ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics

Journal of Proteome Research, vol. 22, pp. 632–636

Abstract

Data set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC) coupled to mass spectrometry (MS) data sets, due to the high levels of data reduction that occur between raw data and machine learning-ready data. Since predictive proteomics is an emerging field, when predicting peptide behavior in LC-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based data sets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides data sets that are useful for comparing state-of-the-art machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available at https://www.proteomicsml.org/, and we welcome the entire proteomics community to contribute to the project at https://github.com/ProteomicsML/ProteomicsML.

Authors 10

1. Tobias Greisager Rehfeldt

   University of Southern Denmark

   Affiliation as printed

   Institute for Mathematics and Computer Science, University of Southern Denmark, 5000 Odense, Denmark

2. Ralf Gabriels

   Ghent University · VIB-UGent Center for Medical Biotechnology

   Affiliation as printed

   Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium

   VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium

3. Robbin Bouwmeester

   Ghent University · VIB-UGent Center for Medical Biotechnology

   Affiliation as printed

   Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium

   VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium

4. Siegfried Gessulat
   Affiliation as printed

   MSAID Gmbh, Berlin 10559, Germany

5. Benjamin A. Neely

   National Institute of Standards and Technology

   Affiliation as printed

   National Institute of Standards and Technology, Charleston, South Carolina 29412, United States

6. Magnus Palmblad Aachen Center for Proteomics and Metabolomics

   Leiden University Medical Center

   Affiliation as printed

   Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands

7. Yasset Pérez‐Riverol

   European Bioinformatics Institute · Wellcome Trust

   Affiliation as printed

   European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom

   European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom

8. Tobias Schmidt
   Affiliation as printed

   MSAID GmbH, Garching b. Munich 85748, Germany

9. Juan Antonio Vizcaíno corresponding

   European Bioinformatics Institute · Wellcome Trust

   Affiliation as printed

   European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom

   European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom

10. Eric W. Deutsch corresponding

    Institute for Systems Biology

    Affiliation as printed

    Institute for Systems Biology, Seattle, Washington 98109, United States