@Article{Pinter_Glatzer_Fahrner-MaxQu_and_MSsta-2022,
  author =	 {Pinter, Niko and Glatzer, Damian and Fahrner, Matthias and 
                  Frohlich, Klemens and Johnson, James and Gruning, Bjorn 
                  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},
  volume =	 {21},
  number =	 {6},
  pages =	 {1558-1565},
  user =	 {backofen},
  pmid =	 {35503992},
  doi = 	 {10.1021/acs.jproteome.2c00051},
  issn = 	 {1535-3893},
  issn = 	 {1535-3907},
  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.}
}