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  OpenMS: A platform for reproducible analysis of mass spectrometry data

Pfeuffer, J., Sachsenberg, T., Alka, O., Walzer, M., Fillbrunn, A., Nilse, L., et al. (2017). OpenMS: A platform for reproducible analysis of mass spectrometry data. Journal of Biotechnology, 261, 142-148. doi:10.1016/j.jbiotec.2017.05.016.

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Pfeuffer, J, Autor
Sachsenberg, T, Autor           
Alka, O, Autor
Walzer, M, Autor
Fillbrunn, A, Autor
Nilse, L, Autor
Schilling, O, Autor
Reinert, K, Autor
Kohlbacher, O1, Autor                 
Affiliations:
1Research Group Biomolecular Interactions, Max Planck Institute for Developmental Biology, Max Planck Society, ou_3380092              

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 Zusammenfassung: Background: In recent years, several mass spectrometry-based omics technologies emerged to investigate qualitative and quantitative changes within thousands of biologically active components such as proteins, lipids and metabolites. The research enabled through these methods potentially contributes to the diagnosis and pathophysiology of human diseases as well as to the clarification of structures and interactions between biomolecules. Simultaneously, technological advances in the field of mass spectrometry leading to an ever increasing amount of data, demand high standards in efficiency, accuracy and reproducibility of potential analysis software.
Results: This article presents the current state and ongoing developments in OpenMS, a versatile open-source framework aimed at enabling reproducible analyses of high-throughput mass spectrometry data. It provides implementations of frequently occurring processing operations on MS data through a clean application programming interface in C++ and Python. A collection of 185 tools and ready-made workflows for typical MS-based experiments enable convenient analyses for non-developers and facilitate reproducible research without losing flexibility.
Conclusions: OpenMS will continue to increase its ease of use for developers as well as users with improved continuous integration/deployment strategies, regular trainings with updated training materials and multiple sources of support. The active developer community ensures the incorporation of new features to support state of the art research.

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 Datum: 2017-11
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1016/j.jbiotec.2017.05.016
PMID: 28559010
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Titel: Journal of Biotechnology
Genre der Quelle: Zeitschrift
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Affiliations:
Ort, Verlag, Ausgabe: Elsevier
Seiten: - Band / Heft: 261 Artikelnummer: - Start- / Endseite: 142 - 148 Identifikator: ISSN: 0168-1656
CoNE: https://pure.mpg.de/cone/journals/resource/954925484698