Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics

TLDR

Next‑generation sequencing has revolutionized gut microbiome research, prompting the creation of advanced bioinformatics tools and highlighting the need for clear guidance on designing, executing, and interpreting NGS experiments. The authors sought to determine which analytical approaches—16S rRNA amplicon sequencing or shotgun metagenomics—are most effective for microbiome studies. They conducted a literature review and analyzed their own datasets, illustrating the strengths and weaknesses of 16S and shotgun methods, discussing taxonomic classification techniques, simulating required sequencing depth, and evaluating α‑ and β‑diversity metrics. They demonstrated how to infer bacterial community metabolic capabilities from both 16S and shotgun data.

Abstract

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.