ProteoSign v2: a faster and evolved user-friendly online tool for statistical analyses of differential proteomics

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.

Proteomes Are of Proteoforms: Embracing the Complexity

“…Until this is done, we will be unable to advance our methods or thus deep and routine proteomic analyses to the extent that is both necessary and sufficient for unbiased identification of genuine highly selective biomarkers and drug targets. That said, there are indeed critical ongoing efforts to improve both sample and data analyses, and the best of these must be integrated into a continuously developing unified approach to proteoform and thus proteome analysis [29,81,88,[257][258][259][260][261][262][263][264][265][266][267][268][269][270][271][272][273][274]. We are hopeful and confident of a more collaborative and unbiased future for the discipline of proteomics.…”

Differential Proteome and Interactome Analysis Reveal the Basis of Pleiotropy Associated With the Histidine Methyltransferase Hpm1p

“…An additional analysis for lysine acetylation was performed as previously mentioned one but with the addition of lysine acetylation as a variable modification. For differential abundance analysis, the resulting evidence.txt and proteingroups.txt files were analyzed using the aggregation, normalization, and differential expression analysis tools in ProteoSign, v2 ( 42 ). Default parameters were used, which only consider proteins quantified in both biological replicates and with at least two different peptides.…”

Kidney failure alters parathyroid Pin1 phosphorylation and parathyroid hormone mRNA binding proteins leading to secondary hyperparathyroidism

“…The rat parathyroid MS proteomics and phosphoproteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD029368 and PXD029401, respectively (64). For the primary statistical analysis of the MaxQuant proteomic data we uploaded proteinGroups.txt and evidence.txt files to the ProteoSign webserver (65). Supplemental Table S1 and Fig 1 present ProteoSign’s differential protein expression results and Supplemental Fig 1 shows ProteoSign’s enrichment results, processed with g:Profiler (66) and plotted using ggplot2 (https://ggplot2.tidyverse.org) for R. MaxQuant-identified phosphoproteins , with the detectable proteome as background, subjected to enrichment analysis using DAVID (67), and the results displayed with ggplot2.…”