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BioMed Research International
Volume 2014 (2014), Article ID 325340, 10 pages
Research Article

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

1CEINGE-Biotecnologie Avanzate, Via G. Salvatore, 80145 Naples, Italy
2Department of Molecular Medicine and Medical Biotechnologies, University of Naples Federico II, Via S. Pansini, 80131 Naples, Italy
3IRCCS-Fondazione SDN, Via Gianturco, 80143 Naples, Italy

Received 5 October 2013; Accepted 30 December 2013; Published 25 February 2014

Academic Editor: Qaisar Mahmood

Copyright © 2014 Valeria D’Argenio et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.