Abstract
Diverse microbial communities of bacteria, archaea, viruses and single-celled eukaryotes have crucial roles in the environment and in human health. However, microbes are frequently difficult to culture in the laboratory, which can confound cataloging of members and understanding of how communities function. High-throughput sequencing technologies and a suite of computational pipelines have been combined into shotgun metagenomics methods that have transformed microbiology. Still, computational approaches to overcome the challenges that affect both assembly-based and mapping-based metagenomic profiling, particularly of high-complexity samples or environments containing organisms with limited similarity to sequenced genomes, are needed. Understanding the functions and characterizing specific strains of these communities offers biotechnological promise in therapeutic discovery and innovative ways to synthesize products using microbial factories and can pinpoint the contributions of microorganisms to planetary, animal and human health.
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Change history
12 September 2017
In the version of this article initially published, the Competing Financial Interests should have indicated the authors had competing interests, but instead indicated there were none. The detailed statement was missing from the HTML: J.T.S. receives research funding from Oxford Nanopore Technologies and has received travel and accommodations to speak at meetings hosted by Oxford Nanopore Technologies. N.J.L. has received honoraria to speak at Oxford Nanopore and Illumina meetings, and travel and accommodation to attend company-sponsored meetings. N.J.L. has ongoing research collaborations with Oxford Nanopore who have provided free-of-charge sequencing reagents as part of the MinION Access Programme and directly in support of research projects. In addition, the publication date was given as 11 September, rather than 12 September 2017. The errors have been corrected for the PDF and HTML versions of this article.
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Acknowledgements
A.W.W. and the Rowett Institute receive core funding support from the Scottish Government's Rural and Environmental Science and Analysis Service (RESAS). N.S. is supported by the European Research Council (ERC-STG project MetaPG), a European Union Framework Program 7 Marie-Curie grant (PCIG13-618833), a MIUR grant (FIR RBFR13EWWI), a Fondazione Caritro grant (Rif.Int.2013.0239) and a Terme di Comano grant. C.Q. and N.J.L. are funded through a MRC bioinformatics fellowship (MR/M50161X/1) as part of the MRC Cloud Infrastructure for Microbial Bioinformatics (CLIMB) consortium (MR/L015080/1). J.T.S. is supported by the Ontario Institute for Cancer Research through funding provided by the Government of Ontario.
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C.Q., A.W.W., J.T.S., N.J.L. and N.S. drafted the paper, revised the text and designed figures, tables and boxes. C.Q. and N.S. performed the metagenomic analyses described in the manuscript.
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J.T.S. receives research funding from Oxford Nanopore Technologies and has received travel and accommodations to speak at meetings hosted by Oxford Nanopore Technologies. N.J.L. has received honoraria to speak at Oxford Nanopore and Illumina meetings, and travel and accommodation to attend company-sponsored meetings. N.J.L. has ongoing research collaborations with Oxford Nanopore who have provided free-of-charge sequencing reagents as part of the MinION Access Programme and directly in support of research projects.
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Supplementary Figure 1 Example workflow for planning a metagenomics study
The advice presented here is targeted towards entry-level researchers in this area, with a particular focus on hypothesis-driven experiments, which of course may be designed very differently compared to exploratory/hypothesis-generating studies. Key considerations for study design (blue box), sample collection (green box) and experimental procedures (yellow box) are highlighted. Understanding the potential for confounding factors, and optimization of design, can substantially improve the quality of both metagenomic sequence data, and interpretation. Supplementary Table 1 contains further specific recommendations.
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Supplementary Code 1
Supporting scripts and pipeline description. (ZIP 91 kb)
Supplementary Box 1
Problems and solutions for study and design. (PDF 287 kb)
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Quince, C., Walker, A., Simpson, J. et al. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol 35, 833–844 (2017). https://doi.org/10.1038/nbt.3935
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