Introduction

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.

Publications

  1. A foundation for reliable spatial proteomics data analysis.
    Cite this
    Gatto L, Breckels LM, Burger T, Nightingale DJ, Groen AJ, Campbell C, Nikolovski N, Mulvey CM, Christoforou A, Ferro M, Lilley KS, 2014-08-01 - Molecular & cellular proteomics : MCP
  2. The effect of organelle discovery upon sub-cellular protein localisation.
    Cite this
    Breckels LM, Gatto L, Christoforou A, Groen AJ, Lilley KS, Trotter MW, 2013-08-01 - Journal of proteomics

Credits

  1. Laurent Gatto
    Developer

    From the ‡Cambridge Centre for Proteomics, Department of Biochemistry

  2. Lisa M Breckels
    Developer

    From the ‡Cambridge Centre for Proteomics, Department of Biochemistry

  3. Thomas Burger
    Developer

    ¶Université Grenoble-Alpes, CEA (iRSTV/BGE), France

  4. Daniel J H Nightingale
    Developer

    From the ‡Cambridge Centre for Proteomics, Department of Biochemistry

  5. Arnoud J Groen
    Developer

    From the ‡Cambridge Centre for Proteomics, Department of Biochemistry

  6. Callum Campbell
    Developer

    From the ‡Cambridge Centre for Proteomics, Department of Biochemistry

  7. Nino Nikolovski
    Developer

    From the ‡Cambridge Centre for Proteomics, Department of Biochemistry

  8. Claire M Mulvey
    Developer

    From the ‡Cambridge Centre for Proteomics, Department of Biochemistry

  9. Andy Christoforou
    Developer

    From the ‡Cambridge Centre for Proteomics, Department of Biochemistry

  10. Myriam Ferro
    Developer

    ¶Université Grenoble-Alpes, CEA (iRSTV/BGE), France

  11. Kathryn S Lilley
    Investigator

    From the ‡Cambridge Centre for Proteomics, Department of Biochemistry

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Summary
AccessionBT000819
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByKathryn S Lilley