The utility of functional interaction and cluster analysis in CNS proteomics

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...

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Bibliographic Details
Published in:Journal of neuroscience methods 2009-06, Vol.180 (2), p.321-329
Main Authors: Deighton, Ruth F., Short, Duncan M., McGregor, Richard J., Gow, Alan J., Whittle, Ian R., McCulloch, James
Format: Article
Language:English
Subjects:
Algorithms
Brain Chemistry - physiology
Brain Neoplasms - chemistry
Central Nervous System - chemistry
Clinical proteomics
Cluster Analysis
Computer Simulation
Evolution, Molecular
Glioma
Glioma - chemistry
Humans
Models, Molecular
Nerve Tissue Proteins - analysis
Nerve Tissue Proteins - chemistry
Neurochemistry - methods
Phylogeny
Protein expression
Proteomics - methods
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Summary: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.
ISSN:0165-0270
1872-678X
DOI:10.1016/j.jneumeth.2009.03.013