Quantitative Metagenomic Analyses Based on Average Genome Size Normalization
Quantitative Metagenomic Analyses Based on Average Genome Size Normalization
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DOI：
10.1128/aem.02167-10
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发表时间：
2011-04-01
影响因子：
4.4
通讯作者：
Sorensen, Soren J.
中科院分区：
文献类型：
--
作者：
Frank, Jeremy A.;Sorensen, Soren J.
在过去的四分之一个世纪里，微生物学家已经使用DNA序列信息来帮助表征微生物群落。在过去十年中，这已经从单基因扩展到微生物群落基因组学或宏基因组学，其中环境的基因内容不仅可以提供群落成员的普查，还可以提供有关代谢能力和群落成员之间潜在相互作用的直接信息。在这里，我们介绍了一种方法的定量表征和比较微生物群落的基础上，通过估计平均基因组大小的宏基因组数据的标准化。这种标准化可以减轻由不同宏基因组之间的群落结构、测序读段数量和测序读段长度的差异引入的比较偏差。我们证明了这种方法的实用性，通过比较宏基因组从两个不同的海洋来源，使用传统的小亚基（SSU）rRNA基因分析和我们的定量方法来计算每个样本中的基因组的比例，能够特定的代谢性状。与这两种环境，以确定它们组成的每个社区的比例以及环境的差异如何影响它们的丰度，我们描述了三种不同类型的自养生物：好氧，光合碳固定器（蓝细菌）;厌氧，光合碳固定器（Chlorobi）;和厌氧，非光合碳固定器（脱硫菌科）。这些分析展示了基因组比例如何与SSU rRNA基因相对丰度进行比较，以及平均基因组大小和SSU rRNA基因拷贝数等因素如何影响采样概率，从而影响两种类型的群落分析。
Over the past quarter-century, microbiologists have used DNA sequence information to aid in the characterization of microbial communities. During the last decade, this has expanded from single genes to microbial community genomics, or metagenomics, in which the gene content of an environment can provide not just a census of the community members but direct information on metabolic capabilities and potential interactions among community members. Here we introduce a method for the quantitative characterization and comparison of microbial communities based on the normalization of metagenomic data by estimating average genome sizes. This normalization can relieve comparative biases introduced by differences in community structure, number of sequencing reads, and sequencing read lengths between different metagenomes. We demonstrate the utility of this approach by comparing metagenomes from two different marine sources using both conventional small-subunit (SSU) rRNA gene analyses and our quantitative method to calculate the proportion of genomes in each sample that are capable of a particular metabolic trait. With both environments, to determine what proportion of each community they make up and how differences in environment affect their abundances, we characterize three different types of autotrophic organisms: aerobic, photosynthetic carbon fixers (the Cyanobacteria); anaerobic, photosynthetic carbon fixers (the Chlorobi); and anaerobic, nonphotosynthetic carbon fixers (the Desulfobacteraceae). These analyses demonstrate how genome proportionality compares to SSU rRNA gene relative abundance and how factors such as average genome size and SSU rRNA gene copy number affect sampling probability and therefore both types of community analysis.