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Global Landscape of a Co-Expressed Gene Network in Barley and its Application to Gene Discovery in Triticeae Crops

机译:大麦共表达基因网络的全球格局及其在麦类作物基因发现中的应用

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摘要

Accumulated transcriptome data can be used to investigate regulatory networks of genes involved in various biological systems. Co-expression analysis data sets generated from comprehensively collected transcriptome data sets now represent efficient resources that are capable of facilitating the discovery of genes with closely correlated expression patterns. In order to construct a co-expression network for barley, we analyzed 45 publicly available experimental series, which are composed of 1,347 sets of GeneChip data for barley. On the basis of a gene-to-gene weighted correlation coefficient, we constructed a global barley co-expression network and classified it into clusters of subnetwork modules. The resulting clusters are candidates for functional regulatory modules in the barley transcriptome. To annotate each of the modules, we performed comparative annotation using genes in Arabidopsis and Brachypodium distachyon. On the basis of a comparative analysis between barley and two model species, we investigated functional properties from the representative distributions of the gene ontology (GO) terms. Modules putatively involved in drought stress response and cellulose biogenesis have been identified. These modules are discussed to demonstrate the effectiveness of the co-expression analysis. Furthermore, we applied the data set of co-expressed genes coupled with comparative analysis in attempts to discover potentially Triticeae-specific network modules. These results demonstrate that analysis of the co-expression network of the barley transcriptome together with comparative analysis should promote the process of gene discovery in barley. Furthermore, the insights obtained should be transferable to investigations of Triticeae plants. The associated data set generated in this analysis is publicly accessible at .
机译:积累的转录组数据可用于研究涉及各种生物系统的基因的调控网络。从全面收集的转录组数据集生成的共表达分析数据集现在代表了有效的资源,能够促进发现紧密相关的表达模式的基因。为了构建大麦的共表达网络,我们分析了45个可公开获得的实验系列,其中包括1,347套大麦的GeneChip数据。在基因间加权相关系数的基础上,我们构建了一个全球大麦共表达网络,并将其分类为子网模块的集群。产生的簇是大麦转录组中功能调节模块的候选者。为了注释每个模块,我们使用拟南芥和短枝Bra属中的基因进行了比较注释。在对大麦和两个模型物种进行比较分析的基础上,我们从基因本体论(GO)术语的代表性分布中研究了功能特性。已经确定了与干旱胁迫响应和纤维素生物发生有关的模块。讨论这些模块以证明共表达分析的有效性。此外,我们将共表达基因的数据集与比较分析相结合,以试图发现潜在的小麦特有的网络模块。这些结果表明,对大麦转录组共表达网络的分析以及比较分析应促进大麦基因发现的过程。此外,所获得的见解应可用于小麦的研究。此分析中生成的相关数据集可在上公开访问。

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