Liu Wei, Li Li, Ye Hua, Chen Haiwei, Shen Weibiao, Zhong Yuexian, Tian Tian, He Huaqin
School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, P.R. China.
Institute of Health Service and Medical Information, Academy of Military Medical Sciences, Beijing 100850, P.R. China.
Biomed Rep. 2017 Aug;7(2):153-158. doi: 10.3892/br.2017.941. Epub 2017 Jul 6.
Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Yeast has long been a popular model organism for biomedical research. In the current study, a weighted gene co-expression network analysis algorithm was applied to construct a gene co-expression network in . Seventeen stable gene co-expression modules were detected from 2,814 microarray data. Further characterization of these modules with the Database for Annotation, Visualization and Integrated Discovery tool indicated that these modules were associated with certain biological processes, such as heat response, cell cycle, translational regulation, mitochondrion oxidative phosphorylation, amino acid metabolism and autophagy. Hub genes were also screened by intra-modular connectivity. Finally, the module conservation was evaluated in a human disease microarray dataset. Functional modules were identified in budding yeast, some of which are associated with patient survival. The current study provided a paradigm for single cell microorganisms and potentially other organisms.
基于网络的系统生物学已成为分析高通量基因表达数据和挖掘基因功能的重要方法。长期以来,酵母一直是生物医学研究中常用的模式生物。在本研究中,应用加权基因共表达网络分析算法构建了一个基因共表达网络。从2814个微阵列数据中检测到17个稳定的基因共表达模块。使用注释、可视化和综合发现数据库工具对这些模块进行进一步表征,结果表明这些模块与某些生物学过程相关,如热反应、细胞周期、翻译调控、线粒体氧化磷酸化、氨基酸代谢和自噬。还通过模块内连通性筛选出了枢纽基因。最后,在人类疾病微阵列数据集中评估了模块保守性。在芽殖酵母中鉴定出了功能模块,其中一些与患者生存相关。本研究为单细胞微生物以及其他可能的生物体提供了一个范例。