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

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

Renewable and Sustainable Energy Reviews, 2019, vol. 100, issue C, 110-126

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.

Keywords: Anaerobic digestion; Microbial communities; Bioinformatics; Metagenomics; Pyrosequencing; Artificial neural network (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.rser.2018.10.021

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