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MSstatsShiny: A GUI for Versatile, Scalable, and Reproducible Statistical Analyses of Quantitative Proteomic Experiments.
Kohler, Devon; Kaza, Maanasa; Pasi, Cristina; Huang, Ting; Staniak, Mateusz; Mohandas, Dhaval; Sabido, Eduard; Choi, Meena; Vitek, Olga.
Affiliation
  • Kohler D; Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States.
  • Kaza M; Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States.
  • Pasi C; Universitat Oberta de Catalunya, Barcelona 08018, Spain.
  • Huang T; Khoury College of Computer Science, Northeastern University, Boston, Massachusetts 02115, United States.
  • Staniak M; University of Wroclaw, Wroclaw 50-137, Poland.
  • Mohandas D; EXL Service, New York, New York 10022, United States.
  • Sabido E; Center for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona 08003, Spain.
  • Choi M; Universitat Pompeu Fabra, Barcelona 08002, Spain.
  • Vitek O; MPL, Genentech, South San Francisco, California 94080, United States.
J Proteome Res ; 22(2): 551-556, 2023 02 03.
Article in En | MEDLINE | ID: mdl-36622173
ABSTRACT
Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is a versatile technology for identifying and quantifying proteins in complex biological mixtures. Postidentification, analysis of changes in protein abundances between conditions requires increasingly complex and specialized statistical methods. Many of these methods, in particular the family of open-source Bioconductor packages MSstats, are implemented in a coding language such as R. To make the methods in MSstats accessible to users with limited programming and statistical background, we have created MSstatsShiny, an R-Shiny graphical user interface (GUI) integrated with MSstats, MSstatsTMT, and MSstatsPTM. The GUI provides a point and click analysis pipeline applicable to a wide variety of proteomics experimental types, including label-free data-dependent acquisitions (DDAs) or data-independent acquisitions (DIAs), or tandem mass tag (TMT)-based TMT-DDAs, answering questions such as relative changes in the abundance of peptides, proteins, or post-translational modifications (PTMs). To support reproducible research, the application saves user's selections and builds an R script that programmatically recreates the analysis. MSstatsShiny can be installed locally via Github and Bioconductor, or utilized on the cloud at www.msstatsshiny.com. We illustrate the utility of the platform using two experimental data sets (MassIVE IDs MSV000086623 and MSV000085565).
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Proteomics Language: En Journal: J Proteome Res Journal subject: BIOQUIMICA Year: 2023 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Proteomics Language: En Journal: J Proteome Res Journal subject: BIOQUIMICA Year: 2023 Type: Article Affiliation country: United States