An introduction to the analysis of shotgun metagenomic data.

Thomas J Sharpton

doi:10.3389/fpls.2014.00209

Abstract

Environmental DNA sequencing has revealed the expansive biodiversity of microorganisms and clarified the relationship between host-associated microbial communities and host phenotype. Shotgun metagenomic DNA sequencing is a relatively new and powerful environmental sequencing approach that provides insight into community biodiversity and function. But, the analysis of metagenomic sequences is complicated due to the complex structure of the data. Fortunately, new tools and data resources have been developed to circumvent these complexities and allow researchers to determine which microbes are present in the community and what they might be doing. This review describes the analytical strategies and specific tools that can be applied to metagenomic data and the considerations and caveats associated with their use. Specifically, it documents how metagenomes can be analyzed to quantify community structure and diversity, assemble novel genomes, identify new taxa and genes, and determine which metabolic pathways are encoded in the community. It also discusses several methods that can be used compare metagenomes to identify taxa and functions that differentiate communities.

Highlights

In the case that the gene has a functional annotation, this method of gene prediction can simultaneously provide a functional annotation for the recruited metagenomic sequences (Desai et al, 2013). This procedure has been used to quantify the genetic diversity of marine communities (Rusch et al, 2007) and gut microbiota (Qin et al, 2010; Human Microbiome Project Consortium, 2012a), and is generally useful for cataloging the specific genes present in the metagenome
Researchers interested in analyzing metagenomes to characterize microbial community diversity and function have a litany of tools and data resources at their disposal
Many of the tools discussed here were developed for researchers comfortable interfacing with a command-line environment. This is understandable given the complexity of metagenomic data and the computational requirements traditionally associated with its analysis

Summary

INTRODUCTION

In the case that the gene has a functional annotation, this method of gene prediction can simultaneously provide a functional annotation for the recruited metagenomic sequences (Desai et al, 2013) This procedure has been used to quantify the genetic diversity of marine communities (Rusch et al, 2007) and gut microbiota (Qin et al, 2010; Human Microbiome Project Consortium, 2012a), and is generally useful for cataloging the specific genes present in the metagenome. This is generally a high-throughput gene prediction procedure because it tends to rely on read mapping algorithms that rapidly assess whether a genomic fragment is nearly identical to a database sequence. Novel strategies for functionally annotating metagenomes and improvements in the way predicted metagenomic proteins are integrated into protein family databases are needed

CONCLUSION

Methods