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Integrating pan-genome with metagenome for microbial community profiling

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 individua...

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Autores principales: Zhong, Chaofang, Chen, Chaoyun, Wang, Lusheng, Ning, Kang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010324/
https://www.ncbi.nlm.nih.gov/pubmed/33841754
http://dx.doi.org/10.1016/j.csbj.2021.02.021
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author Zhong, Chaofang
Chen, Chaoyun
Wang, Lusheng
Ning, Kang
author_facet Zhong, Chaofang
Chen, Chaoyun
Wang, Lusheng
Ning, Kang
author_sort Zhong, Chaofang
collection PubMed
description 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.
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spelling pubmed-80103242021-04-08 Integrating pan-genome with metagenome for microbial community profiling Zhong, Chaofang Chen, Chaoyun Wang, Lusheng Ning, Kang Comput Struct Biotechnol J Review Article 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. Research Network of Computational and Structural Biotechnology 2021-03-07 /pmc/articles/PMC8010324/ /pubmed/33841754 http://dx.doi.org/10.1016/j.csbj.2021.02.021 Text en © 2021 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Review Article
Zhong, Chaofang
Chen, Chaoyun
Wang, Lusheng
Ning, Kang
Integrating pan-genome with metagenome for microbial community profiling
title Integrating pan-genome with metagenome for microbial community profiling
title_full Integrating pan-genome with metagenome for microbial community profiling
title_fullStr Integrating pan-genome with metagenome for microbial community profiling
title_full_unstemmed Integrating pan-genome with metagenome for microbial community profiling
title_short Integrating pan-genome with metagenome for microbial community profiling
title_sort integrating pan-genome with metagenome for microbial community profiling
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010324/
https://www.ncbi.nlm.nih.gov/pubmed/33841754
http://dx.doi.org/10.1016/j.csbj.2021.02.021
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