Computational Proteomics with Jupyter and Python

  • Lars MalmströmEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1977)


Proteomics based on mass spectrometry produces complex data in large quantities. The need for flexible computational pipelines, in the context of big data, in proteomics and other areas of science, has prompted the development of computational platforms and libraries that facilitate data analysis and data processing. In this respect, Python appears to be one of the winners among programming languages in terms of popularity and development. This chapter shows how to perform basic tasks using Python and dedicated libraries in a Jupyter framework: from basic search result summarizations to the creation of MS1 chromatograms.

Key words

Proteomics Python Jupyter JupyterHub Reproducible research 


  1. 1.
    Malmström E, Kilsgård O, Hauri S et al (2016) Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics. Nat Commun 7:10261CrossRefGoogle Scholar
  2. 2.
    Kremer LPM, Leufken J, Oyunchimeg P et al (2016) Ursgal, universal Python module combining common bottom-up proteomics tools for large-scale analysis. J Proteome Res 15:788–794CrossRefGoogle Scholar
  3. 3.
    Mi H, Huang X, Muruganujan A et al (2017) PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res 45:D183–D189CrossRefGoogle Scholar
  4. 4.
    Craig R, Beavis RC (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20:1466–1467CrossRefGoogle Scholar
  5. 5.
    Röst HL, Rosenberger G, Navarro P et al (2014) OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol 32:219–223CrossRefGoogle Scholar
  6. 6.
    Röst HL, Schmitt U, Aebersold R, Malmstrom L (2014) pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics 14:74–77CrossRefGoogle Scholar
  7. 7.
    Röst HL, Sachsenberg T, Aiche S et al (2016) OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 13:741–748CrossRefGoogle Scholar
  8. 8.
    Teleman J, Dowsey AW, Gonzalez-Galarza FF et al (2014) Numerical compression schemes for proteomics mass spectrometry data. Mol Cell Proteomics 13:1537–1542CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Computational ScienceUniversity of ZurichZurichSwitzerland
  2. 2.S3ITUniversity of ZurichZurichSwitzerland
  3. 3.Division of Infection Medicine, Department of Clinical SciencesLund UniversityLundSweden

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