Processing a 16S rRNA Sequencing Dataset with the Microbiome Helper Workflow

Gavin M Douglas; André M Comeau; Morgan G I Langille

doi:10.1007/978-1-4939-8728-3_9

Processing a 16S rRNA Sequencing Dataset with the Microbiome Helper Workflow

Methods Mol Biol. 2018:1849:131-141. doi: 10.1007/978-1-4939-8728-3_9.

Authors

Gavin M Douglas¹, André M Comeau², Morgan G I Langille^{3

4

5}

Affiliations

¹ Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.
² Integrated Microbiome Resource, Dalhousie University, Halifax, NS, Canada.
³ Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada. morgan.g.i.langille@dal.ca.
⁴ Integrated Microbiome Resource, Dalhousie University, Halifax, NS, Canada. morgan.g.i.langille@dal.ca.
⁵ Department of Pharmacology, Dalhousie University, Halifax, NS, Canada. morgan.g.i.langille@dal.ca.

PMID: 30298252
DOI: 10.1007/978-1-4939-8728-3_9

Abstract

Sequencing microbiome samples has recently become a fast and cost-effective method to taxonomically profile communities. The growing interest in analyzing microbial sequencing data has attracted many new researchers to the field. Here, we present a straightforward bioinformatic pipeline that aims to streamline the processing of 16S rRNA sequencing data. This workflow is part of the larger project called Microbiome Helper (Comeau et al. mSyst 2:e00127-16, 2017), which includes other bioinformatic workflows, tutorials, and scripts available here: https://github.com/mlangill/microbiome_helper/wiki .

Keywords: 16S rRNA gene; Amplicon sequencing; Microbiome Helper; Standard operating procedure; Tutorial; VirtualBox image.

MeSH terms

Bacteria / classification
Bacteria / genetics*
Bacteria / isolation & purification
Computational Biology / methods*
High-Throughput Nucleotide Sequencing / methods*
Metagenome*
Microbiota*
Phylogeny
RNA, Ribosomal, 16S / genetics*
Software*
Workflow

Substances

RNA, Ribosomal, 16S