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扩增子测序深度对环境微生物组研究的影响。

Effect of Amplicon Sequencing Depth in Environmental Microbiome Research.

作者信息

Ramakodi Meganathan P

机构信息

Hyderabad Zonal Centre, CSIR-National Environmental Engineering Research Institute (NEERI), IICT Campus, Tarnaka, Hyderabad, Telangana, 500007, India.

出版信息

Curr Microbiol. 2021 Mar;78(3):1026-1033. doi: 10.1007/s00284-021-02345-8. Epub 2021 Feb 3.

DOI:10.1007/s00284-021-02345-8
PMID:33537885
Abstract

Amplicon sequencing approach is commonly employed in microbiome studies and sequencing depth is considered as a major factor influencing the outcome of data analyses. As of now, the effect of amplicon sequencing depth in environmental microbiome analyses is not explicitly illustrated. In this study, microbiome data of nine aquatic samples from Sundarbans mangrove region, obtained from SRA, were analyzed to explain the influence of sequencing depth variation in environmental microbiome data analyses. Briefly, four groups based on number of reads (NOR) were created comprising of, total NOR, 75 k, 50 k and 25 k, followed by data analyses. The results showed that the observed ASVs among four groups were significantly different (P value 1.094e-06). The Bray-Curtis dissimilarity analysis showed differences in microbiome composition and also, each group exhibited slightly different core-microbiome structure. Importantly, the variation in sequencing depth was found to affect the predictions of environmental drivers associated with microbiome composition. Thus, this study emphasizes that the microbiome data are compositional and the NOR in the data could affect the microbial composition. In summary, this study demonstrates the consequences of sequencing depth variation on microbiome data analyses and suggests the researchers to take proper cautions to avoid misleading results due to sequencing depth variation.

摘要

扩增子测序方法常用于微生物组研究,测序深度被视为影响数据分析结果的主要因素。截至目前,扩增子测序深度在环境微生物组分析中的作用尚未得到明确阐述。在本研究中,对从SRA获得的来自孙德尔本斯红树林地区的9个水生样本的微生物组数据进行了分析,以解释测序深度变化在环境微生物组数据分析中的影响。简而言之,根据读数数量(NOR)创建了四组,包括总NOR、75k、50k和25k,随后进行数据分析。结果表明,四组之间观察到的扩增子序列变异(ASVs)存在显著差异(P值为1.094e - 06)。Bray - Curtis差异分析表明微生物组组成存在差异,并且每组都表现出略有不同的核心微生物组结构。重要的是,发现测序深度的变化会影响与微生物组组成相关的环境驱动因素的预测。因此,本研究强调微生物组数据具有组成性,数据中的NOR可能会影响微生物组成。总之,本研究证明了测序深度变化对微生物组数据分析的影响,并建议研究人员采取适当的谨慎措施,以避免由于测序深度变化而产生误导性结果。

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