转贴 蛋白组学的数据分析

在蛋白质组学研究中,如果使用高通量方法会得到大量蛋白质数据, 这就需要采用生物信息学的方法进行处理. 这里介绍一篇文章,希望能起到抛砖引玉的作用, 让大家讨论一下还可以用那些方法进行生物信息学处理.
在这篇论文中， 应用了合并2种检索， 非标记定量， 相对量比较（normalized and non normalized），GO term 比较， 3种算法的蛋白定位预测比较， 通路分析，蛋白修饰(包括氨基酸修饰，和蛋白降解修饰)。另外在结果表格中还列出信号肽， 跨膜区，以及是否血清蛋白分析。文章连接：
Characterization of the Vitreous Proteome in Diabetes without Diabetic Retinopathy and Diabetes with Proliferative Diabetic Retinopathy
J. Proteome Res., ASAP Article, 10.1021/pr800112g
http://pubs.acs.org/cgi-bin/abstract.cgi/jprobs/asap/abs/pr800112g.html
因为版权所以不能贴在这里，无法下载的可以发消息给我。

Figure 1. Proteomic analysis process and the number of proteins identified. A. Schematic of gel-LC-MS/MS analysis and data processing. B. Venn diagram of proteins identified using X!Tandem and SEQUEST algorithms. The number of proteins identified from 17 independent vitreous samples and percent of total number of proteins identified by each algorithm are shown.

This table contains the total list of IPI, protein name, gene symbol, sequence coverage, unique peptides, spectral count of NDM, noDR, and PDR group (Mean ± SEM), p value, search engine, GO term (biological process, cellular component, molecular function), and protein subcellular localization prediction for the 252 proteins identified in this study.