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Computational Proteomics with Jupyter and Python

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Mass Spectrometry of Proteins

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1977))

Abstract

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.

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References

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Author information

Authors and Affiliations

  1. Institute for Computational Science, University of Zurich, Zurich, Switzerland

    Lars Malmström

  2. S3IT, University of Zurich, Zurich, Switzerland

    Lars Malmström

  3. Division of Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden

    Lars Malmström

Authors
  1. Lars Malmström
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Corresponding author

Correspondence to Lars Malmström .

Editor information

Editors and Affiliations

  1. Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, South Yorkshire, UK

    Caroline A. Evans

  2. Faculty of Science, Agriculture & Engineering, Newcastle University, Newcastle upon Tyne, UK

    Phillip C. Wright

  3. Laboratoire GBCM (EA4627), Conservatoire National des Arts et Métiers, HESAM Université, Paris, France

    Josselin Noirel

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Malmström, L. (2019). Computational Proteomics with Jupyter and Python. In: Evans, C., Wright, P., Noirel, J. (eds) Mass Spectrometry of Proteins. Methods in Molecular Biology, vol 1977. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9232-4_15

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  • DOI: https://doi.org/10.1007/978-1-4939-9232-4_15

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9231-7

  • Online ISBN: 978-1-4939-9232-4

  • eBook Packages: Springer Protocols

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