Carvalho Paulo C, Fischer Juliana S G, Xu Tao, Yates John R, Barbosa Valmir C
Carlos Chagas Institute-Fiocruz, Paraná, Brazil.
Department of Cell Biology, The Scripps Research Institute, La Jolla, California.
Curr Protoc Bioinformatics. 2012 Dec;Chapter 13:13.19.1-13.19.18. doi: 10.1002/0471250953.bi1319s40.
PatternLab for proteomics is a self-contained computational environment for analyzing shotgun proteomic data. Recent improvements incorporate modules to facilitate the computational analysis, such as FastaDBXtractor for sequence database preparation and ProLuCID runner for simplifying and managing the protein identification search engine; modules for pushing the limits on proteomics standards, such as SEPro, which relies on a semi-labeled decoy approach for increasing confidence in filtering and organizing peptide spectrum matches; and modules with novel features, such as SEProQ for enabling label-free quantitation by extracted ion chromatograms according to a distributed normalized ion abundance factor approach (dNIAF). Existing modules were also improved, such as the TFold module for pinpointing differentially expressed proteins. These new modules are integrated into the previously described arsenal of tools for further data analysis. Here we provide detailed instructions for operating and understanding them.
蛋白质组学模式实验室是一个用于分析鸟枪法蛋白质组学数据的独立计算环境。最近的改进包括有助于计算分析的模块，例如用于序列数据库准备的FastaDBXtractor和用于简化和管理蛋白质鉴定搜索引擎的ProLuCID运行器；用于突破蛋白质组学标准限制的模块，例如SEPro，它依赖半标记诱饵方法来提高过滤和组织肽谱匹配的可信度；以及具有新功能的模块，例如SEProQ，它能够根据分布式归一化离子丰度因子方法（dNIAF）通过提取离子色谱图进行无标记定量。现有模块也得到了改进，例如用于精确识别差异表达蛋白质的TFold模块。这些新模块被集成到先前描述的工具库中以进行进一步的数据分析。在这里，我们提供了操作和理解它们的详细说明。
