Advancing Our Understanding of the Human Microbiome Using QIIME

Abstract

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 chapter, 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.

Introduction

Advances in DNA sequencing technologies, together with the availability of culture-independent sequencing methods and software for analyzing the massive quantities of data resulting from these technologies, have vastly improved our ability to characterize microbial communities in many diverse environments. The human microbiota, the collection of microbes living in or on the human body, is of considerable interest: microbial cells outnumber human cells in our bodies by a ratio of up to 10 to 1 (Savage, 1977). These microbial communities contribute to healthy human physiology (De Filippo et al., 2010, Dethlefsen and Relman, 2011, Spencer et al., 2011) and development (Dominguez-Bello et al., 2010, Koenig et al., 2011), and dysbiosis (or imbalance in these communities) is now known to be associated with disease, including obesity (Turnbaugh et al., 2009) and Crohn’s disease (Eckburg & Relman, 2007). More recently, evidence from transplants into germ-free mice suggests that some of these associations may be causal, because certain phenotypes can be transmitted by transmitting the microbiota (Carvalho et al., 2012, McLean et al., 2012, Turnbaugh et al., 2009), even including transmission of human phenotypes into mice (Diaz Heijtz et al., 2011, Koren et al., 2012, Smith et al., 2013).

Illumina’s MiSeq and HiSeq DNA sequencing instruments, respectively, sequence tens of millions, or billions, of DNA fragments in a single sequencing run (Kuczynski et al., 2012). The rapidly increasing data volumes typical of recent studies drive a need for more efficient and scalable tools to study the human microbiome (Gonzalez & Knight, 2012). QIIME (Quantitative Insights Into Microbial Ecology) (Caporaso, Kuczynski, et al., 2010) is an open-source pipeline designed to provide self-contained microbial community analyses, from interacting with raw sequence data through publication-quality statistical analyses and visualizations.

QIIME integrates commonly used third-party tools and implements many diversity metrics, statistical methods, and visualization tools for analyzing microbial data. Consequently, most individual steps in the microbial community analysis can be performed in multiple ways. Here, we describe how samples are prepared for an Illumina MiSeq run, the QIIME pipeline, and our view of the current best practices for analyzing microbial communities with QIIME. Although there are other pipelines available, including mothur (Schloss et al., 2009), the RDP tools (Olsen et al., 1991, Olsen et al., 1992), ARB (Ludwig et al., 2004), VAMPS (Sogin, Welch, & Huse, 2009), and other platforms, in this review, we focus on analysis with the MiSeq platform and QIIME as this combination is increasingly popular as a method for analyzing microbial communities and a detailed comparison of other available pipelines and sequencing platforms is beyond the scope of the present work.