In vitro and in silico processes to identify differentially expressed proteins
In vitro and in silico processes to identify differentially expressed proteins
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DOI：
10.1002/pmic.200300840
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发表时间：
2004-08-01
期刊：
影响因子：
3.4
通讯作者：
Zwahlen, C
中科院分区：
文献类型：
--
作者：
Allet, N;Barrillat, N;Zwahlen, C
我们提出了一个专为执行差异分析而设计的集成蛋白质组学平台。由于可重复的结果对于比较研究至关重要，因此我们解释了如何提高实验室流程每一步的可重复性，例如通过利用我们开发的强大的实验室信息管理系统。我们平台的差异容量通过检测真实样品中的已知标记和加标实验来验证。我们引入了一种创新的二维 (2-D) 图，用于显示与色谱数据相结合的识别结果。该二维图对于检测差异蛋白非常方便。我们还采用标准多变量统计技术来表明肽鉴定评分可用于可靠且灵敏的差异研究。我们通常应用的蛋白质分离方法的兴趣是通过大量统计数据来证明的，并通过与对小体积样品进行的简单鸟枪分析进行比较来补充。通过在质谱数据识别后引入自动集成步骤，我们能够系统地搜索大量数据库，包括人类基因组和表达序列标签。最后，我们解释了如何将严格的数据处理与人类专家的工作相结合来设定高质量标准，从而获得可靠（假阳性< 0.35%）和非冗余的蛋白质鉴定。
We present an integrated proteomics platform designed for performing differential analyses. Since reproducible results are essential for comparative studies, we explain how we improved reproducibility at every step of our laboratory processes, e.g. by taking advantage of the powerful laboratory information management system we developed. The differential capacity of our platform is validated by detecting known markers in a real sample and by a spiking experiment. We introduce an innovative two-dimensional (2-D) plot for displaying identification results combined with chromatographic data. This 2-D plot is very convenient for detecting differential proteins. We also adapt standard multivariate statistical techniques to show that peptide identification scores can be used for reliable and sensitive differential studies. The interest of the protein separation approach we generally apply is justified by numerous statistics, complemented by a comparison with a simple shotgun analysis performed on a small volume sample. By introducing an automatic integration step after mass spectrometry data identification, we are able to search numerous databases systematically, including the human genome and expressed sequence tags. Finally, we explain how rigorous data processing can be combined with the work of human experts to set high quality standards, and hence obtain reliable (false positive < 0.35%) and nonredundant protein identifications.