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计算宏基因组学在微生物群落研究中的应用。

Computational meta'omics for microbial community studies.

机构信息

Biostatistics Department, Harvard School of Public Health, Boston, MA, USA.

出版信息

Mol Syst Biol. 2013 May 14;9:666. doi: 10.1038/msb.2013.22.

Abstract

Complex microbial communities are an integral part of the Earth's ecosystem and of our bodies in health and disease. In the last two decades, culture-independent approaches have provided new insights into their structure and function, with the exponentially decreasing cost of high-throughput sequencing resulting in broadly available tools for microbial surveys. However, the field remains far from reaching a technological plateau, as both computational techniques and nucleotide sequencing platforms for microbial genomic and transcriptional content continue to improve. Current microbiome analyses are thus starting to adopt multiple and complementary meta'omic approaches, leading to unprecedented opportunities to comprehensively and accurately characterize microbial communities and their interactions with their environments and hosts. This diversity of available assays, analysis methods, and public data is in turn beginning to enable microbiome-based predictive and modeling tools. We thus review here the technological and computational meta'omics approaches that are already available, those that are under active development, their success in biological discovery, and several outstanding challenges.

摘要

复杂的微生物群落是地球生态系统的一个组成部分,也是我们健康和疾病身体的一个组成部分。在过去的二十年中,非培养方法为它们的结构和功能提供了新的见解,高通量测序成本的指数级下降为微生物调查提供了广泛可用的工具。然而,该领域远未达到技术高原,因为用于微生物基因组和转录组内容的计算技术和核苷酸测序平台仍在不断改进。因此,目前的微生物组分析开始采用多种互补的元组学方法,从而为全面准确地描述微生物群落及其与环境和宿主的相互作用提供了前所未有的机会。这种可用检测、分析方法和公共数据的多样性反过来又开始使基于微生物组的预测和建模工具成为可能。因此,我们在这里回顾了已经可用的技术和计算元组学方法、正在积极开发的方法、它们在生物学发现中的成功以及几个突出的挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08ef/4039370/eedbeb91c958/msb201322-f1.jpg

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