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本文引用的文献

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Survey data are still vital to science.调查数据对科学仍然至关重要。
Nature. 2011 Jan 13;469(7329):162. doi: 10.1038/469162a.
2
Accessing the soil metagenome for studies of microbial diversity.获取土壤宏基因组以研究微生物多样性。
Appl Environ Microbiol. 2011 Feb;77(4):1315-24. doi: 10.1128/AEM.01526-10. Epub 2010 Dec 23.
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Tackling the widespread and critical impact of batch effects in high-throughput data.解决高通量数据中广泛存在且极具影响力的批次效应问题。
Nat Rev Genet. 2010 Oct;11(10):733-9. doi: 10.1038/nrg2825. Epub 2010 Sep 14.
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Identifying biologically relevant differences between metagenomic communities.鉴定宏基因组群落间具有生物学意义的差异。
Bioinformatics. 2010 Mar 15;26(6):715-21. doi: 10.1093/bioinformatics/btq041. Epub 2010 Feb 3.
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To sequence or not to sequence the whole-soil metagenome?是否对土壤宏基因组进行测序?
Nat Rev Microbiol. 2009 Oct;7(10):756; author reply 756-7. doi: 10.1038/nrmicro2119-c2.
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cAMP signaling in Mycobacterium tuberculosis.结核分枝杆菌中的环磷酸腺苷(cAMP)信号传导
Indian J Exp Biol. 2009 Jun;47(6):393-400.
7
Metagenomic signatures of 86 microbial and viral metagenomes.86 个微生物和病毒宏基因组的宏基因组特征。
Environ Microbiol. 2009 Jul;11(7):1752-66. doi: 10.1111/j.1462-2920.2009.01901.x. Epub 2009 Mar 18.
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Functional role of bacterial multidrug efflux pumps in microbial natural ecosystems.细菌多药外排泵在微生物自然生态系统中的功能作用。
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9
The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes.宏基因组学RAST服务器——用于宏基因组自动系统发育和功能分析的公共资源。
BMC Bioinformatics. 2008 Sep 19;9:386. doi: 10.1186/1471-2105-9-386.
10
Worlds within worlds: evolution of the vertebrate gut microbiota.层层世界:脊椎动物肠道微生物群的进化
Nat Rev Microbiol. 2008 Oct;6(10):776-88. doi: 10.1038/nrmicro1978.

微生物学家的宏基因组挖掘。

Metagenomic mining for microbiologists.

机构信息

Environmental Microbial Genomics, Laboratoire Ampère, Ecole Centrale de Lyon, Université de Lyon, Ecully, France.

出版信息

ISME J. 2011 Dec;5(12):1837-43. doi: 10.1038/ismej.2011.61. Epub 2011 May 19.

DOI:10.1038/ismej.2011.61
PMID:21593798
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3223302/
Abstract

Microbial ecologists can now start digging into the accumulating mountains of metagenomic data to uncover the occurrence of functional genes and their correlations to microbial community members. Limitations and biases in DNA extraction and sequencing technologies impact sequence distributions, and therefore, have to be considered. However, when comparing metagenomes from widely differing environments, these fluctuations have a relatively minor role in microbial community discrimination. As a consequence, any functional gene or species distribution pattern can be compared among metagenomes originating from various environments and projects. In particular, global comparisons would help to define ecosystem specificities, such as involvement and response to climate change (for example, carbon and nitrogen cycle), human health risks (eg, presence of pathogen species, toxin genes and viruses) and biodegradation capacities. Although not all scientists have easy access to high-throughput sequencing technologies, they do have access to the sequences that have been deposited in databases, and therefore, can begin to intensively mine these metagenomic data to generate hypotheses that can be validated experimentally. Information about metabolic functions and microbial species compositions can already be compared among metagenomes from different ecosystems. These comparisons add to our understanding about microbial adaptation and the role of specific microbes in different ecosystems. Concurrent with the rapid growth of sequencing technologies, we have entered a new age of microbial ecology, which will enable researchers to experimentally confirm putative relationships between microbial functions and community structures.

摘要

微生物生态学家现在可以开始深入挖掘积累的宏基因组数据,以揭示功能基因的存在及其与微生物群落成员的相关性。DNA 提取和测序技术的局限性和偏差会影响序列分布,因此必须加以考虑。然而,在比较来自广泛不同环境的宏基因组时,这些波动在微生物群落区分中相对作用较小。因此,任何功能基因或物种分布模式都可以在来自不同环境和项目的宏基因组之间进行比较。特别是,全球比较将有助于定义生态系统的特异性,例如参与和对气候变化的响应(例如,碳和氮循环)、人类健康风险(例如,病原体物种、毒素基因和病毒的存在)和生物降解能力。尽管并非所有科学家都容易获得高通量测序技术,但他们可以访问已在数据库中存储的序列,因此可以开始深入挖掘这些宏基因组数据,以生成可以通过实验验证的假设。不同生态系统的宏基因组之间已经可以比较代谢功能和微生物物种组成的信息。这些比较增加了我们对微生物适应和特定微生物在不同生态系统中作用的理解。随着测序技术的快速发展,我们已经进入了微生物生态学的新时代,这将使研究人员能够通过实验来验证微生物功能和群落结构之间假定的关系。