Serin Elise A R, Nijveen Harm, Hilhorst Henk W M, Ligterink Wilco
Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen University Wageningen, Netherlands.
Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen UniversityWageningen, Netherlands; Laboratory of Bioinformatics, Wageningen UniversityWageningen, Netherlands.
Front Plant Sci. 2016 Apr 8;7:444. doi: 10.3389/fpls.2016.00444. eCollection 2016.
Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.
植物是迷人而复杂的生物体。全面了解植物基因的组织、功能和进化对于理清重要的生物学过程以及推进作物工程和育种策略至关重要。破译复杂生物学过程的最终目标是发现控制这些过程的因果基因和调控机制。近期组学数据的激增为系统全面理解复杂性状背后的生物信息流打开了大门。然而，处理相应的大数据集是一项具有挑战性的工作，需要开发强大的生物信息学方法。一种常用的方法是构建和分析基因网络。此类网络常用于全基因组层面表示生物系统的复杂功能组织。基于基因表达相似性的网络称为（基因）共表达网络。基因共表达网络的主要应用之一是对未知基因进行功能注释。构建共表达网络通常较为简单。相比之下，最终得到的连接基因网络可能会变得非常复杂，这限制了其生物学解读。可以采用多种策略来加强对网络的解读。需要建立一种与所探讨的生物学问题相一致的策略来推断可靠的网络。利用先验知识和数据整合的基于网络的策略可带来额外的好处，进一步加强对基因调控关系的阐释。因此，生物网络提供的应用远不止于共表达基因的简单可视化。在本研究中，我们综述了植物中共表达网络推断的不同方法。我们分析了近期研究中使用的整合基因组学策略，这些研究利用基因共表达网络成功鉴定出了候选基因。此外，我们还讨论了为特定目的预测网络的有前景的生物信息学方法。
