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转录组数据建模在靶向植物代谢工程中的应用。

Transcriptome data modeling for targeted plant metabolic engineering.

机构信息

RIKEN Plant Science Center, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan.

出版信息

Curr Opin Biotechnol. 2013 Apr;24(2):285-90. doi: 10.1016/j.copbio.2012.10.018. Epub 2012 Dec 4.

Abstract

The massive data generated by omics technologies require the power of bioinformatics, especially network analysis, for data mining and doing data-driven biology. Gene coexpression analysis, a network approach based on comprehensive gene expression data using microarrays, is becoming a standard tool for predicting gene function and elucidating the relationship between metabolic pathways. Differential and comparative gene coexpression analyses suggest a change in coexpression relationships and regulators controlling common and/or specific biological processes. In conjunction with the newly emerging genome editing technology, network analysis integrated with other omics data should pave the way for robust and practical plant metabolic engineering.

摘要

组学技术产生的大量数据需要生物信息学的支持,特别是网络分析,以进行数据挖掘和数据驱动的生物学研究。基于微阵列的综合基因表达数据的基因共表达分析是一种网络方法,正在成为预测基因功能和阐明代谢途径之间关系的标准工具。差异和比较基因共表达分析表明,共同和/或特定生物过程的共表达关系和调控因子发生了变化。与新兴的基因组编辑技术相结合,与其他组学数据集成的网络分析应该为稳健和实用的植物代谢工程铺平道路。

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