Johannes Griss

The Proteomics Identifications (PRIDE) database and associated tools: status in 2013

The PRoteomics IDEntifications (PRIDE, http://www.ebi.ac.uk/pride) database at the European Bioinformatics Institute is one of the most prominent data repositories of mass spectrometry (MS)-based proteomics data. Here, we summarize recent developments in the PRIDE database and related tools. First, we provide up-to-date statistics in data content, splitting the figures by groups of organisms and species, including peptide and protein identifications, and post-translational modifications. We then describe the tools that are part of the PRIDE submission pipeline, especially the recently developed PRIDE Converter 2 (new submission tool) and PRIDE Inspector (visualization and analysis tool). We also give an update about the integration of PRIDE with other MS proteomics resources in the context of the ProteomeXchange consortium. Finally, we briefly review the quality control efforts that are ongoing at present and outline our future plans.

2016 update of the PRIDE database and its related tools

The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data. Since the beginning of 2014, PRIDE Archive (http://www.ebi.ac.uk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database. Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013. PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components. PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium. The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month). We outline some statistics on the current PRIDE Archive data contents. We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool. Finally, we will give a brief update on the resources under development ‘PRIDE Cluster’ and ‘PRIDE Proteomes’, which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive.

The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results

We report the release of mzIdentML, an exchange standard for peptide and protein identification data, designed by the Proteomics Standards Initiative. The format was developed by the Proteomics Standards Initiative in collaboration with instrument and software vendors, and the developers of the major open-source projects in proteomics. Software implementations have been developed to enable conversion from most popular proprietary and open-source formats, and mzIdentML will soon be supported by the major public repositories. These developments enable proteomics scientists to start working with the standard for exchanging and publishing data sets in support of publications and they provide a stable platform for bioinformatics groups and commercial software vendors to work with a single file format for identification data.

PRIDE Inspector: a tool to visualize and validate MS proteomics data

This work was supported by the Wellcome Trust (grant number WT085949MA) and EMBL core funding. R.G.C. is supported by EU FP7 grant SLING (grant number 226073). J.A.V. is supported by the EU FP7 grants LipidomicNet (grant number 202272) and ProteomeXchange (grant number 260558). A.F. was partially supported by the Spanish network COMBIOMED (RD07/0067/0006, ISCIII-FIS). L.M. would like to acknowledge support from the EU FP7 PRIME-XS grant (grant number 262067).

The PRoteomics IDEntification (PRIDE) Converter 2 Framework: An Improved Suite of Tools to Facilitate Data Submission to the PRIDE Database and the ProteomeXchange Consortium

The original PRIDE Converter tool greatly simplified the process of submitting mass spectrometry (MS)-based proteomics data to the PRIDE database. However, after much user feedback, it was noted that the tool had some limitations and could not handle several user requirements that were now becoming commonplace. This prompted us to design and implement a whole new suite of tools that would build on the successes of the original PRIDE Converter and allow users to generate submission-ready, well-annotated PRIDE XML files. The PRIDE Converter 2 tool suite allows users to convert search result files into PRIDE XML (the format needed for performing submissions to the PRIDE database), generate mzTab skeleton files that can be used as a basis to submit quantitative and gel-based MS data, and post-process PRIDE XML files by filtering out contaminants and empty spectra, or by merging several PRIDE XML files together. All the tools have both a graphical user interface that provides a dialog-based, user-friendly way to convert and prepare files for submission, as well as a command-line interface that can be used to integrate the tools into existing or novel pipelines, for batch processing and power users. The PRIDE Converter 2 tool suite will thus become a cornerstone in the submission process to PRIDE and, by extension, to the ProteomeXchange consortium of MS-proteomics data repositories.

The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience

The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R. We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found online.

Cytoplasmic proteome and secretome profiles of differently stimulated human dendritic cells.

