title: Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata.
abstract: BACKGROUND
  Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools.
  
  
  RESULTS
  Here we present pRoloc, a complete infrastructure to support and guide the sound analysis of quantitative mass-spectrometry-based spatial proteomics data. It provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection to identify new putative sub-cellular clusters. The software builds upon existing infrastructure for data management and data processing.
authors.affiliation: Computational Proteomics Unit and Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, CB2 1QR, Cambridge, UK and Université Grenoble-Alpes, CEA (iRSTV/BGE), INSERM (U1038), CNRS (FR3425), 38054 Grenoble, France.
authors.forename: Laurent
authors.initials: L
authors.lastname: Gatto
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Breckels
authors.forename: Samuel
authors.initials: S
authors.lastname: Wieczorek
authors.forename: Thomas
authors.initials: T
authors.lastname: Burger
authors.forename: Kathryn S
authors.initials: KS
authors.lastname: Lilley
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D013058:Mass Spectrometry / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins