Comparative Metagenomic Analysis of Human Gut Microbiome Composition Using Two Different Bioinformatic Pipelines

Valeria D’Argenio,Vincenza Precone,Giorgio Casaburi,Francesco Salvatore

doi:10.1155/2014/325340

Abstract

Technological advances in next-generation sequencing-based approaches have greatly impacted the analysis of microbial community composition. In particular, 16S rRNA-based methods have been widely used to analyze the whole set of bacteria present in a target environment. As a consequence, several specific bioinformatic pipelines have been developed to manage these data. MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) and Quantitative Insights Into Microbial Ecology (QIIME) are two freely available tools for metagenomic analyses that have been used in a wide range of studies. Here, we report the comparative analysis of the same dataset with both QIIME and MG-RAST in order to evaluate their accuracy in taxonomic assignment and in diversity analysis. We found that taxonomic assignment was more accurate with QIIME which, at family level, assigned a significantly higher number of reads. Thus, QIIME generated a more accurate BIOM file, which in turn improved the diversity analysis output. Finally, although informatics skills are needed to install QIIME, it offers a wide range of metrics that are useful for downstream applications and, not less important, it is not dependent on server times.

Highlights

Microbes play an important role in virtually all ecosystems ranging from those in the sea or the soil [1, 2] to those in human body environments like the skin or the gut [3,4,5]
We successfully carried out the comparative metagenomic analysis of the gut microbiome composition in the same subjects using both MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) and Quantitative Insights Into Microbial Ecology (QIIME) pipelines
Our results showed that the QIIME tool provides a more accurate taxonomic identification which is crucial for the subsequent diversity analysis

Summary

Introduction

Microbes play an important role in virtually all ecosystems ranging from those in the sea or the soil [1, 2] to those in human body environments like the skin or the gut [3,4,5]. The link with human body environments generated many studies of microbial community composition designed to assess its role in various metabolic pathways and to determine whether it is involved in inducing and/or preventing specific pathological conditions. Such investigations could help to clarify the pathogenesis of specific diseases and could lead to novel disease-markers and/or to the development of novel therapeutic strategies. Procedures based on 16S rRNA next-generation sequencing, which allow the high throughput microbial identification within a specific metagenome, represent a powerful means to investigate the composition and the biodiversity of microbial communities [10]. An accurate taxonomic assignment of each microbe in a target environment is required to evaluate the structure, the biodiversity, the richness, and the role of the community resident in a given environment [11, 12]

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