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MSnbase, efficient and elegant R-based processing and visualisation of raw mass spectrometry data

View ORCID ProfileLaurent Gatto, View ORCID ProfileSebastian Gibb, View ORCID ProfileJohannes Rainer
doi: https://doi.org/10.1101/2020.04.29.067868
Laurent Gatto
†Computational Biology Unit, de Duve Institute, Université catholique de Louvain, Brussels, Belgium
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  • For correspondence: laurent.gatto@uclouvain.be
Sebastian Gibb
‡Department of Anaesthesiology and Intensive Care of the University Medicine Greifswald, Germany
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Johannes Rainer
¶Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
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Abstract

We present version 2 of the MSnbase R/Bioconductor package. MSnbase provides infrastructure for the manipulation, processing and visualisation of mass spectrometry data. We focus on the new on-disk infrastructure, that allows the handling of large raw mass spectrometry experiments on commodity hardware and illustrate how the package is used for elegant data processing, method development, and visualisation.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Explain usage for in-memory and on-disk backends (fix typo fixed).

  • https://github.com/lgatto/2020-msnbase-v2

  • 1 https://github.com/jorainer/metabolomics2018

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 29, 2020.
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MSnbase, efficient and elegant R-based processing and visualisation of raw mass spectrometry data
Laurent Gatto, Sebastian Gibb, Johannes Rainer
bioRxiv 2020.04.29.067868; doi: https://doi.org/10.1101/2020.04.29.067868
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MSnbase, efficient and elegant R-based processing and visualisation of raw mass spectrometry data
Laurent Gatto, Sebastian Gibb, Johannes Rainer
bioRxiv 2020.04.29.067868; doi: https://doi.org/10.1101/2020.04.29.067868

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