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Comprehensive Network Analysis of Anther-Expressed Genes in Rice by the Combination of 33 Laser Microdissection and 143 Spatiotemporal Microarrays

机译:33激光显微切割和143时空微阵列相结合的水稻花药表达基因综合网络分析

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摘要

Co-expression networks systematically constructed from large-scale transcriptome data reflect the interactions and functions of genes with similar expression patterns and are a powerful tool for the comprehensive understanding of biological events and mining of novel genes. In Arabidopsis (a model dicot plant), high-resolution co-expression networks have been constructed from very large microarray datasets and these are publicly available as online information resources. However, the available transcriptome data of rice (a model monocot plant) have been limited so far, making it difficult for rice researchers to achieve reliable co-expression analysis. In this study, we performed co-expression network analysis by using combined 44 K agilent microarray datasets of rice, which consisted of 33 laser microdissection (LM)-microarray datasets of anthers, and 143 spatiotemporal transcriptome datasets deposited in RicexPro. The entire data of the rice co-expression network, which was generated from the 176 microarray datasets by the Pearson correlation coefficient (PCC) method with the mutual rank (MR)-based cut-off, contained 24,258 genes and 60,441 genes pairs. Using these datasets, we constructed high-resolution co-expression subnetworks of two specific biological events in the anther, “meiosis” and “pollen wall synthesis”. The meiosis network contained many known or putative meiotic genes, including genes related to meiosis initiation and recombination. In the pollen wall synthesis network, several candidate genes involved in the sporopollenin biosynthesis pathway were efficiently identified. Hence, these two subnetworks are important demonstrations of the efficiency of co-expression network analysis in rice. Our co-expression analysis included the separated transcriptomes of pollen and tapetum cells in the anther, which are able to provide precise information on transcriptional regulation during male gametophyte development in rice. The co-expression network data presented here is a useful resource for rice researchers to elucidate important and complex biological events.
机译:由大规模转录组数据系统构建的共表达网络反映了具有相似表达模式的基因的相互作用和功能,是全面了解生物学事件和挖掘新基因的有力工具。在拟南芥(双子叶植物的典范植物)中,已经从非常大的微阵列数据集中构建了高分辨率的共表达网络,这些网络可以作为在线信息资源公开获得。但是,到目前为止,水稻(单子叶植物的典范植物)的可用转录组数据有限,这使水稻研究人员难以实现可靠的共表达分析。在这项研究中,我们利用水稻的44 K agilent微阵列数据集进行了共表达网络分析,该数据集由33个花药的激光显微切割(LM)-微阵列数据集和143个时空转录组数据集组成。水稻共表达网络的全部数据由176个微阵列数据集通过Pearson相关系数(PCC)方法生成,基于互秩(MR)的截止值,包含24,258个基因和60,441个基因对。使用这些数据集,我们构建了由花药中的两个特定生物学事件“减数分裂”和“花粉壁合成”组成的高分辨率共表达子网络。减数分裂网络包含许多已知或推测的减数分裂基因,包括与减数分裂起始和重组有关的基因。在花粉壁合成网络中,有效地鉴定了参与孢粉蛋白生物合成途径的几个候选基因。因此,这两个子网是水稻共表达网络分析效率的重要证明。我们的共表达分析包括了花药中花粉和绒毡层细胞分离的转录组,它们能够提供水稻雄配子体发育过程中转录调控的精确信息。此处提供的共表达网络数据为水稻研究人员阐明重要而复杂的生物学事件提供了有用的资源。

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