National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland.
Biotechnol Bioeng. 2012 Jun;109(6):1368-70. doi: 10.1002/bit.24416. Epub 2012 Jan 4.
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.
共表达分析是一种强大的、广泛使用的方法，用于研究基因表达数据中的潜在模式。这种“关联有罪”的方法旨在找到具有密切相关表达谱的基因群。观察到在表型多样化的样本中存在一致的相关性表明这些基因具有共同的功能。我们最近描述了加权基因共表达网络分析（WGCNA）在 295 个样本生产 CHO 细胞系微阵列数据集的应用，并阐明了与生长速率和细胞特异性生产力（Qp）相关的基因群。在这项研究中，我们提出了 CHO 基因共表达数据库（CGCDB），这是一个基于网络的系统，专为 CHO 研究社区的研究人员设计，提供了对这些基因-基因共表达模式的用户友好访问。除了基因之间的相关性外，还提供了探针与生长速率或 Qp 之间的直接相关性。结果通过交互式网络图和可下载的表格格式呈现给用户。希望该资源将使研究人员能够优先考虑细胞系工程和/或生物标志物候选物，以提高基于 CHO 的细胞培养生产生物疗法的效率。
