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Computational Proteomics with Jupyter and Python

  • Lars MalmströmEmail author
Protocol
Part of the Methods in Molecular Biology book series (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.

Key words

Proteomics Python Jupyter JupyterHub Reproducible research 

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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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