The Sample to Data file format (SDRF) for Proteomics project aims to define a set of guidelines to support the annotation of the sample metadata in in public proteomics experiments. Our goal with the Sample to Data file format (SDRF) for Proteomics 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 Experimental design data format:

• The "Sample to Data file format (SDRF) for Proteomics" complement the proteomeXchange.xml file format implemented by ProteomeXchange to capture the minimum metadata about a proteomics dataset. The ProteomeXchange submission XML file format is detailed here.

• It 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.

• It SHOULD facilitate the automatic reanalysis of public proteomics datasets, by providing a better representation of quantitative data in public repositories.

The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “MAY”, and “OPTIONAL” are to be interpreted as described in RFC-2119 (Bradner 1997).

The Sample to Data file format (SDRF) for Proteomics should be based on ontology or controlled vocabulary (CV) terms (e.g. UNIMOD-35). An ontology encompasses a representation, formal naming and definition of the categories, properties and relationships between the concepts, data and entities that substantiate one, many or all domains of discourse. All Ontologies used in the Proteomics Experimental Design format MUST be indexed in the Ontology Lockup Service. The current ontologies supported in the format are:

• PRIDE Controlled Vocabulary (CV)
• PSI Mass Spectrometry CV
• Experimental Factor Ontology
• Unimod protein modification database for mass spectrometry
• PSI-MOD CV
• Cell line ontology
• Drosophila anatomy ontology
• Cell ontology
• Plant ontology
• Uber-anatomy ontology
• Zebrafish anatomy and development ontology
• Zebrafish developmental stages ontology
• Plant Environment Ontology
• FlyBase Developmental Ontology
• Rat Strain Ontology
• Chemical Entitities of Biological Interest Ontology
• NCBI organismal classification

WARNING: If you you are contributing with the following guidelines and file format, and WOULD like to add another ontology; please modify the list with a Pull Request.

• Experimental Design

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 the user 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 specification

. Depending on the type of dataset, choose the appropriate sample template

. Annotate the the corresponding ProteomeXchange PXD dataset following the guidelines

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

pip install sdrf-pipelines

validate the SDRF

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

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)

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

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

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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