Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
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
_version_ 1782427892754415616
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
work_keys_str_mv AT joveljuan characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics
AT pattersonjordan characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics
AT wangweiwei characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics
AT hottenaomi characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics
AT okeefesandra characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics
AT mitcheltroy characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics
AT perrytroy characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics
AT kaodina characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics
AT masonandrewl characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics
AT madsenkarenl characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics
AT wongganeks characterizationofthegutmicrobiomeusing16sorshotgunmetagenomics