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Approaches for Extracting Practical Information from Gene Co-expression Networks in Plant Biology

机译:从植物生物学基因共表达网络中提取实用信息的方法

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Gene co-expression, in many cases, implies the presence of a functional linkage between genes. Co-expression analysis has uncovered gene regulatory mechanisms in model organisms such as Escherichia coli and yeast. Recently, accumulation of Arabidopsis microarray data has facilitated a genome-wide inspection of gene co-expression profiles in this model plant. An approach using network analysis has provided an intuitive way to represent complex co-expression patterns between many genes. Co-expression network analysis has enabled us to extract modules, or groups of tightly co-expressed genes, associated with biological processes. Furthermore, integrated analysis of gene expression and metabolite accumulation has allowed us to hypothesize the functions of genes associated with specific metabolic processes. Co-expression network analysis is a powerful approach for data-driven hypothesis construction and gene prioritization, and provides novel insights into the system-level understanding of plant cellular processes.
机译:在许多情况下,基因共表达意味着基因之间存在功能性连接。共表达分析在模型生物如大肠杆菌和酵母菌中发现了基因调控机制。最近,拟南芥微阵列数据的积累促进了该模型植物中基因共表达谱的全基因组检查。使用网络分析的方法提供了一种直观的方式来表示许多基因之间的复杂共表达模式。共表达网络分析使我们能够提取与生物学过程相关的模块或紧密共表达的基因组。此外,基因表达和代谢物积累的综合分析使我们能够假设与特定代谢过程相关的基因的功能。共表达网络分析是一种用于数据驱动的假设构建和基因优先级排序的强大方法,并且提供了对植物细胞过程的系统级理解的新颖见解。

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