Kohler Devon, Kaza Maanasa, Pasi Cristina, Huang Ting, Staniak Mateusz, Mohandas Dhaval, Sabido Eduard, Choi Meena, Vitek Olga
Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States.
Universitat Oberta de Catalunya, Barcelona 08018, Spain.
J Proteome Res. 2023 Feb 3;22(2):551-556. doi: 10.1021/acs.jproteome.2c00603. Epub 2023 Jan 9.
Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages , are implemented in a coding language such as R. To make the methods in accessible to users with limited programming and statistical background, we have created , an R-Shiny graphical user interface (GUI) integrated with , , and . The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).
基于液相色谱与自下而上质谱法(LC-MS/MS)的蛋白质组学是一种用于识别和定量复杂生物混合物中蛋白质的通用技术。在鉴定之后,分析不同条件下蛋白质丰度的变化需要越来越复杂和专门的统计方法。这些方法中的许多,特别是开源的生物导体软件包家族,是用诸如R之类的编程语言实现的。为了使编程和统计背景有限的用户能够使用这些方法,我们创建了 ,一个与 、 和 集成的R-Shiny图形用户界面(GUI)。该GUI提供了一个适用于多种蛋白质组学实验类型的点击式分析管道,包括无标记数据依赖采集(DDA)或数据独立采集(DIA),或基于串联质量标签(TMT)的TMT-DDA,回答诸如肽、蛋白质或翻译后修饰(PTM)丰度的相对变化等问题。为了支持可重复研究,该应用程序会保存用户的选择并生成一个以编程方式重新创建分析的R脚本。 可以通过Github和生物导体在本地安装,也可以在www.msstatsshiny.com上在云端使用。我们使用两个实验数据集(MassIVE ID MSV000086623和MSV000085565)来说明该平台的实用性。