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Proteomics Sample Metadata Format

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Improving metadata annotation of Proteomics datasets

Metadata is essential in proteomics data repositories and is crucial to interpret and reanalyze the deposited data sets. While the dataset general description and standard data file formats are supported and captured for every dataset by ProteomeXchange partners, the information regarding the sample to data files is mostly missing. Recently, members of the European Bioinformatics Community for Mass Spectrometry (EuBIC - have created this open-source project to enable the standardization of sample metadata of public proteomics data sets.

The Proteomics Sample Metadata Project aims to standardize the way ProteomeXchange partners and the proteomics community capture the relation between the samples and the data generated within a PX submission. We have adapted the MAGE-TAB v1.1 format to capture necessary metadata for Proteomics experiments to allow automated re-processing. The MAGE-TAB (MicroArray Gene Expression Tabular) is the file format to store the metadata and sample information on transcriptomics experiments. By repurposing and extending the MAGE-TAB for Proteomics, we aim to provide a format for future submissions of multiomics experiments to ProteomeXchange partners and better integration with other omics data. The MAGE-TAB is divided in two main files: IDF (Investigation Description Format) and SDRF (Sample and Data Relationship Format). We will describe how these two files are adapted for Proteomics.

Our goal is to ensure maximum reusability of the deposited data. Our work aims to define the minimum information required to report the experimental design of proteomics experiments, enabling the use and reuse of the deposited data by the proteomics community. The following Use Cases should be considered to design the Proteomics Sample Metadata Format:

  • The SDRF for Proteomics should be fully compatible with MAGE-TAB version v1.1 that is used to represent transcriptomics data.
  • The IDF part of the MAGE-TAB should be compatible with the current proteomeXchange.xml file format.
  • The "Sample and Data Relationship Format for Proteomics (SDRF-Proteomics)" based on the SDRF part of MAGE-TAB should capture the Sample to Data relationships.
  • The resulting file format SHOULD enable data submitters and curators to annotate a proteomics dataset at different levels, including the sample metadata (e.g. organism and tissues), technical metadata (e.g. instrument model) and the experimental design.
  • The resulting file format SHOULD facilitate the automatic reanalysis of public proteomics datasets, by providing a better representation of quantitative datasets in public repositories.


ProteomeXchange resources developed a file format called submission.px which captures the same information as the MAGE-TAB IDF. We have developed a set of tools to automatically translate from submission.px to IDF.

SDRF (SDRF-Proteomics)

While the experiment general description is captured for all the PX submissions and experiments, the Sample to Data information is missing (or not standardized) for all PX datasets. The standardization of the SDRF (within MAGE-TAB) for proteomics is the main objective of this project (Read more about SDRF-Proteomics)

Final PSI specification

The final HUPO-PSI specification is: SDRF HUPO-PSI

How to contribute

External contributors, researchers and the proteomics community are more than welcome to contribute to this project.

Contribute with the specification: you can contribute to the specification with ideas or refinements by adding an issue into the issue tracker or performing a PR.

In the annotated projects folder users can see different public datasets that have been annotated so far by the contributors. If you would like to join these efforts, make a Fork of this repo and perform a pull request (PR) with your annotated project. If you don't have a project in mind, you can take one project from the issues and perform the annotation.

Annotate a dataset in 5 steps:

  • Read the SDRF-Proteomics specification.
  • Depending on the type of dataset, choose the appropriate sample template.
  • Annotate the corresponding ProteomeXchange PXD dataset following the guidelines.
  • Validate your SDRF file:

In order to validate your SDRF, you can install the sdrf-pipelines tool in Python

pip install sdrf-pipelines

validate the SDRF file

parse_sdrf validate-sdrf --sdrf_file sdrf.tsv

You can read more about the validator here.