Dendritic cells (DCs), the most potent and specialized antigen-presenting cells, play a key role in the regulation of the adaptive immunity. Immature DCs were generated by in vitro culturing of peripheral blood monocytes and functionally activated with the classical pathogen-associated molecular pattern lipopolysaccharide (LPS). Alternative activation resulting in Th-2 polarization was induced with lipid oxidation products derived from 1-palmitoyl-2-arachidoyl-sn-glycerol-3-phosphorylcholin (OxPAPC). Tolerogenic cells were obtained by treating DCs with human rhinovirus (HRV). The aim of this study was the identification of proteome profiles related to the functionally different dendritic cell phenotypes. Cytoplasmic proteins were analyzed by shotgun proteomics resulting in the identification of 1690 proteins. While mature and alternatively activated DCs displayed highly distinct protein expression profiles, HRV-treated DCs showed minor proteome alterations. As DCs exert many specific functions via secretion, we investigated the secretomes by a combination of 2D-PAGE and shotgun proteomics. We successfully identified a broad variety of cytokines (e.g., GM-CSF, TNF-alpha, interleukin-1beta, 6, 12 beta, 28B and 29), chemokines (e.g., CCL3, 5, 8, 17, 18, 19, 24, CXCL1, 2, 9 and 10) and growth factors (growth/differentiation factor 8, C-type lectin domain family 11 member A). The relative composition of secretome profiles, although comprising much less proteins, was found to be much more affected by functional alteration of cells than the cytoplasmic protein composition. In conclusion, we demonstrate that functional distinct subsets of DCs display distinct proteome profiles which comprise biomarker candidates. These proteins may prove useful for the interpretation of complex clinical proteomics data.

Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets

Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average, 75% of spectra analyzed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large scale to shed light on these unidentified spectra. The Proteomics Identifications (PRIDE) Database Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in the PRIDE Archive, coming from hundreds of data sets, we were able to consistently characterize spectra into three distinct groups: (1) incorrectly identified, (2) correctly identified but below the set scoring threshold, and (3) truly unidentified. Using multiple complementary analysis approaches, we were able to identify ∼20% of the consistently unidentified spectra. The complete spectrum-clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.

Proteome signatures of inflammatory activated primary human peripheral blood mononuclear cells

Proteome profiling is the method of choice to identify marker proteins whose expression may be characteristic for certain diseases. The formation of such marker proteins results from disease-related pathophysiologic processes. In healthy individuals, peripheral blood mononuclear cells (PBMCs) circulate in a quiescent cell state monitoring potential immune-relevant events, but have the competence to respond quickly and efficiently in an inflammatory manner to any invasion of potential pathogens. Activation of these cells is most plausibly accompanied by characteristic proteome alterations. Therefore we investigated untreated and inflammatory activated primary human PBMCs by proteome profiling using a ‘top down’ 2D-PAGE approach in addition to a ‘bottom up’ LC–MS/MS-based shotgun approach. Furthermore, we purified primary human T-cells and monocytes and activated them separately. Comparative analysis allowed us to characterize a robust proteome signature including NAMPT and PAI2 which indicates the activation of PBMCs. The T-cell specific inflammation signature included IRF-4, GBP1and the previously uncharacterized translation product of GBP5; the corresponding monocyte signature included PDCD5, IL1RN and IL1B. The involvement of inflammatory activated PBMCs in certain diseases as well as the responsiveness of these cells to anti-inflammatory drugs may be evaluated by quantification of these marker proteins. This article is part of a Special Issue entitled: Integrated omics.

Introducing a new parameter for quality control of proteome profiles : Consideration of commonly expressed proteins

Interpretation of proteome profiling experiments largely relies on comparative analyses. False‐positive identifications may cause fatal misinterpretation of data. On the other hand, proteome analysis may also suffer from false negatives, when proteins that are actually present are not detected. This circumstance may be as fatal as false‐positive identifications and was hardly considered until now. Appropriate positive controls would facilitate quality assessment of proteome profiling experiments. Based on cell biology knowledge, our aim was to generate a list of commonly expressed proteins, which may serve as positive control. Following a pragmatic experimental strategy, we compared the cytoplasmic fractions of four largely differing kinds of cells, which were human DCs, endothelial cells, fibroblasts and keratinocytes. Proteome profiling was performed by 2D‐PAGE in addition to shotgun analysis. By shotgun analysis, 665 proteins were identified, which occurred in each of the four cells types; 360 proteins of those were also detectable in the corresponding 2‐D gels. We consider these proteins as common proteins. All shotgun analysis data, including mass fragmentation spectra of the corresponding peptides, are accessible via the proteomics identification database (http://www.ebi.ac.uk/pride). As expected, most of the common proteins could be clearly assigned to at least one of the following functional categories: chaperones, cytoskeleton, energy metabolism, redox regulation, nucleic acid processing, protein turnover, membrane transport, protein synthesis and signaling. We suggest that the present data may prove helpful for data assessment, quality control and interpretation of a large variety of experiments based on proteome profiling.