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充分利用“组学”进行共生研究。

Making the most of "omics" for symbiosis research.

作者信息

Chaston J, Douglas A E

机构信息

Department of Entomology, Comstock Hall, Cornell University, Ithaca, New York 14853, USA.

出版信息

Biol Bull. 2012 Aug;223(1):21-9. doi: 10.1086/BBLv223n1p21.

Abstract

Omics, including genomics, proteomics, and metabolomics, enable us to explain symbioses in terms of the underlying molecules and their interactions. The central task is to transform molecular catalogs of genes, metabolites, etc., into a dynamic understanding of symbiosis function. We review four exemplars of omics studies that achieve this goal, through defined biological questions relating to metabolic integration and regulation of animal-microbial symbioses, the genetic autonomy of bacterial symbionts, and symbiotic protection of animal hosts from pathogens. As omic datasets become increasingly complex, computationally sophisticated downstream analyses are essential to reveal interactions not evident from visual inspection of the data. We discuss two approaches, phylogenomics and transcriptional clustering, that can divide the primary output of omics studies-long lists of factors-into manageable subsets, and we describe how they have been applied to analyze large datasets and generate testable hypotheses.

摘要

组学,包括基因组学、蛋白质组学和代谢组学,使我们能够从潜在分子及其相互作用的角度解释共生现象。核心任务是将基因、代谢物等分子目录转化为对共生功能的动态理解。我们回顾了四项组学研究实例,这些研究通过与动物-微生物共生的代谢整合和调控、细菌共生体的遗传自主性以及动物宿主免受病原体的共生保护相关的明确生物学问题实现了这一目标。随着组学数据集变得越来越复杂,计算复杂的下游分析对于揭示从数据的视觉检查中不明显的相互作用至关重要。我们讨论了两种方法,系统发育基因组学和转录聚类,它们可以将组学研究的主要输出——长长的因子列表——划分为可管理的子集,并且我们描述了它们如何被应用于分析大型数据集并生成可检验的假设。

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