Integrating pan-genome with metagenome for microbial community profiling

• Published in: Computational and structural biotechnology journal Vol. 19; pp. 1458 - 1466
• Main Authors: Zhong, Chaofang, Chen, Chaoyun, Wang, Lusheng, Ning, Kang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2021; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: Integrated pan-genomics with metagenomics; Metagenome; Microbial community; Pan-genome; Review; Integrated pan-genomics with metagenomics; Metagenome; Pan-genome; Microbial community
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2021.02.021

Advances in sequencing technology have led to the increased availability of genomes and metagenomes, which has greatly facilitated microbial pan-genome and metagenome analysis in the community. In line with this trend, studies on microbial genomes and phenotypes have gradually shifted from individuals to environmental communities. Pan-genomics and metagenomics are powerful strategies for in-depth profiling study of microbial communities. Pan-genomics focuses on genetic diversity, dynamics, and phylogeny at the multi-genome level, while metagenomics profiles the distribution and function of culture-free microbial communities in special environments. Combining pan-genome and metagenome analysis can reveal the microbial complicated connections from an individual complete genome to a mixture of genomes, thereby extending the catalog of traditional individual genomic profile to community microbial profile. Therefore, the combination of pan-genome and metagenome approaches has become a promising method to track the sources of various microbes and decipher the population-level evolution and ecosystem functions. This review summarized the pan-genome and metagenome approaches, the combined strategies of pan-genome and metagenome, and applications of these combined strategies in studies of microbial dynamics, evolution, and function in communities. We discussed emerging strategies for the study of microbial communities that integrate information in both pan-genome and metagenome. We emphasized studies in which the integrating pan-genome with metagenome approach improved the understanding of models of microbial community profiles, both structural and functional. Finally, we illustrated future perspectives of microbial community profile: more advanced analytical techniques, including big-data based artificial intelligence, will lead to an even better understanding of the patterns of microbial communities.