ProteoSign v2: a faster and evolved user-friendly online tool for statistical analyses of differential proteomics. Issue Volume 49:Issue W1(2021) (8th May 2021)

• Record Type: Journal Article
• Authors: Theodorakis, Evangelos; Antonakis, Andreas N; Baltsavia, Ismini; Pavlopoulos, Georgios A; Samiotaki, Martina; Amoutzias, Grigoris D; Theodosiou, Theodosios; Acuto, Oreste; Efstathiou, Georgios; Iliopoulos, Ioannis
• Is Part Of: Nucleic acids research. Volume 49:Issue W1(2021)
• Page Start: W573
• Page End: W577
• Publication Date: 2021-05-08
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkab329
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: 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 . Graphical Abstract: