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16S rRNA gene high-throughput sequencing data mining of microbial diversity and interactions

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Abstract

The ubiquitous occurrence of microorganisms gives rise to continuous public concerns regarding their pathogenicity and threats to human environment, as well as potential engineering benefits in biotechnology. The development and wide application of environmental biotechnology, for example in bioenergy production, wastewater treatment, bioremediation, and drinking water disinfection, have been bringing us with both environmental and economic benefits. Strikingly, extensive applications of microscopic and molecular techniques since 1990s have allowed engineers to peep into the microbiology in “black box” of engineered microbial communities in biotechnological processes, providing guidelines for process design and optimization. Recently, revolutionary advances in DNA sequencing technologies and rapidly decreasing costs are altering conventional ways of microbiology and ecology research, as it launches an era of next-generation sequencing (NGS). The principal research burdens are now transforming from traditional labor-intensive wet-lab experiments to dealing with analysis of huge and informative NGS data, which is computationally expensive and bioinformatically challenging. This study discusses state-of-the-art bioinformatics and statistical analyses of 16S ribosomal RNA (rRNA) gene high-throughput sequencing (HTS) data from prevalent NGS platforms to promote its applications in exploring microbial diversity of functional and pathogenic microorganisms, as well as their interactions in biotechnological processes.

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Acknowledgments

The authors wish to thank the Hong Kong General Research Fund (7195/06E, 7197/08E, 7202/09E, 7198/10E, 7201/11E, 7190/12E, and 172099/14E) for the financial support of this study.

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  1. Environmental Biotechnology Lab, The University of Hong Kong, Pokfulam Road, Hong Kong, China

    Feng Ju &  Tong Zhang

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  1. Feng Ju
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Correspondence to Tong Zhang.

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Ju, F., Zhang, T. 16S rRNA gene high-throughput sequencing data mining of microbial diversity and interactions. Appl Microbiol Biotechnol 99, 4119–4129 (2015). https://doi.org/10.1007/s00253-015-6536-y

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