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以……为例的分子机制中的组学全景。 (你提供的原文“with as an example”部分缺失关键信息,我只能按现有内容尽量准确翻译)

Omics landscapes in molecular mechanisms with as an example.

作者信息

Fu Dengke, Wang Yuanzhong, Zhang Jinyu

机构信息

Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650205, China.

College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Yunnan 650500, Kunming, China.

出版信息

Food Chem (Oxf). 2025 Aug 25;11:100294. doi: 10.1016/j.fochms.2025.100294. eCollection 2025 Dec.

Abstract

To improve the quality and efficiency of cultivating (AT), a non-model plant, it is crucial to understand the intrinsic molecular mechanisms underlying its growth. This review summarizes the significance of multi-omics in the study of plant molecular mechanisms and illustrates how multi-omics technology can solve the practical problems of non-model plants using AT as an example. In this review, we argue that nonlinear dimensionality reduction is more suitable for data organization in multi-omics because it is compatible with the nonlinear relationship between the components of systems biology. Subsequently, researchers have verified the strong vitality of multi-omics from three perspectives: the natural communication, breeding, and shade tolerance mechanisms of AT. Finally, we summarized some of the current commonly used plant genome databases and analyzed their utility for such research. We believe that our study makes a significant contribution to the literature because this review summarizes the multi-omics research process in detail, from data processing to application to the use of public databases, and illustrates the potential for the application of multi-omics with the example of a non-model plant, AT.

摘要

为提高非模式植物青蒿(AT)的培养质量和效率,了解其生长背后的内在分子机制至关重要。本综述总结了多组学在植物分子机制研究中的重要性,并以青蒿为例说明多组学技术如何解决非模式植物的实际问题。在本综述中,我们认为非线性降维更适合多组学中的数据组织,因为它与系统生物学各组分之间的非线性关系相兼容。随后,研究人员从青蒿的自然交流、育种和耐荫机制三个角度验证了多组学的强大生命力。最后,我们总结了一些当前常用的植物基因组数据库,并分析了它们在此类研究中的效用。我们相信我们的研究对文献做出了重大贡献,因为本综述详细总结了多组学的研究过程,从数据处理到应用再到公共数据库的使用,并以非模式植物青蒿为例说明了多组学的应用潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/051f/12418846/a8c634a29e17/gr1.jpg

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