What Can We Learn about the Bias of Microbiome Studies from Analyzing Data from Mock Communities?

Li, Mo; Tyx, Robert E; Rivera, Angel J; Zhao, Ni; Satten, Glen A

Li, Mo; Tyx, Robert E; Rivera, Angel J; Zhao, Ni; Satten, Glen A.

Afiliación

Li M; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
Tyx RE; Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA.
Rivera AJ; Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA.
Zhao N; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
Satten GA; Department of Gynecology and Obstetrics, School of Medicine, Emory University, Atlanta, GA 30322, USA.

Genes (Basel) ; 13(10)2022 Sep 28.

Article en En | MEDLINE | ID: mdl-36292643

Asunto(s)

Microbiota; ARN Ribosómico 16S/genética; Microbiota/genética; Metagenómica/métodos; Bacterias/genética; Sesgo

Palabras clave

16s rRNA sequencing; biased measurement; log-linear model; mock community

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Microbiota Idioma: En Revista: Genes (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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