生物
微生物群
采样(信号处理)
微生物生态学
肺
医学微生物学
放大器
细菌
微生物学
计算生物学
病理
聚合酶链反应
生物信息学
遗传学
基因
医学
内科学
计算机科学
滤波器(信号处理)
计算机视觉
作者
Jennifer M. Baker,Kevin J. Hinkle,Roderick A. McDonald,Christopher A. Brown,Nicole R. Falkowski,Gary B. Huffnagle,Robert P. Dickson
出处
期刊:Microbiome
[BioMed Central]
日期:2021-05-05
卷期号:9 (1)
被引量:44
标识
DOI:10.1186/s40168-021-01055-4
摘要
Low-biomass microbiome studies (such as those of the lungs, placenta, and skin) are vulnerable to contamination and sequencing stochasticity, which obscure legitimate microbial signal. While human lung microbiome studies have rigorously identified sampling strategies that reliably capture microbial signal from these low-biomass microbial communities, the optimal sampling strategy for characterizing murine lung microbiota has not been empirically determined. Performing accurate, reliable characterization of murine lung microbiota and distinguishing true microbial signal from noise in these samples will be critical for further mechanistic microbiome studies in mice.
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