@article{Pinter_Glatzer_Fahrner-MaxQu_and_MSsta-2022,
author = {Pinter, Niko and Glatzer, Damian and Fahrner, Matthias and 
          Frohlich, Klemens and Johnson, James and Grüning, Björn 
          Andreas and Warscheid, Bettina and Drepper, Friedel and 
          Schilling, Oliver and Foll, Melanie Christine},
title = {{MaxQuant} and {MSstats} in {Galaxy} {Enable} 
         {Reproducible} {Cloud}-{Based} {Analysis} of {Quantitative} 
         {Proteomics} {Experiments} for {Everyone}},
journal = {J Proteome Res},
year = {2022},
doi = {10.1021/acs.jproteome.2c00051},
volume = {21},
user = {backofen},
pmid = {35503992},
pages = {1558-1565},
number = {6},
issn = {1535-3893},
abstract = {Quantitative mass spectrometry-based proteomics has become 
            a high-throughput technology for the identification and 
            quantification of thousands of proteins in complex 
            biological samples. Two frequently used tools, MaxQuant and 
            MSstats, allow for the analysis of raw data and finding 
            proteins with differential abundance between conditions of 
            interest. To enable accessible and reproducible quantitative 
            proteomics analyses in a cloud environment, we have 
            integrated MaxQuant (including TMTpro 16/18plex), Proteomics 
            Quality Control (PTXQC), MSstats, and MSstatsTMT into the 
            open-source Galaxy framework. This enables the web-based 
            analysis of label-free and isobaric labeling proteomics 
            experiments via Galaxy's graphical user interface on public 
            clouds. MaxQuant and MSstats in Galaxy can be applied in 
            conjunction with thousands of existing Galaxy tools and 
            integrated into standardized, sharable workflows. Galaxy 
            tracks all metadata and intermediate results in analysis 
            histories, which can be shared privately for collaborations 
            or publicly, allowing full reproducibility and transparency 
            of published analysis. To further increase accessibility, we 
            provide detailed hands-on training materials. The 
            integration of MaxQuant and MSstats into the Galaxy 
            framework enables their usage in a reproducible way on 
            accessible large computational infrastructures, hence 
            realizing the foundation for high-throughput proteomics data 
            science for everyone.}
}