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用于微生物组分析的宏基因组学、宏转录组学和代谢组学方法。

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis.

作者信息

Aguiar-Pulido Vanessa, Huang Wenrui, Suarez-Ulloa Victoria, Cickovski Trevor, Mathee Kalai, Narasimhan Giri

机构信息

Bioinformatics Research Group (BioRG), School of Computing and Information Sciences, Florida International University, Miami, FL, USA.

Chromatin Structure and Evolution Group (Chromevol), Department of Biological Sciences, Florida International University, Miami, FL, USA.

出版信息

Evol Bioinform Online. 2016 May 12;12(Suppl 1):5-16. doi: 10.4137/EBO.S36436. eCollection 2016.

Abstract

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from multiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes.

摘要

微生物群落无处不在,存在于海洋、土壤以及其他生物体内/体表。微生物群落的变化会影响它们所处环境生态位的健康状况。为了更深入了解这些群落,人们采用了基于多种组学数据的不同方法。宏基因组学可生成样本的分类学概况,宏转录组学有助于我们获得功能概况,而代谢组学则通过确定哪些副产物被释放到环境中完善整体情况。虽然每种方法都能单独提供有价值的信息,但我们发现,将它们结合起来能描绘出更全面的图景。我们最后回顾了应用于综合研究的基于网络的方法,我们认为这是深入理解微生物群落的关键。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e67/4869604/8f8b357370ed/ebo-suppl.1-2016-005f1.jpg

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