Christian Panse

Dr. Christian Panse

orcid

Research Focus & Metrics

Academic Career Length

21Years (2006-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 March 2023 in Journal of Proteome Research
Verified
Journal Article
Open access
WWWitold E. Wolski
Witold E. Wolski
PNPaolo Nanni
Paolo Nanni
JGJonas Grossmann
Jonas Grossmann
MDMaria D’errico
Maria D’errico
Ralph SchlapbachRalph Schlapbach
Ralph Schlapbach
CPChristian Panse
Christian Panse

22 April 2019 in Nature Methods
Verified
Journal Article
Closed access
PDF file
PEPascal Egloff
Pascal Egloff
IZIwan Zimmermann
Iwan Zimmermann
FAFabian M. Arnold
Fabian M. Arnold
CHCedric A. J. Hutter
Cedric A. J. Hutter
DMDamien Morger
Damien Morger
MSMarkus A. Seeger
Markus A. Seeger

10 May 2021 in Journal of Proteome Research
Verified
Journal Article
Closed access
TSTobias Schmidt
Tobias Schmidt
PSPatroklos Samaras
Patroklos Samaras
VDViktoria Dorfer
Viktoria Dorfer
CPChristian Panse
Christian Panse
TKTobias Kockmann
Tobias Kockmann
MWMathias Wilhelm
Mathias Wilhelm

30 April 2016 in Proteomics
Verified
Journal Article
Closed access
PDF file
TKTobias Kockmann
Tobias Kockmann
CTChristian Trachsel
Christian Trachsel
CPChristian Panse
Christian Panse
AWAsa Wahlander
Asa Wahlander
NSNathalie Selevsek
Nathalie Selevsek
Ralph SchlapbachRalph Schlapbach
Ralph Schlapbach

6 July 2018 in Journal of Proteome Research
Verified
Journal Article
Closed access
CTChristian Trachsel
Christian Trachsel
CPChristian Panse
Christian Panse
TKTobias Kockmann
Tobias Kockmann
WWWitold E. Wolski
Witold E. Wolski
JGJonas Grossmann
Jonas Grossmann
Ralph SchlapbachRalph Schlapbach
Ralph Schlapbach

9 March 2021 in Journal of Proteome Research
Verified
Journal Article
Open access
TKTobias Kockmann
Tobias Kockmann
CPChristian Panse
Christian Panse
The Bioconductor project (Nat. Methods2015, 12 (2), 115–121) has shown that the R statistical environment is a highly valuable tool for genomics data analysis, but with respect to proteomics, we are still missing low-level infrastructure to enable performant and robust analysis workflows in R. Fundamentally important are libraries that provide raw data access. Our R package rawDiag (J. Proteome Res.2018, 17 (8), 2908–2914) has provided the proof-of-principle how access to mass spectrometry raw files can be realized by wrapping a vendor-provided advanced programming interface (API) for the purpose of metadata analysis and visualization. Our novel package rawrr now provides complete, OS-independent access to all spectral data logged in Thermo Fisher Scientific raw files. In this technical note, we present implementation details and describe the main functionalities provided by the rawrr package. In addition, we report two use cases inspired by real-world research tasks that demonstrate the application of the package. The raw data used for demonstration purposes was deposited as MassIVE data set MSV000086542. Availability: https://github.com/fgcz/rawrr.