title: Interpretation of Quantitative Shotgun Proteomic Data.
abstract: In quantitative proteomics, large lists of identified and quantified proteins are used to answer biological questions in a systemic approach. However, working with such extensive datasets can be challenging, especially when complex experimental designs are involved. Here, we demonstrate how to post-process large quantitative datasets, detect proteins of interest, and annotate the data with biological knowledge. The protocol presented can be achieved without advanced computational knowledge thanks to the user-friendly Perseus interface (available from the MaxQuant website, www.maxquant.org ). Various visualization techniques facilitating the interpretation of quantitative results in complex biological systems are also highlighted.
authors.affiliation: Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.
authors.forename: Elise
authors.initials: E
authors.lastname: Aasebø
authors.affiliation: Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.|KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway.|Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital, Bergen, Norway.
authors.forename: Frode S
authors.initials: FS
authors.lastname: Berven
authors.affiliation: Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.
authors.forename: Frode
authors.initials: F
authors.lastname: Selheim
authors.affiliation: Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.|KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway.
authors.forename: Harald
authors.initials: H
authors.lastname: Barsnes
authors.affiliation: Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway. marc.vaudel@uib.no.
authors.forename: Marc
authors.initials: M
authors.lastname: Vaudel
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D025341:Principal Component Analysis; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D012984:Software
chemical_list: D020543:Proteome
keywords: Data interpretation; Data post-processing; Perseus; Quantification