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低测序深度会影响微生物群落中共享分类群的分析。

Low sequencing efforts bias analyses of shared taxa in microbial communities.

机构信息

Universidade Federal do Pampa, Campus São Gabriel, Av. Antônio Trilha, 1847, São Gabriel, Rio Grande do Sul, Brazil.

出版信息

Folia Microbiol (Praha). 2012 Sep;57(5):409-13. doi: 10.1007/s12223-012-0155-0. Epub 2012 May 5.

Abstract

The potential for comparing microbial community population structures has been greatly enhanced by developments in next generation sequencing methods that can generate hundreds of thousands to millions of reads in a single run. Conversely, many microbial community comparisons have been published with no more than 1,000 sequences per sample. These studies have presented data on levels of shared operational taxonomic units (OTUs) between communities. Due to lack of coverage, that approach might compromise the conclusions about microbial diversity and the degree of difference between environments. In this study, we present data from recent studies that highlight this problem. Also, we analyzed datasets of 16 rRNA sequences with small and high sequence coverage from different environments to demonstrate that the level of sequencing effort used for analyzing microbial communities biases the results. We randomly sampled pyrosequencing-generated 16S rRNA gene libraries with increasing sequence effort. Sequences were used to calculate Good's coverage, the percentage of shared OTUs, and phylogenetic distance measures. Our data showed that simple counts of presence/absence of taxonomic unities do not reflect the real similarity in membership and structure of the bacterial communities and that community comparisons based on phylogenetic tests provide a way to test statistically significant differences between two or more environments without need an exhaustive sampling effort.

摘要

下一代测序方法的发展极大地提高了比较微生物群落种群结构的潜力,这些方法在一次运行中可以产生数十万到数百万个读取。相反,许多微生物群落比较研究的每个样本的序列数都不超过 1000 个。这些研究提供了关于群落之间共享操作分类单位(OTUs)水平的数据。由于覆盖率不足,这种方法可能会影响关于微生物多样性和环境之间差异程度的结论。在这项研究中,我们展示了来自最近研究的数据,强调了这个问题。此外,我们还分析了来自不同环境的具有小和高序列覆盖率的 16 rRNA 序列数据集,以证明用于分析微生物群落的测序工作水平会产生偏差。我们随机抽取了具有不同测序工作水平的焦磷酸测序生成的 16S rRNA 基因文库进行抽样。使用这些序列来计算 Good's 覆盖率、共享 OTUs 的百分比和系统发育距离度量。我们的数据表明,分类单元存在/不存在的简单计数并不能反映细菌群落成员和结构的真实相似性,并且基于系统发育检验的群落比较为在不需要详尽采样工作的情况下测试两个或更多环境之间的统计学显著差异提供了一种方法。

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