Large-scale analysis of Arabidopsis transcription reveals a basal co-regulation network
Large-scale analysis of Arabidopsis transcription reveals a basal co-regulation network
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DOI：
10.1186/1752-0509-3-86
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发表时间：
2009-09-03
影响因子：
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通讯作者：
Chamovitz, Daniel A.
中科院分区：
文献类型：
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作者：
Atias, Osnat;Chor, Benny;Chamovitz, Daniel A.
背景资料：从微阵列实验的基因表达数据的分析已成为一个核心工具，用于确定共调节，功能基因模块。这种分析的一个关键方面是整合来自不同实验和不同实验室的数据。如何衡量不同实验的贡献是影响最终结果的一个重要因素。我们已经开发了一种新的方法，这种整合，并将其应用到全基因组数据从多个拟南芥微阵列实验在各种实验条件下进行。本研究的目的是确定功能的全局共调控基因模块在拟南芥genome.Results：在43个数据集和约2 × 10（8）基因对的21,000拟南芥基因的分析，我们确定了一个全局共表达的基因网络。我们发现集群的全球共表达的拟南芥基因，是丰富的已知基因本体论注释。两种类型的模块被确定在监管网络中，不同的节点评分参数的敏感性，我们进一步表明这两个属于一般和专门的模块。其中一些模块使用Genevestigator微阵列实验纲要进行了进一步研究。较小的数据子集的分析导致条件特定modules.Conclusion的识别：我们的方法用于识别基因簇允许整合不同的微阵列实验从许多来源。分析表明，拟南芥转录组的一部分是全球共表达的，并可以进一步分为已知的以及新的功能基因模块。我们的方法是通用的，足以适用于任何一组微阵列实验，使用任何评分函数。
Background: Analyses of gene expression data from microarray experiments has become a central tool for identifying co-regulated, functional gene modules. A crucial aspect of such analysis is the integration of data from different experiments and different laboratories. How to weigh the contribution of different experiments is an important point influencing the final outcomes. We have developed a novel method for this integration, and applied it to genome-wide data from multiple Arabidopsis microarray experiments performed under a variety of experimental conditions. The goal of this study is to identify functional globally co-regulated gene modules in the Arabidopsis genome.Results: Following the analysis of 21,000 Arabidopsis genes in 43 datasets and about 2 x 10(8) gene pairs, we identified a globally co-expressed gene network. We found clusters of globally co-expressed Arabidopsis genes that are enriched for known Gene Ontology annotations. Two types of modules were identified in the regulatory network that differed in their sensitivity to the node-scoring parameter; we further showed these two pertain to general and specialized modules. Some of these modules were further investigated using the Genevestigator compendium of microarray experiments. Analyses of smaller subsets of data lead to the identification of condition-specific modules.Conclusion: Our method for identification of gene clusters allows the integration of diverse microarray experiments from many sources. The analysis reveals that part of the Arabidopsis transcriptome is globally co-expressed, and can be further divided into known as well as novel functional gene modules. Our methodology is general enough to apply to any set of microarray experiments, using any scoring function.