The utility of functional interaction and cluster analysis in CNS proteomics

Ruth F. Deighton, Duncan M. Short, Richard J. McGregor, Alan J. Gow, Ian R. Whittle, James McCulloch

    Research output: Contribution to journalArticle

    13 Citations (Scopus)

    Abstract

    Proteomic studies offer enormous potential for gaining insight into cellular dynamics and disease processes. An immediate challenge for enhancing the utility of proteomics in translational research lies in methods of handling and interpreting the large datasets generated. Publications rarely extend beyond lists of proteins, putatively altered derived from basic statistics. Here we describe two additional distinct approaches (with particular strengths and limitations) that will enhance the analysis of proteomic datasets. Arithmetic and functional cluster analyses have been performed on proteins found differentially regulated in human glioma. These two approaches highlight (i) subgroups of proteins that may be co-regulated and play a role in glioma pathophysiology, and (ii) functional protein interactions that may improve comprehension of the biological mechanisms involved. A coherent proteomic strategy which involves both arithmetic and functional clustering, (together with careful consideration of conceptual limitations), is imperative for quantitative proteomics to deliver and advance the biological understanding of disease of the CNS. A strategy which combines arithmetic analysis and bioinformatics of protein-protein interactions is both generally applicable and will facilitate the interpretation of proteomic data. (C) 2009 Elsevier B.V. All rights reserved.

    Original languageEnglish
    Pages (from-to)321-329
    Number of pages9
    JournalJournal of Neuroscience Methods
    Volume180
    Issue number2
    DOIs
    Publication statusPublished - 15 Jun 2009

    Keywords

    • clinical proteomics
    • glioblastoma-multiforme
    • protein expression
    • identification
    • proteins
    • 2-dimensional gel-electrophoresis
    • cluster analysis
    • glioma
    • malignant glioma
    • statistical analysis
    • rat

    Cite this