  • Fork the current repository, add a folder with the ProteomeXchange accession and the annotated sdrf.tsv

30 Minutes Guide to MAGE-TAB for Proteomics

Documentation page (

We have created a 30 minutes Guide to the file format in the github repository. Additionally the following materials are relevant for new users:

Core contributors and collaborators

The project is run by different groups:

  • Yasset Perez-Riverol (PRIDE Team, European Bioinformatics Institute - EMBL-EBI, U.K.)
  • Timo Sachsenberg (OpenMS Team, Tübingen University, Germany)
  • Anja Fullgrabe (Expression Atlas Team, European Bioinformatics Institute - EMBL-EBI, U.K.)
  • Nancy George (Expression Atlas Team, European Bioinformatics Institute - EMBL-EBI, U.K.)
  • Mathias Walzer (PRIDE Team, European Bioinformatics Institute - EMBL-EBI, U.K.)
  • Pablo Moreno (Expression Atlas Team, European Bioinformatics Institute - EMBL-EBI, U.K.)
  • Juan Antonio Vizcaíno (PRIDE Team, European Bioinformatics Institute - EMBL-EBI, U.K.)
  • Oliver Alka (OpenMS Team, Tübingen University, Germany)
  • Julianus Pfeuffer (OpenMS Team, Tübingen University, Germany)
  • Marc Vaudel (University of Bergen, Norway)
  • Harald Barsnes (University of Bergen, Norway)
  • Niels Hulstaert (Compomics, University of Gent, Belgium)
  • Lennart Martens (Compomics, University of Gent, Belgium)
  • Expression Atlas Team (European Bioinformatics Institute - EMBL-EBI, U.K.)
  • Lev Levitsky (INEP team, INEPCP RAS, Moscow, Russia)
  • Elizaveta Solovyeva (INEP team, INEPCP RAS, Moscow, Russia)
  • Stefan Schulze (University of Pennsylvania, USA)
  • Veit Schwämmle (Protein Research Group, University of Southern Denmark, Denmark)
  • ProteomicsDB Team (Technical University of Munich, Germany)
  • David Bouyssié (ProFI/IPBS, University of Toulouse, CNRS, Toulouse, France)
  • Nicholas Carruthers (Wayne State University, USA)
  • Paul Rudnick (NCI, Proteomic Data Commons, USA)
  • Enrique Audain (University Medical Center Schleswig-Holstein, Germany)
  • Marie Locard-Paulet (Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark)
  • Johannes Griss (Department of Dermatology, Medical University of Vienna, Austria)
  • Chengxin Dai (Chongqing Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and telecommunications, Chongqing, China)
  • Julian Uszkoreit ( Medical Faculty, Medizinisches Proteom-Center and Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, Germany)
  • Dirk Winkelhardt ( Medical Faculty, Medizinisches Proteom-Center and Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, Germany)
  • Kanami Arima (Toyama University of International Studies, Toyama Japan)
  • Shin Kawano (Toyama University of International Studies, Toyama Japan)
  • Ruri Okamoto (Toyama University of International Studies, Toyama Japan)

IMPORTANT: If you contribute with the following specification, please make sure to add your name to the list of contributors.

Code of Conduct

As part of our efforts toward delivering open and inclusive science, we follow the Contributor Covenant Code of Conduct for Open Source Projects.

How to cite

  • Dai C, Füllgrabe A, Pfeuffer J, Solovyeva EM, Deng J, Moreno P, Kamatchinathan S, Kundu DJ, George N, Fexova S, Grüning B, Föll MC, Griss J, Vaudel M, Audain E, Locard-Paulet M, Turewicz M, Eisenacher M, Uszkoreit J, Van Den Bossche T, Schwämmle V, Webel H, Schulze S, Bouyssié D, Jayaram S, Duggineni VK, Samaras P, Wilhelm M, Choi M, Wang M, Kohlbacher O, Brazma A, Papatheodorou I, Bandeira N, Deutsch EW, Vizcaíno JA, Bai M, Sachsenberg T, Levitsky LI, Perez-Riverol Y. A proteomics sample metadata representation for multiomics integration and big data analysis. Nat Commun. 2021 Oct 6;12(1):5854. doi: 10.1038/s41467-021-26111-3. PMID: 34615866; PMCID: PMC8494749. Manuscript
  • Perez-Riverol, Yasset, European Bioinformatics Community for Mass Spectrometry. "Towards a sample metadata standard in public proteomics repositories." Journal of Proteome Research (2020) Manuscript.

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