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16S rRNA Gene Analysis with QIIME2

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Book cover Microbiome Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1849))

Abstract

Microbial marker-gene sequence data can be used to generate comprehensive taxonomic profiles of the microorganisms present in a given community and for other community diversity analyses. The process of going from raw gene sequences to taxonomic profiles or diversity measures involves a series of data transformations performed by numerous computational tools. This includes tools for sequence quality checking, denoising, taxonomic classification, alignment, and phylogenetic tree building. In this chapter, we demonstrate how the Quantitative Insights Into Microbial Ecology version 2 (QIIME2) software suite can simplify 16S rRNA marker-gene analysis. We walk through an example data set extracted from the guts of bumblebees in order to show how QIIME2 can transform raw sequences into taxonomic bar plots, phylogenetic trees, principal co-ordinates analyses, and other visualizations of microbial diversity.

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Authors and Affiliations

  1. Dalhousie University, Halifax, Nova Scotia, Canada

    Michael Hall & Robert G. Beiko

Authors
  1. Michael Hall
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  2. Robert G. Beiko
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Corresponding author

Correspondence to Michael Hall .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada

    Robert G. Beiko

  2. Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada

    Will Hsiao

  3. Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, Canada

    John Parkinson

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Hall, M., Beiko, R.G. (2018). 16S rRNA Gene Analysis with QIIME2. In: Beiko, R., Hsiao, W., Parkinson, J. (eds) Microbiome Analysis. Methods in Molecular Biology, vol 1849. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8728-3_8

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  • DOI: https://doi.org/10.1007/978-1-4939-8728-3_8

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8726-9

  • Online ISBN: 978-1-4939-8728-3

  • eBook Packages: Springer Protocols

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