Optimizing methods and dodging pitfalls in microbiome research

Kim, Dorothy; Hofstaedter, Casey E.; Zhao, Chunyu; Mattei, Lisa M.; Tanes, Ceylan; Clarke, Erik; Lauder, Abigail; Sherrill-Mix, Scott; Chehoud, Christel; Kelsen, Judith R.; Conrad, Máire; Collman, Ronald G.; Baldassano, Robert N.; Bushman, Frederic D.; Bittinger, Kyle

doi:10.1186/s40168-017-0267-5

reviewMicrobiomeMay 5, 2017GOLD OA

Optimizing methods and dodging pitfalls in microbiome research

DKDorothy Kim CECasey E. Hofstaedter CZChunyu Zhao LMLisa M. Mattei CTCeylan Tanes

Children's Hospital of Philadelphia · University of Pennsylvania

PubMed

Indexed incrossrefdoajpubmed

Abstract

Research on the human microbiome has yielded numerous insights into health and disease, but also has resulted in a wealth of experimental artifacts. Here, we present suggestions for optimizing experimental design and avoiding known pitfalls, organized in the typical order in which studies are carried out. We first review best practices in experimental design and introduce common confounders such as age, diet, antibiotic use, pet ownership, longitudinal instability, and microbial sharing during cohousing in animal studies. Typically, samples will need to be stored, so we provide data on best practices for several sample types. We then discuss design and analysis of positive and negative controls, which should…

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563

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FWCI: 26.13
Percentile: 100%
References: 135

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Authors

15

Topics & keywords

Topics

Gut microbiota and health100%

Keywords

Microbiome
Robustness (evolution)
Biology
Data science
Risk analysis (engineering)
Confounding
Set (abstract data type)
Computer science

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