Division of Basic Sciences, University of Crete Medical School, Heraklion 71110, Greece.
Department of Informatics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany.
Nucleic Acids Res. 2021 Jul 2;49(W1):W573-W577. doi: 10.1093/nar/gkab329.
Bottom-up proteomics analyses have been proved over the last years to be a powerful tool in the characterization of the proteome and are crucial for understanding cellular and organism behaviour. Through differential proteomic analysis researchers can shed light on groups of proteins or individual proteins that play key roles in certain, normal or pathological conditions. However, several tools for the analysis of such complex datasets are powerful, but hard-to-use with steep learning curves. In addition, some other tools are easy to use, but are weak in terms of analytical power. Previously, we have introduced ProteoSign, a powerful, yet user-friendly open-source online platform for protein differential expression/abundance analysis designed with the end-proteomics user in mind. Part of Proteosign's power stems from the utilization of the well-established Linear Models For Microarray Data (LIMMA) methodology. Here, we present a substantial upgrade of this computational resource, called ProteoSign v2, where we introduce major improvements, also based on user feedback. The new version offers more plot options, supports additional experimental designs, analyzes updated input datasets and performs a gene enrichment analysis of the differentially expressed proteins. We also introduce the deployment of the Docker technology and significantly increase the speed of a full analysis. ProteoSign v2 is available at http://bioinformatics.med.uoc.gr/ProteoSign.
近年来,自下而上的蛋白质组学分析已被证明是一种强大的蛋白质组学特征描述工具,对于理解细胞和生物行为至关重要。通过差异蛋白质组学分析,研究人员可以揭示在某些正常或病理条件下发挥关键作用的蛋白质组或个别蛋白质。然而,有几个用于分析此类复杂数据集的工具功能强大,但学习曲线陡峭,难以使用。此外,一些其他工具易于使用,但在分析能力方面较弱。此前,我们介绍了 ProteoSign,这是一个功能强大但用户友好的开源在线平台,用于设计具有端蛋白质组学用户思维的蛋白质差异表达/丰度分析。ProteoSign 的部分功能源自广泛使用的 Linear Models For Microarray Data (LIMMA) 方法。在这里,我们展示了这个计算资源的一个重大升级,称为 ProteoSign v2,其中我们根据用户反馈引入了主要改进。新版本提供了更多的绘图选项,支持更多的实验设计,分析更新的输入数据集,并对差异表达蛋白进行基因富集分析。我们还引入了 Docker 技术的部署,并大大提高了全面分析的速度。ProteoSign v2 可在 http://bioinformatics.med.uoc.gr/ProteoSign 上获得。