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    Exon level integration of proteomics and microarray data.

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    Authors
    Bitton, Danny A
    Okoniewski, Michal J
    Connolly, Yvonne
    Miller, Crispin J
    Affiliation
    Cancer Research UK, Applied Computational Biology and Bioinformatics Group, Paterson Institute for Cancer Research, The University of Manchester, Christie Hospital Site, Wilmslow Road, Manchester, M20 4BX, UK. dbitton@picr.man.ac.uk
    Issue Date
    2008
    
    Metadata
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    Abstract
    BACKGROUND: Previous studies comparing quantitative proteomics and microarray data have generally found poor correspondence between the two. We hypothesised that this might in part be because the different assays were targeting different parts of the expressed genome and might therefore be subjected to confounding effects from processes such as alternative splicing. RESULTS: Using a genome database as a platform for integration, we combined quantitative protein mass spectrometry with Affymetrix Exon array data at the level of individual exons. We found significantly higher degrees of correlation than have been previously observed (r = 0.808). The study was performed using cell lines in equilibrium in order to reduce a major potential source of biological variation, thus allowing the analysis to focus on the data integration methods in order to establish their performance. CONCLUSION: We conclude that part of the variation observed when integrating microarray and proteomics data may occur as a consequence both of the data analysis and of the high granularity to which studies have until recently been limited. The approach opens up the possibility for the first time of considering combined microarray and proteomics datasets at the level of individual exons and isoforms, important given the high proportion of alternative splicing observed in the human genome.
    Citation
    Exon level integration of proteomics and microarray data. 2008, 9:118 BMC Bioinformatics
    Journal
    BMC Bioinformatics
    URI
    http://hdl.handle.net/10541/68763
    DOI
    10.1186/1471-2105-9-118
    PubMed ID
    18298841
    Type
    Article
    Language
    en
    ISSN
    1471-2105
    ae974a485f413a2113503eed53cd6c53
    10.1186/1471-2105-9-118
    Scopus Count
    Collections
    Applied Computational Biology and Bioinformatics
    All Paterson Institute for Cancer Research
    Molecular Biology Core Facility

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