Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics

The advent of next generation sequencing (NGS) has enabled investigations of the gut microbiome with unprecedented resolution and throughput. This has stimulated the development of sophisticated bioinformatics tools to analyze the massive amounts of data generated. Researchers therefore need a clear...

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Bibliographic Details
Published in:	Frontiers in microbiology Vol. 7; p. 459
Main Authors:	Jovel, Juan, Patterson, Jordan, Wang, Weiwei, Hotte, Naomi, O'Keefe, Sandra, Mitchel, Troy, Perry, Troy, Kao, Dina, Mason, Andrew L, Madsen, Karen L, Wong, Gane K-S
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 20-04-2016
Subjects:	16S rRNA gene sequencing bioinformatics Diversity analysis Functional Profiling gut microbiome Microbiology Shotgun metagenomics taxonomic classification gut microbiome diversity analysis bioinformatics 16S rRNA gene sequencing shotgun metagenomics functional profiling
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Summary:	The advent of next generation sequencing (NGS) has enabled investigations of the gut microbiome with unprecedented resolution and throughput. This has stimulated the development of sophisticated bioinformatics tools to analyze the massive amounts of data generated. Researchers therefore need a clear understanding of the key concepts required for the design, execution and interpretation of NGS experiments on microbiomes. We conducted a literature review and used our own data to determine which approaches work best. The two main approaches for analyzing the microbiome, 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics, are illustrated with analyses of libraries designed to highlight their strengths and weaknesses. Several methods for taxonomic classification of bacterial sequences are discussed. We present simulations to assess the number of sequences that are required to perform reliable appraisals of bacterial community structure. To the extent that fluctuations in the diversity of gut bacterial populations correlate with health and disease, we emphasize various techniques for the analysis of bacterial communities within samples (α-diversity) and between samples (β-diversity). Finally, we demonstrate techniques to infer the metabolic capabilities of a bacteria community from these 16S and shotgun data.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Ranjit Kumar, University of Alabama at Birmingham, USA; Henning Seedorf, Temasek Life Sciences Laboratory, Singapore Edited by: Martha E. Trujillo, Universidad de Salamanca, Spain These authors have contributed equally to this work. This article was submitted to Evolutionary and Genomic Microbiology, a section of the journal Frontiers in Microbiology
ISSN:	1664-302X 1664-302X
DOI:	10.3389/fmicb.2016.00459