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The ProteoRed multicentric experiment was designed to test each laboratory abilities to perform quantitative proteomic analysis, compare methodologies and inter-lab reproducibility for relative quantitative analysis of proteomes and to evaluate data reporting and data sharing tools (MIAPE documents, standard formats, public repositories). The experiment consist in analyzing two different samples (A and B), which contain an identical matrix of E. Coli proteins plus four standard proteins (CYC_HORSE, MYG_HORSE, ALDOA_RABIT, BSA_BOVIN), spiked in different amounts. The samples are designed primarily to be analyzed by LC-MS, although DIGE analysis could be also possible. Each lab will have the choice to test their preferred method for quantitative comparison of the two samples. However, to set as much standardization and reproducibility as possible in terms of data analysis, data sharing, protocols information, results and reporting we propose the OmicsHub Proteomics server to be the single platform to integrate the protein identification steps of the MS multicentric experiment and serve as a web repository. After the “In-lab” analysis is performed, with the laboratory's own tools, every lab is able to load its experiment (protocols, parameters, instruments, etc.) and import its spectrum data via web into the OmicsHub Proteomics analysis and managment server. Every experiment in OmicsHub is automatically stored following the PRIDE standard format. The OmicsHub Proteomics software tool performs the workflow tasks of Protein identification (using the search engines Mascot and Phenyx), Protein annotation, Protein grouping, FDR filtering (allowing the use of a local decoy database, designed ad hoc for this experiment) and Reporting of the protein identification results in a systematic and centralized manner. The OmicsHub Proteomics allows the researchers at ProteoRed consortium to perform its multicentric study with full reproducibility, standardization and experiment comparison; reducing time and data management complexity prior to the final quantification analysis.