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Computational Proteomics with Jupyter and Python


Malmström, Lars (2019). Computational Proteomics with Jupyter and Python. In: Evans, C; Wright, P; Noirel, J. Mass Spectrometry of Proteins. Switzerland: Springer, 237-248.

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.

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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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute for Computational Science
Dewey Decimal Classification:530 Physics
Scopus Subject Areas:Life Sciences > Molecular Biology
Life Sciences > Genetics
Language:English
Date:2019
Deposited On:04 Jun 2019 14:31
Last Modified:29 Jul 2020 10:47
Publisher:Springer
Series Name:Methods in Molecular Biology
Number:Vol. 1977
ISSN:1064-3745
ISBN:978-1-4939-9231-7
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/978-1-4939-9232-4_15
PubMed ID:30980332

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