Lee Sungho, Lee Tak, Yang Sunmo, Lee Insuk
Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, South Korea.
Front Plant Sci. 2020 Feb 18;11:98. doi: 10.3389/fpls.2020.00098. eCollection 2020.
Cultivated barley ( L.) is one of the most produced cereal crops worldwide after maize, bread wheat, and rice. Barley is an important crop species not only as a food source, but also in plant genetics because it harbors numerous stress response alleles in its genome that can be exploited for crop engineering. However, the functional annotation of its genome is relatively poor compared with other major crops. Moreover, bioinformatics tools for system-wide analyses of omics data from barley are not yet available. We have thus developed BarleyNet, a co-functional network of 26,145 barley genes, along with a web server for network-based predictions (http://www.inetbio.org/barleynet). We demonstrated that BarleyNet's prediction of biological processes is more accurate than that of an existing barley gene network. We implemented three complementary network-based algorithms for prioritizing genes or functional concepts to study genetic components of complex traits such as environmental stress responses: (i) a pathway-centric search for candidate genes of pathways or complex traits; (ii) a gene-centric search to infer novel functional concepts for genes; and (iii) a context-centric search for novel genes associated with stress response. We demonstrated the usefulness of these network analysis tools in the study of stress response using proteomics and transcriptomics data from barley leaves and roots upon drought or heat stresses. These results suggest that BarleyNet will facilitate our understanding of the underlying genetic components of complex traits in barley.
栽培大麦（L.）是全球产量仅次于玉米、面包小麦和水稻的主要谷类作物之一。大麦不仅是一种重要的粮食作物，在植物遗传学领域也具有重要地位，因为其基因组中含有众多应激反应等位基因，可用于作物工程。然而，与其他主要作物相比，大麦基因组的功能注释相对较少。此外，目前还没有用于对大麦组学数据进行全系统分析的生物信息学工具。因此，我们开发了BarleyNet，这是一个包含26145个大麦基因的共功能网络，并搭建了一个用于基于网络进行预测的网络服务器（http://www.inetbio.org/barleynet）。我们证明，BarleyNet对生物过程的预测比现有的大麦基因网络更准确。我们实施了三种基于网络的互补算法，用于对基因或功能概念进行优先级排序，以研究复杂性状（如环境应激反应）的遗传成分：（i）以途径为中心搜索途径或复杂性状的候选基因；（ii）以基因为中心搜索以推断基因的新功能概念；（iii）以上下文为中心搜索与应激反应相关的新基因。我们利用干旱或热胁迫下大麦叶片和根系的蛋白质组学和转录组学数据，证明了这些网络分析工具在应激反应研究中的实用性。这些结果表明，BarleyNet将有助于我们理解大麦复杂性状的潜在遗传成分。
