Journal
FERMENTATION-BASEL
Volume 9, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/fermentation9010062
Keywords
microbial metagenomics; anaerobic digestion; bioinformatics techniques; metabolomics
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Biogas production involves complex microbial populations in anaerobic digesters. Understanding these microbial populations through metagenomics analysis can help optimize and enhance anaerobic digestion processes. With advancements in metagenomic sequencing technologies, such as next generation sequencing and high-throughput sequencing, the study of microbial dynamics in anaerobic digestion has been revolutionized. This review provides an overview of the basic processes of metagenomics research, and discusses various bioinformatics approaches for analyzing microbial communities and characterizing metagenomes.
Biogas production involves various types of intricate microbial populations in an anaerobic digester (AD). To understand the anaerobic digestion system better, a broad-based study must be conducted on the microbial population. Deep understanding of the complete metagenomics including microbial structure, functional gene form, similarity/differences, and relationships between metabolic pathways and product formation, could aid in optimization and enhancement of AD processes. With advancements in technologies for metagenomic sequencing, for example, next generation sequencing and high-throughput sequencing, have revolutionized the study of microbial dynamics in anaerobic digestion. This review includes a brief introduction to the basic process of metagenomics research and includes a detailed summary of the various bioinformatics approaches, viz., total investigation of data obtained from microbial communities using bioinformatics methods to expose metagenomics characterization. This includes (1) methods of DNA isolation and sequencing, (2) investigation of anaerobic microbial communities using bioinformatics techniques, (3) application of the analysis of anaerobic microbial community and biogas production, and (4) restriction and prediction of bioinformatics analysis on microbial metagenomics. The review has been concluded, giving a summarized insight into bioinformatic tools and also promoting the future prospects of integrating humungous data with artificial intelligence and neural network software.
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