The Madness of Microbiome: Attempting To Find Consensus "Best Practice" for 16S Microbiome Studies

doi:10.1128/AEM.02627-17

Review

. 2018 Mar 19;84(7):e02627-17.

doi: 10.1128/AEM.02627-17. Print 2018 Apr 1.

The Madness of Microbiome: Attempting To Find Consensus "Best Practice" for 16S Microbiome Studies

Jolinda Pollock^#^{1

2}, Laura Glendinning^#², Trong Wisedchanwet², Mick Watson²

Affiliations

¹ Animal and Veterinary Sciences, Scotland's Rural College (SRUC), Edinburgh, United Kingdom jolinda.pollock@sruc.ac.uk.
² The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.

^# Contributed equally.

PMID: 29427429
PMCID: PMC5861821
DOI: 10.1128/AEM.02627-17

Review

The Madness of Microbiome: Attempting To Find Consensus "Best Practice" for 16S Microbiome Studies

Jolinda Pollock et al. Appl Environ Microbiol. 2018.

. 2018 Mar 19;84(7):e02627-17.

doi: 10.1128/AEM.02627-17. Print 2018 Apr 1.

Authors

Jolinda Pollock^#^{1

2}, Laura Glendinning^#², Trong Wisedchanwet², Mick Watson²

Affiliations

¹ Animal and Veterinary Sciences, Scotland's Rural College (SRUC), Edinburgh, United Kingdom jolinda.pollock@sruc.ac.uk.
² The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.

^# Contributed equally.

PMID: 29427429
PMCID: PMC5861821
DOI: 10.1128/AEM.02627-17

Abstract

The development and continuous improvement of high-throughput sequencing platforms have stimulated interest in the study of complex microbial communities. Currently, the most popular sequencing approach to study microbial community composition and dynamics is targeted 16S rRNA gene metabarcoding. To prepare samples for sequencing, there are a variety of processing steps, each with the potential to introduce bias at the data analysis stage. In this short review, key information from the literature pertaining to each processing step is described, and consequently, general recommendations for future 16S rRNA gene metabarcoding experiments are made.

Keywords: 16S RNA; DNA sequencing; microbiome.

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References

1. Hong PY, Croix JA, Greenberg E, Gaskins HR, Mackie RI. 2011. Pyrosequencing-based analysis of the mucosal microbiota in healthy individuals reveals ubiquitous bacterial groups and micro-heterogeneity. PLoS One 6:e25042. doi:10.1371/journal.pone.0025042. - DOI - PMC - PubMed
1. Glendinning L, Wright S, Pollock J, Tennant P, Collie D, McLachlan G. 2016. Variability of the sheep lung microbiota. Appl Environ Microbiol 82:3225–3238. doi:10.1128/AEM.00540-16. - DOI - PMC - PubMed
1. Mendes LW, Tsai SM. 2014. Variations of bacterial community structure and composition in mangrove sediment at different depths in Southeastern Brazil. Diversity 6:827–843. doi:10.3390/d6040827. - DOI
1. Steven B, Gallegos-Graves LV, Belnap J, Kuske CR. 2013. Dryland soil microbial communities display spatial biogeographic patterns associated with soil depth and soil parent material. FEMS Microbiol Ecol 86:101–113. doi:10.1111/1574-6941.12143. - DOI - PubMed
1. Flores GE, Caporaso JG, Henley JB, Rideout JR, Domogala D, Chase J, Leff JW, Vázquez-Baeza Y, Gonzalez A, Knight R, Dunn RR, Fierer N. 2014. Temporal variability is a personalized feature of the human microbiome. Genome Biol 15:531. doi:10.1186/s13059-014-0531-y. - DOI - PMC - PubMed

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[1] Hong PY, Croix JA, Greenberg E, Gaskins HR, Mackie RI. 2011. Pyrosequencing-based analysis of the mucosal microbiota in healthy individuals reveals ubiquitous bacterial groups and micro-heterogeneity. PLoS One 6:e25042. doi:10.1371/journal.pone.0025042. - DOI - PMC - PubMed

[2] Hong PY, Croix JA, Greenberg E, Gaskins HR, Mackie RI. 2011. Pyrosequencing-based analysis of the mucosal microbiota in healthy individuals reveals ubiquitous bacterial groups and micro-heterogeneity. PLoS One 6:e25042. doi:10.1371/journal.pone.0025042. - DOI - PMC - PubMed

[3] Glendinning L, Wright S, Pollock J, Tennant P, Collie D, McLachlan G. 2016. Variability of the sheep lung microbiota. Appl Environ Microbiol 82:3225–3238. doi:10.1128/AEM.00540-16. - DOI - PMC - PubMed

[4] Glendinning L, Wright S, Pollock J, Tennant P, Collie D, McLachlan G. 2016. Variability of the sheep lung microbiota. Appl Environ Microbiol 82:3225–3238. doi:10.1128/AEM.00540-16. - DOI - PMC - PubMed

[5] Mendes LW, Tsai SM. 2014. Variations of bacterial community structure and composition in mangrove sediment at different depths in Southeastern Brazil. Diversity 6:827–843. doi:10.3390/d6040827. - DOI

[6] Mendes LW, Tsai SM. 2014. Variations of bacterial community structure and composition in mangrove sediment at different depths in Southeastern Brazil. Diversity 6:827–843. doi:10.3390/d6040827. - DOI

[7] Steven B, Gallegos-Graves LV, Belnap J, Kuske CR. 2013. Dryland soil microbial communities display spatial biogeographic patterns associated with soil depth and soil parent material. FEMS Microbiol Ecol 86:101–113. doi:10.1111/1574-6941.12143. - DOI - PubMed

[8] Steven B, Gallegos-Graves LV, Belnap J, Kuske CR. 2013. Dryland soil microbial communities display spatial biogeographic patterns associated with soil depth and soil parent material. FEMS Microbiol Ecol 86:101–113. doi:10.1111/1574-6941.12143. - DOI - PubMed

[9] Flores GE, Caporaso JG, Henley JB, Rideout JR, Domogala D, Chase J, Leff JW, Vázquez-Baeza Y, Gonzalez A, Knight R, Dunn RR, Fierer N. 2014. Temporal variability is a personalized feature of the human microbiome. Genome Biol 15:531. doi:10.1186/s13059-014-0531-y. - DOI - PMC - PubMed

[10] Flores GE, Caporaso JG, Henley JB, Rideout JR, Domogala D, Chase J, Leff JW, Vázquez-Baeza Y, Gonzalez A, Knight R, Dunn RR, Fierer N. 2014. Temporal variability is a personalized feature of the human microbiome. Genome Biol 15:531. doi:10.1186/s13059-014-0531-y. - DOI - PMC - PubMed

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The Madness of Microbiome: Attempting To Find Consensus "Best Practice" for 16S Microbiome Studies

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The Madness of Microbiome: Attempting To Find Consensus "Best Practice" for 16S Microbiome Studies

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