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整合多种“组学”分析方法研究微生物生物学:应用与方法。

Integrating multiple 'omics' analysis for microbial biology: application and methodologies.

机构信息

Center for Ecogenomics, Biodesign Institute, Arizona State University, Tempe, AZ 85287-6501, USA.

Division of Biometrics II, Office of Biometrics/OTS/CDER/FDA, Silver Spring, MD 20993-0002, USA.

出版信息

Microbiology (Reading). 2010 Feb;156(Pt 2):287-301. doi: 10.1099/mic.0.034793-0. Epub 2009 Nov 12.

Abstract

Recent advances in various 'omics' technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. Several popular 'omics' platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single 'omics' analysis can fully unravel the complexities of fundamental microbial biology. Therefore, integration of multiple layers of information, the multi-'omics' approach, is required to acquire a precise picture of living micro-organisms. In spite of this being a challenging task, some attempts have been made recently to integrate heterogeneous 'omics' datasets in various microbial systems and the results have demonstrated that the multi-'omics' approach is a powerful tool for understanding the functional principles and dynamics of total cellular systems. This article reviews some basic concepts of various experimental 'omics' approaches, recent application of the integrated 'omics' for exploring metabolic and regulatory mechanisms in microbes, and advances in computational and statistical methodologies associated with integrated 'omics' analyses. Online databases and bioinformatic infrastructure available for integrated 'omics' analyses are also briefly discussed.

摘要

近年来,各种“组学”技术的进步使得能够高通量地定量监测各种生物分子的丰度,从而能够在基因组范围内确定它们在不同生物状态之间的变化。在微生物系统生物学中已经使用了几种流行的“组学”平台,包括转录组学,它测量 mRNA 转录本水平;蛋白质组学,它定量蛋白质丰度;代谢组学,它确定细胞内小分子代谢物的丰度;相互作用组学,它解析细胞内的全部分子相互作用;以及通量组学,它建立了细胞内分子随时间的动态变化。然而,没有单一的“组学”分析可以完全揭示基本微生物生物学的复杂性。因此,需要整合多个层次的信息,即多“组学”方法,以获得对活微生物的精确描述。尽管这是一项具有挑战性的任务,但最近已经有人尝试在各种微生物系统中整合异构“组学”数据集,结果表明多“组学”方法是理解总细胞系统功能原理和动态的有力工具。本文综述了各种实验“组学”方法的一些基本概念,以及整合“组学”在探索微生物代谢和调控机制方面的最新应用,以及与整合“组学”分析相关的计算和统计方法的进展。还简要讨论了用于整合“组学”分析的在线数据库和生物信息学基础设施。

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