BioDiscovery Institute and Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA.
Acta Biochim Biophys Sin (Shanghai). 2019 Sep 6;51(10):981-988. doi: 10.1093/abbs/gmz080.
Co-expression network analysis is one of the most powerful approaches for interpretation of large transcriptomic datasets. It enables characterization of modules of co-expressed genes that may share biological functional linkages. Such networks provide an initial way to explore functional associations from gene expression profiling and can be applied to various aspects of plant biology. This review presents the applications of co-expression network analysis in plant biology and addresses optimized strategies from the recent literature for performing co-expression analysis on plant biological systems. Additionally, we describe the combined interpretation of co-expression analysis with other genomic data to enhance the generation of biologically relevant information.
共表达网络分析是解释大型转录组数据集最有力的方法之一。它能够描述可能具有生物学功能联系的共表达基因模块。这种网络为从基因表达谱中探索功能关联提供了一种初始方法,并且可以应用于植物生物学的各个方面。本文综述了共表达网络分析在植物生物学中的应用,并讨论了近期文献中针对植物生物系统进行共表达分析的优化策略。此外,我们还描述了共表达分析与其他基因组数据的联合解释,以增强生成具有生物学意义的信息。