Comparative metagenomic and rRNA microbial diversity characterization using archaeal and bacterial synthetic communities

Authors

  • Migun Shakya,

    1. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
    2. Genome Science and Technology Program, University of Tennessee, Knoxville, TN, USA
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  • Christopher Quince,

    1. School of Engineering, University of Glasgow, Glasgow, UK
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  • James H. Campbell,

    1. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
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  • Zamin K. Yang,

    1. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
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  • Christopher W. Schadt,

    1. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
    2. Genome Science and Technology Program, University of Tennessee, Knoxville, TN, USA
    3. Department of Microbiology, University of Tennessee, Knoxville, TN, USA
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  • Mircea Podar

    Corresponding author
    1. Genome Science and Technology Program, University of Tennessee, Knoxville, TN, USA
    2. Department of Microbiology, University of Tennessee, Knoxville, TN, USA
    • Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
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For correspondence. E-mail podarm@ornl.gov; Tel. (+1) 865 576 6144; Fax (+1) 865 576 8646.

Summary

Next-generation sequencing has dramatically changed the landscape of microbial ecology, large-scale and in-depth diversity studies being now widely accessible. However, determining the accuracy of taxonomic and quantitative inferences and comparing results obtained with different approaches are complicated by incongruence of experimental and computational data types and also by lack of knowledge of the true ecological diversity. Here we used highly diverse bacterial and archaeal synthetic communities assembled from pure genomic DNAs to compare inferences from metagenomic and SSU rRNA amplicon sequencing. Both Illumina and 454 metagenomic data outperformed amplicon sequencing in quantifying the community composition, but the outcome was dependent on analysis parameters and platform. New approaches in processing and classifying amplicons can reconstruct the taxonomic composition of the community with high reproducibility within primer sets, but all tested primers sets lead to significant taxon-specific biases. Controlled synthetic communities assembled to broadly mimic the phylogenetic richness in target environments can provide important validation for fine-tuning experimental and computational parameters used to characterize natural communities.

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