J Proteome Res. 2021 Apr 2;20(4):2014-2020. doi: 10.1021/acs.jproteome.0c00857. Epub 2021 Mar 4.
Visual examination of mass spectrometry data is necessary to assess data quality and to facilitate data exploration. Graphics provide the means to evaluate spectral properties, test alternative peptide/protein sequence matches, prepare annotated spectra for publication, and fine-tune parameters during wet lab procedures. Visual inspection of LC-MS data is constrained by proteomics visualization software designed for particular workflows or vendor-specific tools without open-source code. We built PSpecteR, an open-source and interactive R Shiny web application for visualization of LC-MS data, with support for several steps of proteomics data processing, including reading various mass spectrometry files, running open-source database search engines, labeling spectra with fragmentation patterns, testing post-translational modifications, plotting where identified fragments map to reference sequences, and visualizing algorithmic output and metadata. All figures, tables, and spectra are exportable within one easy-to-use graphical user interface. Our current software provides a flexible and modern R framework to support fast implementation of additional features. The open-source code is readily available (https://github.com/EMSL-Computing/PSpecteR), and a PSpecteR Docker container (https://hub.docker.com/r/emslcomputing/pspecter) is available for easy local installation.
对质谱数据进行目视检查是评估数据质量和促进数据探索的必要手段。图形提供了评估光谱特性、测试替代肽/蛋白质序列匹配、为发布准备带注释的光谱以及在湿实验室过程中微调参数的方法。LC-MS 数据的目视检查受到特定工作流程或特定于供应商的工具的蛋白质组学可视化软件的限制,这些工具没有开源代码。我们构建了 PSpecteR,这是一个用于 LC-MS 数据可视化的开源交互式 R Shiny 网络应用程序,支持蛋白质组学数据处理的几个步骤,包括读取各种质谱文件、运行开源数据库搜索引擎、用碎片模式标记光谱、测试翻译后修饰、绘制鉴定片段映射到参考序列的位置,并可视化算法输出和元数据。所有图形、表格和光谱都可以在一个易于使用的图形用户界面中导出。我们当前的软件提供了一个灵活的现代 R 框架,以支持快速实现其他功能。开源代码可随时获取(https://github.com/EMSL-Computing/PSpecteR),并且可以使用 PSpecteR Docker 容器(https://hub.docker.com/r/emslcomputing/pspecter)轻松进行本地安装。