@Article{Fahrner_Foll_Gruning-Democ_data_acqui-2022,
  author =	 {Fahrner, Matthias and Foll, Melanie Christine and Gruning, 
                  Bjorn Andreas and Bernt, Matthias and Rost, Hannes and 
                  Schilling, Oliver},
  title =	 {Democratizing data-independent acquisition proteomics 
                  analysis on public cloud infrastructures via the {Galaxy} 
                  framework},
  journal =	 {Gigascience},
  year =	 {2022},
  volume =	 {11},
  number =	 {},
  pages =	 {},
  user =	 {backofen},
  pmid =	 {35166338},
  doi = 	 {10.1093/gigascience/giac005},
  issn = 	 {2047-217X},
  abstract =	 {BACKGROUND: Data-independent acquisition (DIA) has become 
                  an important approach in global, mass spectrometric 
                  proteomic studies because it provides in-depth insights into 
                  the molecular variety of biological systems. However, DIA 
                  data analysis remains challenging owing to the high 
                  complexity and large data and sample size, which require 
                  specialized software and vast computing infrastructures. 
                  Most available open-source DIA software necessitates basic 
                  programming skills and covers only a fraction of a complete 
                  DIA data analysis. In consequence, DIA data analysis often 
                  requires usage of multiple software tools and compatibility 
                  thereof, severely limiting the usability and 
                  reproducibility. FINDINGS: To overcome this hurdle, we have 
                  integrated a suite of open-source DIA tools in the Galaxy 
                  framework for reproducible and version-controlled data 
                  processing. The DIA suite includes OpenSwath, PyProphet, 
                  diapysef, and swath2stats. We have compiled functional 
                  Galaxy pipelines for DIA processing, which provide a 
                  web-based graphical user interface to these pre-installed 
                  and pre-configured tools for their use on freely accessible, 
                  powerful computational resources of the Galaxy framework. 
                  This approach also enables seamless sharing workflows with 
                  full configuration in addition to sharing raw data and 
                  results. We demonstrate the usability of an all-in-one DIA 
                  pipeline in Galaxy by the analysis of a spike-in case study 
                  dataset. Additionally, extensive training material is 
                  provided to further increase access for the proteomics 
                  community. CONCLUSION: The integration of an open-source DIA 
                  analysis suite in the web-based and user-friendly Galaxy 
                  framework in combination with extensive training material 
                  empowers a broad community of researches to perform 
                  reproducible and transparent DIA data analysis.}
}