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首页> 外文期刊>Frontiers in Plant Science >BarleyNet: A Network-Based Functional Omics Analysis Server for Cultivated Barley, Hordeum vulgare L.
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BarleyNet: A Network-Based Functional Omics Analysis Server for Cultivated Barley, Hordeum vulgare L.

机译:BarleyNet:用于耕种大麦的基于网络的功能OMIC分析服务器,<斜体> Hordeum Vulgare L.

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Cultivated barley ( Hordeum vulgare 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.
机译:耕种大麦(Hordeum Vulgare L.)是玉米,面包小麦和米饭之后最具生产的谷物作物之一。大麦是一种重要的作物物种,不仅是食物来源,而且在植物遗传学中,因为它在其基因组中突出了许多可以利用作物工程的基因组的应力响应等位基因。然而,与其他主要作物相比,其基因组的功能诠释相对较差。此外,来自Barley的全系统范围内的生物信息学工具尚不可用。因此,我们已经开发了BarleyNet,这是一个26,145大麦基因的合作网络,以及用于基于网络的网络服务器的Web服务器(http://www.inetbio.org/barleynet)。我们证明Barrootnet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基于网络的算法,用于优先考虑基因或功能概念,以研究复杂性状的遗传成分,例如环境应力响应:(i)以途径或复杂性状的候选基因为中心的途径搜索; (ii)以基因为中心的搜索,以推断出基因的新功能概念; (iii)以背景为中心寻找与压力反应相关的新基因。我们展示了这些网络分析工具在使用蛋白质组学和转录组织在干旱或热应力下从大麦叶和根的转录组织数据进行压力反应研究的有用性。这些结果表明,巴利网络将促进我们对大麦复杂性状的潜在遗传成分的理解。

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