Suppr超能文献

使用稳定同位素标记进行蛋白质定量的自动化通用分析工具。

Automated generic analysis tools for protein quantitation using stable isotope labeling.

作者信息

Hsu Wen-Lian, Sung Ting-Yi

机构信息

Institute of Information Science, Academia Sinica, Taipei, Taiwan.

出版信息

Methods Mol Biol. 2010;604:257-72. doi: 10.1007/978-1-60761-444-9_17.

Abstract

Isotope labeling combined with LC-MS/MS provides a robust platform for quantitative proteomics. Protein quantitation based on mass spectral data falls into two categories: one determined by MS/MS scans, e.g., iTRAQ-labeling quantitation, and the other by MS scans, e.g., quantitation using SILAC, ICAT, or (18)O labeling. In large-scale LC-MS proteomic experiments, tens of thousands of MS and MS/MS spectra are generated and need to be analyzed. Data noise further complicates the data analysis. In this chapter, we present two automated tools, called Multi-Q and MaXIC-Q, for MS/MS- and MS-based quantitation analysis. They are designed as generic platforms that can accommodate search results from SEQUEST and Mascot, as well as mzXML files converted from raw files produced by various mass spectrometers. Toward accurate quantitation analysis, Multi-Q determines detection limits of the user's instrument to filter out outliers and MaXIC-Q adopts stringent validation on our constructed projected ion mass spectra to ensure correct data for quantitation.

摘要

同位素标记与液相色谱-串联质谱联用为定量蛋白质组学提供了一个强大的平台。基于质谱数据的蛋白质定量可分为两类:一类由串联质谱扫描确定,如iTRAQ标记定量;另一类由质谱扫描确定,如使用稳定同位素标记氨基酸细胞培养法(SILAC)、同位素标记亲和标签(ICAT)或(18)O标记进行定量。在大规模液相色谱-质谱蛋白质组学实验中,会生成数以万计的质谱和串联质谱图谱,需要进行分析。数据噪声进一步使数据分析复杂化。在本章中,我们介绍了两种自动化工具,即Multi-Q和MaXIC-Q,用于基于串联质谱和质谱的定量分析。它们被设计为通用平台,可容纳来自SEQUEST和 Mascot的搜索结果,以及从各种质谱仪产生的原始文件转换而来的mzXML文件。为了进行准确的定量分析,Multi-Q确定用户仪器的检测限以滤除异常值,而MaXIC-Q对我们构建的投影离子质谱采用严格的验证,以确保用于定量的正确数据。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验