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Bioinformatics analysis of metagenomics data of biogas-producing microbial communities in anaerobic digesters: A review

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  • Zhang, Le
  • Loh, Kai-Chee
  • Lim, Jun Wei
  • Zhang, Jingxin

Abstract

Complex microbial communities in anaerobic digestion (AD) system play a vital role in the production of biogas. An in-depth understanding of the microbial compositions, diversity/similarity, metabolic networks, functional gene patterns, and relations between biodiversity and system functions at the genome level could help to optimize microbial productivity and contribute to enhancement of AD process. The study of microbial communities has been revolutionized in recent years with the development of high-throughput sequencing technologies. Analysis of high-throughput sequencing data and a suitable bioinformatics analysis approach therefore plays a very critical role in the investigation of microbial metagenome. The present article reviews the overall procedure of processing metagenomics data of microbial communities for revealing metagenomics characterization using bioinformatics approaches. This includes (1) introduction of application case summary, (2) DNA extraction and high-throughput pyrosequencing, (3) processing metagenomics data using function-based bioinformatics platforms and tools, and (4) several specific bioinformatics analysis of anaerobic microbial communities. Key findings on anaerobic digestion via bioinformatics analysis are summarized. Limitations and future potential of bioinformatics approaches for analysis of metagenomics information of microbial communities are also discussed, with the hope of promoting its further development. Finally, a big-data-based precision fermentation platform using artificial neural network is proposed for integrating the bioinformatics data of microbial communities with performance of anaerobic digesters to facilitate the usage of huge metagenomics data.

Suggested Citation

  • Zhang, Le & Loh, Kai-Chee & Lim, Jun Wei & Zhang, Jingxin, 2019. "Bioinformatics analysis of metagenomics data of biogas-producing microbial communities in anaerobic digesters: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 110-126.
    • Handle: RePEc:eee:rensus:v:100:y:2019:i:c:p:110-126
    DOI: 10.1016/j.rser.2018.10.021
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    References listed on IDEAS

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    1. Zhang, Jingxin & Loh, Kai-Chee & Li, Wangliang & Lim, Jun Wei & Dai, Yanjun & Tong, Yen Wah, 2017. "Three-stage anaerobic digester for food waste," Applied Energy, Elsevier, vol. 194(C), pages 287-295.
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    3. Sakiewicz, P. & Piotrowski, K. & Ober, J. & Karwot, J., 2020. "Innovative artificial neural network approach for integrated biogas – wastewater treatment system modelling: Effect of plant operating parameters on process intensification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    4. Stephen Tangwe & Patrick Mukumba & Golden Makaka, 2022. "Comparison of the Prediction Accuracy of Total Viable Bacteria Counts in a Batch Balloon Digester Charged with Cow Manure: Multiple Linear Regression and Non-Linear Regression Models," Energies, MDPI, vol. 15(19), pages 1-23, October.
    5. Susanne Theuerl & Christiane Herrmann & Monika Heiermann & Philipp Grundmann & Niels Landwehr & Ulrich Kreidenweis & Annette Prochnow, 2019. "The Future Agricultural Biogas Plant in Germany: A Vision," Energies, MDPI, vol. 12(3), pages 1-32, January.
    6. Susanne Theuerl & Johanna Klang & Annette Prochnow, 2019. "Process Disturbances in Agricultural Biogas Production—Causes, Mechanisms and Effects on the Biogas Microbiome: A Review," Energies, MDPI, vol. 12(3), pages 1-20, January.

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