pRoloc

pRoloc is a software tool designed for the spatial proteomics community, explicitly addressing sub-cellular protein localization using high-throughput mass spectrometry (MS) data. Leveraging recent advances in MS methods, pRoloc integrates heterogeneous data sources, including immunofluorescence microscopy, protein annotations, and sequences, to enhance the accuracy and quantity of sub-cellular protein assignment. The tool employs a unique transfer learning classification framework, utilizing nearest-neighbor or support vector machine systems.

Topic

Proteomics experiment;Proteomics;Machine learning

Detail

  • Operation: Protein architecture analysis

  • Software interface: Command-line user interface,Library

  • Language: R

  • License: GNU General Public License, version 2

  • Cost: Free

  • Version name: 1.42.0

  • Credit: BBSRC Tools and Resources Development Fund, Wellcome Trust Technology Development Grant, the European Union 7th Framework Program, BBSRC Strategic Longer and Larger Award, Deutsche Forschungsgemeinschaft.

  • Input: -

  • Output: -

  • Contact: Laurent Gatto laurent.gatto@uclouvain.be

  • Collection: -

  • Maturity: Stable

Publications

  • ProLoc: prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features.
  • Huang WL, et al. ProLoc: prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features. ProLoc: prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features. 2007; 90:573-81. doi: 10.1016/j.biosystems.2007.01.001
  • https://doi.org/10.1016/j.biosystems.2007.01.001
  • PMID: 17291684
  • PMC: -
  • Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.
  • Breckels LM, et al. Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. 2016; 12:e1004920. doi: 10.1371/journal.pcbi.1004920
  • https://doi.org/10.1371/journal.pcbi.1004920
  • PMID: 27175778
  • PMC: PMC4866734

Download and documentation

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