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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4837688/ https://www.ncbi.nlm.nih.gov/pubmed/27148170 http://dx.doi.org/10.3389/fmicb.2016.00459 |
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author | 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. |
author_facet | 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. |
author_sort | Jovel, Juan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4837688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-48376882016-05-04 Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics 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. Front Microbiol Microbiology 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. Frontiers Media S.A. 2016-04-20 /pmc/articles/PMC4837688/ /pubmed/27148170 http://dx.doi.org/10.3389/fmicb.2016.00459 Text en Copyright © 2016 Jovel, Patterson, Wang, Hotte, O'Keefe, Mitchel, Perry, Kao, Mason, Madsen and Wong. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology 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. Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics |
title | Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics |
title_full | Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics |
title_fullStr | Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics |
title_full_unstemmed | Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics |
title_short | Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics |
title_sort | characterization of the gut microbiome using 16s or shotgun metagenomics |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4837688/ https://www.ncbi.nlm.nih.gov/pubmed/27148170 http://dx.doi.org/10.3389/fmicb.2016.00459 |
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