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A novel method to determine the minimum number of sequences required for reliable microbial community analysis

机译:一种确定可靠微生物群落分析所需的最小序列的新方法

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摘要

Although high-throughput sequencing is an efficient approach to study the structure of microbial communities in detail, it is still impossible to enumerate all individuals using this method. Therefore, it is a common strategy to generate sampling datasets that are representative of the assemblages. However, the representativeness of these sampling datasets has never been assessed. In this study, we developed a method to determine the minimum number sequences that are required to be analyzed to obtain a reliable description of microbial community structure. First, a set of datasets from microbial communities were constructed by in silico sampling at different depths. Second, the correlation equation between dissimilarity of the sampling datasets and sampling depths was fitted, and thereby the minimum number of 16S rRNA gene sequences was predicted. Finally, we verified the method using empirical data of microbiota from a laboratory mesocosm. Our method showed that at least 5,528,079 sequences were required to reliably characterize microbial communities inhabiting the mesocosms. However, if only dominant OTUs ( > 1%) were considered, thousands of sequences were enough. This promising method provides a criterion to ensure sequencing sufficiency when analyzing the structure of natural microbial communities.
机译:虽然高通量测序是一种有效的方法来研究微生物群落结构的细节,但仍然不可能使用这种方法来枚举所有个体。因此,它是生成代表组装的采样数据集的共同策略。然而,从未评估了这些采样数据集的代表性。在本研究中,我们开发了一种确定要分析所需的最小数量序列以获得微生物群落结构的可靠描述的方法。首先,通过在不同深度的硅采样中构建来自微生物群落的一组数据集。其次,装配采样数据集和采样深度的不相似性之间的相关方程,从而预测了16S rRNA基因序列的最小数量。最后,我们验证了使用实验室中科姆的微生物群的经验数据的方法。我们的方法表明,至少需要5,528,079个序列以可靠地表征居住的微生物群落居住的胚源。但是,如果考虑过占优势Otus(> 1%),则足够了数千次序列。该承诺的方法提供了一种标准,以确保在分析自然微生物群落的结构时进行排序。

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