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CGCDB: A web-based resource for the investigation of gene coexpression in CHO cell culture

Authors

  • Colin Clarke,

    Corresponding author
    1. National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland; telephone: +353-1-7005700; fax: +353-1-7005484
    • National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland; telephone: +353-1-7005700; fax: +353-1-7005484.
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  • Padraig Doolan,

    1. National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland; telephone: +353-1-7005700; fax: +353-1-7005484
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  • Niall Barron,

    1. National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland; telephone: +353-1-7005700; fax: +353-1-7005484
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  • Paula Meleady,

    1. National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland; telephone: +353-1-7005700; fax: +353-1-7005484
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  • Stephen F. Madden,

    1. National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland; telephone: +353-1-7005700; fax: +353-1-7005484
    2. Molecular Therapeutics for Cancer Ireland, Dublin City University, Glasnevin, Dublin 9, Ireland
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  • Dana DiNino,

    1. Bioprocess R&D, Pfizer Inc., Andover, Massachusetts
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  • Mark Leonard,

    1. Bioprocess R&D, Pfizer Inc., Andover, Massachusetts
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  • Martin Clynes

    1. National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland; telephone: +353-1-7005700; fax: +353-1-7005484
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Abstract

Coexpression analysis is a powerful, widely used methodology for the investigation of underlying patterns in gene expression data. This “guilt-by-association” approach aims to find groups of genes with closely correlated expression profiles. Observation of consistent correlations across phenotypically diverse samples indicates that these genes have a shared function. We have recently described the application of weighted gene coexpression network analysis (WGCNA) to a 295 sample production CHO cell line microarray dataset and elucidated groups of genes related to growth rate and cell-specific productivity (Qp). In this study, we present the CHO gene coexpression database (CGCDB), a web-based system, designed specifically for researchers in the CHO community to provide user-friendly access to these gene–gene coexpression patterns. In addition to correlation between genes, the direct correlations between probesets and either growth rate or Qp are provided. Results are presented to the user via an interactive network diagram and in a downloadable tabular format. It is hoped that this resource will allow researchers to prioritize cell line engineering and/or biomarker candidates to enhance CHO-based cell culture for the production of biotherapeutics. Availability: www.cgcdb.org. Biotechnol. Bioeng. 2012; 109:1368–1370. © 2011 Wiley Periodicals, Inc.

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