Aoki Koh, Ogata Yoshiyuki, Shibata Daisuke
Kazusa DNA Research Institute, Kazusa-Kamatari 2-6-7, Kisarazu, 292-0818, Japan.
Plant Cell Physiol. 2007 Mar;48(3):381-90. doi: 10.1093/pcp/pcm013. Epub 2007 Jan 23.
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.
在许多情况下,基因共表达意味着基因之间存在功能联系。共表达分析揭示了诸如大肠杆菌和酵母等模式生物中的基因调控机制。最近,拟南芥微阵列数据的积累促进了对这种模式植物基因共表达谱的全基因组检查。一种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复杂的共表达模式。共表达网络分析使我们能够提取与生物过程相关的模块或紧密共表达的基因群。此外,基因表达与代谢物积累的综合分析使我们能够推测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,并为植物细胞过程的系统水平理解提供了新的见解。