Advancing our understanding of the human microbiome using QIIME

By Jose A Navas-Molina, J. Peralta‐Sánchez, Antonio Gonzalez, Paul J. McMurdie, Yoshiki Vazquez-Baeza, Zhenjiang Xu, L. Ursell, C. Lauber, Hongwei Zhou, S. Song and 6 more. Methods in Enzymology, 2013

544 citations recorded by the source corpus.

High-throughput DNA sequencing technologies, coupled with advanced bioinformatics tools, have enabled rapid advances in microbial ecology and our understanding of the human microbiome. QIIME (Quantitative Insights Into Microbial Ecology) is an open-source bioinformatics software package designed for microbial community analysis based on DNA sequence data, which provides a single analysis framework for analysis of raw sequence data through publication quality statistical analyses and interactive visualizations. In this paper, we demonstrate the use of the QIIME pipeline to analyze microbial communities obtained from several sites on the bodies of transgenic and wild-type mice, as assessed using 16S rRNA gene sequences generated on the Illumina MiSeq platform. We present our recommended pipeline for performing microbial community analysis, and provide guidelines for making critical choices in the process. We present examples of some of the types of analyses that are enabled by QIIME, and discuss how other tools, such as phyloseq and R, can be applied to expand upon these analyses.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3df1fa0df08efb0da6d0c64aadb9ff20c6f28024

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Citations in the corpus

Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences (2014, background): “These are canonically described as de novo, closed-reference, and open-reference OTU picking (Navas-Molina et al., 2013).”
Gut bacteria from multiple sclerosis patients modulate human T cells and exacerbate symptoms in mouse models (2017, background): “distasonis has also been shown to induce Treg differentiation in GF mice (2).”
Gut Microbes and the Brain: Paradigm Shift in Neuroscience (2014, background): “Of particular importance are tools to integrate large, highly multivariate datasets (Gonzalez and Knight, 2012; Navas-Molina et al., 2013).”
A microbial signature for Crohn's disease (2017, methodology): “To work with normalised data, we analysed an equal number of sequences from all groups.”
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible (2013, methodology): “Counts are divided by total library size.”
Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics (2016, methodology): “The selection of each metric will depend on the hypothesis being evaluated as some phenotypes are more strongly influenced by relative abundance of taxa rather than presence or absence of specific taxa (Navas-Molina et al., 2013).”