Proteomics Platform, CIC bioGUNE, CIBERehd, ProteoRed, Derio, Bizkaia, Spain.
Proteomics. 2010 Apr;10(8):1545-56. doi: 10.1002/pmic.200900255.
A frequent goal of MS-based proteomics experiments nowadays is to quantify changes in the abundance of proteins across several biological samples. The iTRAQ labeling method is a powerful technique; when combined with LC coupled to MS/MS it allows relative quantitation of up to eight different samples simultaneously. Despite the usefulness of iTRAQ current software solutions have limited functionality and require the combined use of several software programs for analysis of the data from different MS vendors. We developed an integrated tool, now available in the virtual expert mass spectrometrist (VEMS) program, for database-dependent search of MS/MS spectra, quantitation and database storage for iTRAQ-labeled samples. VEMS also provides useful alternative report types for large-scale quantitative experiments. The implemented statistical algorithms build on quantitative algorithms previously used in proposed iTRAQ tools as described in detail herein. We propose a new algorithm, which provides more accurate peptide ratios for data that show an intensity-dependent saturation. The accuracy of the proposed iTRAQ algorithm and the performance of VEMS are demonstrated by comparing results from VEMS, MASCOT and PEAKS Q obtained by analyzing data from a reference mixture of six proteins. Users can download VEMS and test data from "http://www.portugene.com/software.html".
目前，基于 MS 的蛋白质组学实验的一个常见目标是定量跨多个生物样本的蛋白质丰度变化。iTRAQ 标记方法是一种强大的技术；当与 LC 与 MS/MS 结合使用时，它允许同时相对定量多达 8 种不同的样本。尽管 iTRAQ 很有用，但当前的软件解决方案功能有限，并且需要结合使用多个软件程序来分析来自不同 MS 供应商的数据。我们开发了一种集成工具，现在可在虚拟专家质谱仪 (VEMS) 程序中使用，用于对 MS/MS 光谱进行数据库依赖的搜索、对 iTRAQ 标记样本进行定量和数据库存储。VEMS 还为大规模定量实验提供了有用的替代报告类型。所实现的统计算法基于本文详细描述的先前在提议的 iTRAQ 工具中使用的定量算法。我们提出了一种新算法，该算法为显示强度依赖性饱和的数据提供更准确的肽比。通过分析来自六种蛋白质参考混合物的数据来比较 VEMS、MASCOT 和 PEAKS Q 获得的结果，证明了所提议的 iTRAQ 算法和 VEMS 的性能。用户可以从“http://www.portugene.com/software.html”下载 VEMS 和测试数据。
