Summary: Mass spectrometry coupled to liquid chromatography (LC-MS/MS) is a powerful technique for the charac-terisation of proteomes. However, the diverse software platforms available for processing the raw proteomics data, each produce their own output format, making the extraction of meaningful and interpretable results a difficult task. We present TraianProt, a web-based, user-friendly proteomics data analysis platform, that enables the analysis of both label-free and labeled data from Data-Dependent or Data-Independent Acquisition mass spectrometry mode support-ing different computational platforms such as MaxQuant, MSFragger, DIA-NN, ProteoScape and Proteome Discoverer output formats. TraianProt provides a dynamic framework that includes several processing modules allowing the user to perform a complete downstream analysis covering the stages of data pre-processing, differential expression analy-sis, functional analysis and protein-protein interaction analysis. Data output includes a wide range of high-quality, cus-tomisable graphs such as heatmap, volcano plot, boxplot and barplot. This allows users to extract biological insights from proteomic data without any programming skills. Availability and implementation: TraianProt is implemented in R. Its code and documentation are available on GitHub at https://github.com/SamueldelaCamaraFuentes/TraianProt along with a step-by-step tutorial incorporated in the repository. Contact: sdelacam@ucm.es Supplementary information: Supplementary data are available at Bioinformatics online

翻译：摘要： 总结：液相色谱-质谱联用技术是一种强大的蛋白质组表征方法。然而，用于处理原始蛋白质组学数据的各类软件平台均产生各自独特的输出格式，这使得提取有意义且可解释的结果成为一项困难任务。我们推出TraianProt，一个基于网络的用户友好型蛋白质组学数据分析平台，能够分析来自数据依赖采集或数据非依赖采集质谱模式的标记与非标记数据，并支持MaxQuant、MSFragger、DIA-NN、ProteoScape及Proteome Discoverer等多种计算平台的输出格式。TraianProvides提供了一个动态框架，包含多个处理模块，使用户能够执行涵盖数据预处理、差异表达分析、功能分析和蛋白质-蛋白质相互作用分析阶段的完整下游分析。数据输出包含多种高质量、可定制化的图表，如热图、火山图、箱线图和条形图。这使得用户无需编程技能即可从蛋白质组数据中提取生物学洞见。可用性与实现：TraianProt基于R语言实现，其代码与文档可在GitHub获取：https://github.com/SamueldelaCamaraFuentes/TraianProt，该仓库同时附有逐步操作教程。联系方式：sdelacam@ucm.es 补充信息：补充数据可在Bioinformatics在线获取。