomics Platform, Mass Spectrometry for Biology Unit, Institut Pasteur, USR 2000 IP CNRS, 75015 Paris, France.

出版信息

J Proteomics. 2019 Sep 15;207:103441. doi: 10.1016/j.jprot.2019.103441. Epub 2019 Jul 10.

Abstract

Results from mass spectrometry based quantitative proteomics analysis correspond to a subset of proteins which are considered differentially abundant relative to a control. Their selection is delicate and often requires some statistical expertise in addition to a refined knowledge of the experimental data. To facilitate the selection process, we have considered differential analysis as a five-step process, and here we present the practical aspects of the different steps. Prostar software is used throughout this article for illustration, but the general methodology is applicable with many other tools. By applying the approach detailed here, researchers who may be less familiar with statistical considerations can be more confident in the results they present.

摘要

基于质谱的定量蛋白质组学分析的结果与被认为相对于对照差异丰度的蛋白质子集相对应。它们的选择是微妙的,通常需要一些统计专业知识以及对实验数据的精细了解。为了方便选择过程,我们将差异分析视为一个五步过程,并在此介绍不同步骤的实际方面。本文通篇使用 Prostar 软件进行说明,但一般方法适用于许多其他工具。通过应用此处详述的方法,那些可能不太熟悉统计考虑因素的研究人员可以对他们呈现的结果更有信心。

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