ProteomeXchange provides globally coordinated proteomics data submission and dissemination

Vizcaíno, Juan Antonio; Deutsch, Eric W.; Wang, Rui; Csordás, Attila; Reisinger, Florian; Ríos, Daniel; Dianes, José A.; Sun, Zhi-Jun; Farrah, Terry; Bandeira, Nuno; Binz, Pierre‐Alain; Xenarios, Ioannis; Eisenacher, Martin; Mayer, Gerhard; Gatto, Laurent; Campos, Alex; Chalkley, Robert J.; Kraus, Hans-Joachim; Albar, Juan Pablo; Martínez‐Bartolomé, Salvador; Apweiler, Rolf; Omenn, Gilbert S.; Martens, Lennart; Jones, Andrew R.; Hermjakob, Henning

doi:10.1038/nbt.2839

Cited by 2,548 publications

(2,210 citation statements)

References 12 publications

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“…Third, the provision of the mzqViewer should help both bioinformaticians and lab‐based scientists to visualise and understand their data. Lastly, the two tools used in combination should help groups to prepare quantitative data in PSI standard format (mzQuantML or mzTab) for submission to PRIDE/ProteomeXchange 29 in support of published studies.…”

Section: Resultsmentioning

confidence: 99%

“…The mzTab exporter embeds the jmzTab API 24, writing valid files that for example could be submitted to the ProteomeXchange public repository 29 or loaded into a spreadsheet or statistical software easily. To support end user visualisation of results and downstream analysis, we have also written exporters to a simple Comma Separated Values (CSV) file format (not‐standardised), equivalent version in XLS (Excel format) and HTML for viewing mzQuantML formatted results in web‐pages.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The mzqLibrary – An open source Java library supporting the HUPO‐PSI quantitative proteomics standard

Zhang

Fan

et al. 2015

Proteomics

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The mzQuantML standard has been developed by the Proteomics Standards Initiative for capturing, archiving and exchanging quantitative proteomic data, derived from mass spectrometry. It is a rich XML‐based format, capable of representing data about two‐dimensional features from LC‐MS data, and peptides, proteins or groups of proteins that have been quantified from multiple samples. In this article we report the development of an open source Java‐based library of routines for mzQuantML, called the mzqLibrary, and associated software for visualising data called the mzqViewer. The mzqLibrary contains routines for mapping (peptide) identifications on quantified features, inference of protein (group)‐level quantification values from peptide‐level values, normalisation and basic statistics for differential expression. These routines can be accessed via the command line, via a Java programming interface access or a basic graphical user interface. The mzqLibrary also contains several file format converters, including import converters (to mzQuantML) from OpenMS, Progenesis LC‐MS and MaxQuant, and exporters (from mzQuantML) to other standards or useful formats (mzTab, HTML, csv). The mzqViewer contains in‐built routines for viewing the tables of data (about features, peptides or proteins), and connects to the R statistical library for more advanced plotting options. The mzqLibrary and mzqViewer packages are available from https://code.google.com/p/mzq‐lib/.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The mzqLibrary – An open source Java library supporting the HUPO‐PSI quantitative proteomics standard

Zhang

Fan

et al. 2015

Proteomics

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“…For example, post‐translationally modified pMHC (e.g., phosphorylations,30 symmetric vs asymmetric demethylation,31 O‐GlcNAc vs O‐GalNAc32) may be directly identified by enabling variable modifications in the database searches. Therefore, all raw and processed data should be submitted to a public proteomic or immunopeptidomic repository (e.g., the resources included in the ProteomeXchange Consortium [http://www.proteomexchange.org/], such as PRIDE, MassIVE, jPOST, and iProX repositories,33 or the recently created SysteMHC Atlas immunopeptidomic data repository [https://systemhcatlas.org/]34) to enable better collaborations among researchers and data reuse by third parties, to advance the field more efficiently. As discussed above, immunopeptidomics experiments are carried out by applying different methods, using a variety of instrument types, and employing different computational analysis tools.…”

Section: Informatics and Pmhc Identificationmentioning

confidence: 99%

“…It is encouraged that data is submitted to one public repository, following the requirements established by the ProteomeXchange Consortium 33. Data submissions must then include at least: the metadata information described in Section 2.1.1, raw data (MS data coming from the mass spectrometer), and processed results, including the output of the analysis (ideally in an open standard format if possible, e.g., mzIdentML, mzTab).…”

Section: Informatics and Pmhc Identificationmentioning

confidence: 99%

Minimal Information About an Immuno‐Peptidomics Experiment (MIAIPE)

Lill¹,

Veelen

Admon

et al. 2018

Proteomics

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Minimal information about an immuno‐peptidomics experiment (MIAIPE) is an initiative of the members of the Human Immuno‐Peptidome Project (HIPP), an international program organized by the Human Proteome Organization (HUPO). The aim of the MIAIPE guidelines is to deliver technical guidelines representing the minimal information required to sufficiently support the evaluation and interpretation of immunopeptidomics experiments. The MIAIPE document has been designed to report essential information about sample preparation, mass spectrometric measurement, and associated mass spectrometry (MS)‐related bioinformatics aspects that are unique to immunopeptidomics and may not be covered by the general proteomics MIAPE (minimal information about a proteomics experiment) guidelines.

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“…This goal was obtained with the establishment of the ProteomeXchange consortium 28, which connects some of the most used proteomics databases (at present PRIDE, MassIVE, PASSEL, and PeptideAtlas) with a single submission system and the use of unique identifiers that can be tracked across these databases and over time.…”

Section: Introductionmentioning

confidence: 99%

Exploring the potential of public proteomics data

et al. 2015

Self Cite

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In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS‐based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re‐)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

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ProteomeXchange provides globally coordinated proteomics data submission and dissemination

Cited by 2,548 publications

References 12 publications

The mzqLibrary – An open source Java library supporting the HUPO‐PSI quantitative proteomics standard

The mzqLibrary – An open source Java library supporting the HUPO‐PSI quantitative proteomics standard

Minimal Information About an Immuno‐Peptidomics Experiment (MIAIPE)

Exploring the potential of public proteomics data

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