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Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics.

Jovel J, Patterson J, Wang W, Hotte N, O'Keefe S, Mitchel T, Perry T, Kao D, Mason AL, Madsen KL, Wong GK - Front Microbiol (2016)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, University of Alberta Edmonton, AB, Canada.

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.

No MeSH data available.


Related in: MedlinePlus

Comparison of taxonomic analyses of a low complexity artificial microbial population using 16S amplicon or shotgun metagenomic approaches. Eleven bacterial species (representing 7 genera) were cultured under standard laboratory conditions. DNA was extracted using the FastDNA spin kit for feces (MPBio). 16S amplicon and shotgun metagenomics libraries were constructed using the NEXTflex 16S V4 Amplicon-Seq (BioO Scientific) and the Nextera XT (Illumina) kits, respectively. Libraries were paired-end sequenced on a MiSeq sequencer using a 500-cycle kit. For 16S libraries, sequences were trimmed with the “split_fastq_libraries.py” script from QIIME. Default parameters were used, with the exception that the quality threshold for trimming was raised to 30. PCR primer sequences were trimmed with in-house Perl scripts. Shotgun metagenomics libraries were trimmed with the fastqMcf tool, and a quality threshold of 15. The relative abundance of each species was determined with the software indicated at the bottom of the bar graph, using default parameters, at the genus (A) or species (B) levels. The Pearson correlation coefficient between the expected (Input) relative abundance and the classification performed by each program is indicated on top of the bar graph.
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Figure 1: Comparison of taxonomic analyses of a low complexity artificial microbial population using 16S amplicon or shotgun metagenomic approaches. Eleven bacterial species (representing 7 genera) were cultured under standard laboratory conditions. DNA was extracted using the FastDNA spin kit for feces (MPBio). 16S amplicon and shotgun metagenomics libraries were constructed using the NEXTflex 16S V4 Amplicon-Seq (BioO Scientific) and the Nextera XT (Illumina) kits, respectively. Libraries were paired-end sequenced on a MiSeq sequencer using a 500-cycle kit. For 16S libraries, sequences were trimmed with the “split_fastq_libraries.py” script from QIIME. Default parameters were used, with the exception that the quality threshold for trimming was raised to 30. PCR primer sequences were trimmed with in-house Perl scripts. Shotgun metagenomics libraries were trimmed with the fastqMcf tool, and a quality threshold of 15. The relative abundance of each species was determined with the software indicated at the bottom of the bar graph, using default parameters, at the genus (A) or species (B) levels. The Pearson correlation coefficient between the expected (Input) relative abundance and the classification performed by each program is indicated on top of the bar graph.

Mentions: For the analysis of 16S amplicon libraries, we evaluated QIIME (Caporaso et al., 2010; Navas-Molina et al., 2013) and mothur (Schloss et al., 2009), the most widely adopted pipelines, and the MiSeq Reporter v2.5 (MRS; the software developed by Illumina and accompanying the MiSeq instrument) pipeline, all with default parameters. At the genus level, all pipelines produced similar results, but the Pearson correlation coefficient between the expected (input) and obtained relative abundance was somewhat higher for QIIME (Figure 1A). We therefore selected QIIME for our subsequent analyses; however, we do not discourage the use of mothur, which is also a reliable pipeline. None of the 16S pipelines performed satisfactorily at the species level.


Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics.

Jovel J, Patterson J, Wang W, Hotte N, O'Keefe S, Mitchel T, Perry T, Kao D, Mason AL, Madsen KL, Wong GK - Front Microbiol (2016)

Comparison of taxonomic analyses of a low complexity artificial microbial population using 16S amplicon or shotgun metagenomic approaches. Eleven bacterial species (representing 7 genera) were cultured under standard laboratory conditions. DNA was extracted using the FastDNA spin kit for feces (MPBio). 16S amplicon and shotgun metagenomics libraries were constructed using the NEXTflex 16S V4 Amplicon-Seq (BioO Scientific) and the Nextera XT (Illumina) kits, respectively. Libraries were paired-end sequenced on a MiSeq sequencer using a 500-cycle kit. For 16S libraries, sequences were trimmed with the “split_fastq_libraries.py” script from QIIME. Default parameters were used, with the exception that the quality threshold for trimming was raised to 30. PCR primer sequences were trimmed with in-house Perl scripts. Shotgun metagenomics libraries were trimmed with the fastqMcf tool, and a quality threshold of 15. The relative abundance of each species was determined with the software indicated at the bottom of the bar graph, using default parameters, at the genus (A) or species (B) levels. The Pearson correlation coefficient between the expected (Input) relative abundance and the classification performed by each program is indicated on top of the bar graph.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4837688&req=5

Figure 1: Comparison of taxonomic analyses of a low complexity artificial microbial population using 16S amplicon or shotgun metagenomic approaches. Eleven bacterial species (representing 7 genera) were cultured under standard laboratory conditions. DNA was extracted using the FastDNA spin kit for feces (MPBio). 16S amplicon and shotgun metagenomics libraries were constructed using the NEXTflex 16S V4 Amplicon-Seq (BioO Scientific) and the Nextera XT (Illumina) kits, respectively. Libraries were paired-end sequenced on a MiSeq sequencer using a 500-cycle kit. For 16S libraries, sequences were trimmed with the “split_fastq_libraries.py” script from QIIME. Default parameters were used, with the exception that the quality threshold for trimming was raised to 30. PCR primer sequences were trimmed with in-house Perl scripts. Shotgun metagenomics libraries were trimmed with the fastqMcf tool, and a quality threshold of 15. The relative abundance of each species was determined with the software indicated at the bottom of the bar graph, using default parameters, at the genus (A) or species (B) levels. The Pearson correlation coefficient between the expected (Input) relative abundance and the classification performed by each program is indicated on top of the bar graph.
Mentions: For the analysis of 16S amplicon libraries, we evaluated QIIME (Caporaso et al., 2010; Navas-Molina et al., 2013) and mothur (Schloss et al., 2009), the most widely adopted pipelines, and the MiSeq Reporter v2.5 (MRS; the software developed by Illumina and accompanying the MiSeq instrument) pipeline, all with default parameters. At the genus level, all pipelines produced similar results, but the Pearson correlation coefficient between the expected (input) and obtained relative abundance was somewhat higher for QIIME (Figure 1A). We therefore selected QIIME for our subsequent analyses; however, we do not discourage the use of mothur, which is also a reliable pipeline. None of the 16S pipelines performed satisfactorily at the species level.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, University of Alberta Edmonton, AB, Canada.

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.

No MeSH data available.


Related in: MedlinePlus