<i>DEprot</i> : a comprehensive R-package for the analysis of label-free quantitation mass spectrometry data

In recent years, studies investigating protein-protein interactions on a proteome-wide scale have increased exponentially. These developments were achieved through implementation of new techniques, as well as improvements in mass spectrometry hardware. However, issues specific to this type of technique, such as distinct imputation of random and not-at-random missing proteins, have not been completely addressed. Here, we present <i>DEprot</i>, an R-package providing a comprehensive toolset for end-to-end analysis and visualization of proteomics data. Moreover, we implemented a serial imputation strategy for handling non-random missing values, particularly suitable for protein enrichment techniques and knockout conditions.

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Publication Details

Journal
NAR Genomics and Bioinformatics
Published
2026-01-06
DOI
https://doi.org/10.1093/nargab/lqag015
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
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article

DEprot : a comprehensive R-package for the analysis of label-free quantitation mass spectrometry data

Sebastian Gregoricchio, Andries M. Bergman, Onno B. Bleijerveld, Liesbeth Hoekman et al.
NAR Genomics and Bioinformatics
Advanced Proteomics Techniques and Applications
article

DEprot : a comprehensive R-package for the analysis of label-free quantitation mass spectrometry data

Sebastian Gregoricchio, Andries M. Bergman, Onno B. Bleijerveld, Liesbeth Hoekman, Nils Eickhoff, Wilbert Zwart
article en

Abstract

In recent years, studies investigating protein-protein interactions on a proteome-wide scale have increased exponentially. These developments were achieved through implementation of new techniques, as well as improvements in mass spectrometry hardware. However, issues specific to this type of technique, such as distinct imputation of random and not-at-random missing proteins, have not been completely addressed. Here, we present <i>DEprot</i>, an R-package providing a comprehensive toolset for end-to-end analysis and visualization of proteomics data. Moreover, we implemented a serial imputation strategy for handling non-random missing values, particularly suitable for protein enrichment techniques and knockout conditions.

NAR Genomics and BioinformaticsVol. 8(1)
Oncode Institute (NL), Cancer Genomics Centre (NL), Amsterdam University Medical Centers (NL), Eindhoven University of Technology (NL), University of Amsterdam (NL), Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
Openalex Percentile: Top 61%
Advanced Proteomics Techniques and Applications
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<i>DEprot</i> : a comprehensive R-package for the analysis of label-free quantitation mass spectrometry data — Sebastian Gregoricchio, Andries M. Bergman, et al. · NAR Genomics and Bioinformatics (2026) | TGRS Research Map | TGRS