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From Peptidome to PRIDE: public proteomics data migration at a large scale.

ABSTRACT: The PRIDE database, developed and maintained at the European Bioinformatics Institute (EBI), is one of the most prominent data repositories dedicated to high throughput MS-based proteomics data. Peptidome, developed by the National Center for Biotechnology Information (NCBI) as a sibling resource to PRIDE, was discontinued due to funding constraints in April 2011. A joint effort between the two teams was started soon after the Peptidome closure to ensure that data were not "lost" to the wider proteomics community by exporting it to PRIDE. As a result, data in the low terabyte range have been migrated from Peptidome to PRIDE and made publicly available under experiment accessions 17 900-18 271, representing 54 projects, ~53 million mass spectra, ~10 million peptide identifications, ~650,000 protein identifications, ~1.1 million biologically relevant protein modifications, and 28 species, from more than 30 different labs.

SUBMITTER: Csordas A

PROVIDER: S-EPMC3717177 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

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From Peptidome to PRIDE: public proteomics data migration at a large scale.

Csordas Attila A Wang Rui R Ríos Daniel D Reisinger Florian F Foster Joseph M JM Slotta Douglas J DJ Vizcaíno Juan Antonio JA Hermjakob Henning H

Proteomics 20130420 10-11

The PRIDE database, developed and maintained at the European Bioinformatics Institute (EBI), is one of the most prominent data repositories dedicated to high throughput MS-based proteomics data. Peptidome, developed by the National Center for Biotechnology Information (NCBI) as a sibling resource to PRIDE, was discontinued due to funding constraints in April 2011. A joint effort between the two teams was started soon after the Peptidome closure to ensure that data were not "lost" to the wider pr ...[more]

PMID: 23533138

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Project description:BACKGROUND: Phosphorylation is the most prevalent post-translational modification on eukaryotic proteins. Multisite phosphorylation enables a specific combination of phosphosites to determine the speed, specificity and duration of biological response. Until recent years, the lack of high quality data limited the possibility for analyzing the properties of phosphorylation at the proteome scale and in the context of a wide range of conditions. Thanks to advances of mass spectrometry technologies, thousands of phosphosites from in-vivo experiments were identified and archived in the public domain. Such resource is appropriate to derive an unbiased view on the phosphosites properties in eukaryotes and on their functional relevance. RESULTS: We present statistically rigorous tests on the spatial and functional properties of a collection of approximately 70,000 reported phosphosites. We show that the distribution of phosphosites positioning along the protein tends to occur as dense clusters of Serine/Threonines (pS/pT) and between Serine/Threonines and Tyrosines, but generally not as much between Tyrosines (pY) only. This phenomenon is more ubiquitous than anticipated and is pertinent for most eukaryotic proteins: for proteins with > or = 2 phosphosites, 54% of all pS/pT sites are within 4 amino acids of another site. We found a strong tendency for clustered pS/pT to be activated by the same kinase. Large-scale analyses of phosphopeptides are thus consistent with a cooperative function within the cluster. CONCLUSIONS: We present evidence supporting the notion that clusters of pS/pT but generally not pY should be considered as the elementary building blocks in phosphorylation regulation. Indeed, closely positioned sites tend to be activated by the same kinase, a signal that overrides the tendency of a protein to be activated by a single or only few kinases. Within these clusters, coordination and positional dependency is evident. We postulate that cellular regulation takes advantage of such design. Specifically, phosphosite clusters may increase the robustness of the effectiveness of phosphorylation-dependent response. REVIEWERS: Reviewed by Joel Bader, Frank Eisenhaber, Emmanuel Levy (nominated by Sarah Teichmann). For the full reviews, please go to the Reviewers' comments section.

Project description:MotivationWith the advancement of high-throughput techniques, large-scale profiling of biological systems with multiple experimental perturbations is becoming more prevalent. Pathway analysis incorporates prior biological knowledge to analyze genes/proteins in groups in a biological context. However, the hypotheses under investigation are often confined to a 1D space (i.e. up, down, either or mixed regulation). Here, we develop direction pathway analysis (DPA), which can be applied to test hypothesis in a high-dimensional space for identifying pathways that display distinct responses across multiple perturbations.ResultsOur DPA approach allows for the identification of pathways that display distinct responses across multiple perturbations. To demonstrate the utility and effectiveness, we evaluated DPA under various simulated scenarios and applied it to study insulin action in adipocytes. A major action of insulin in adipocytes is to regulate the movement of proteins from the interior to the cell surface membrane. Quantitative mass spectrometry-based proteomics was used to study this process on a large-scale. The combined dataset comprises four separate treatments. By applying DPA, we identified that several insulin responsive pathways in the plasma membrane trafficking are only partially dependent on the insulin-regulated kinase Akt. We subsequently validated our findings through targeted analysis of key proteins from these pathways using immunoblotting and live cell microscopy. Our results demonstrate that DPA can be applied to dissect pathway networks testing diverse hypotheses and integrating multiple experimental perturbations.Availability and implementationThe R package 'directPA' is distributed from CRAN under GNU General Public License (GPL)-3 and can be downloaded from: http://cran.r-project.org/web/packages/directPA/index.htmlContactjean.yang@sydney.edu.auSupplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

From Peptidome to PRIDE: public proteomics data migration at a large scale.

Publications

From Peptidome to PRIDE: public proteomics data migration at a large scale.

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