Environmental Microbial Genomics, Laboratoire Ampère, Ecole Centrale de Lyon, Université de Lyon, Ecully, France.
ISME J. 2011 Dec;5(12):1837-43. doi: 10.1038/ismej.2011.61. Epub 2011 May 19.
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.
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。