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Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology

Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.e...

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Autores principales: Yang, Pengshuo, Tan, Chongyang, Han, Maozhen, Cheng, Lin, Cui, Xuefeng, Ning, Kang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671402/
https://www.ncbi.nlm.nih.gov/pubmed/33575595
http://dx.doi.org/10.1093/nargab/lqaa042
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author Yang, Pengshuo
Tan, Chongyang
Han, Maozhen
Cheng, Lin
Cui, Xuefeng
Ning, Kang
author_facet Yang, Pengshuo
Tan, Chongyang
Han, Maozhen
Cheng, Lin
Cui, Xuefeng
Ning, Kang
author_sort Yang, Pengshuo
collection PubMed
description Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.e. species) of the Species-Centric Networks (SCNs). However, little was understood about the change of correlation between network members (i.e. edges of the SCNs) when the network was disturbed. Here, we introduced a Correlation-Centric Network (CCN) to the microbial research based on the concept of edge networks. In CCN, each node represented a species–species correlation, and edge represented the species shared by two correlations. In this research, we investigated the CCNs and their corresponding SCNs on two large cohorts of microbiome. The results showed that CCNs not only retained the characteristics of SCNs, but also contained information that cannot be detected by SCNs. In addition, when the members of microbial communities were decreased (i.e. environmental disturbance), the CCNs fluctuated within a small range in terms of network connectivity. Therefore, by highlighting the important species correlations, CCNs could unveil new insights when studying not only the functions of target species, but also the stabilities of their residing microbial communities.
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spelling pubmed-76714022021-02-10 Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology Yang, Pengshuo Tan, Chongyang Han, Maozhen Cheng, Lin Cui, Xuefeng Ning, Kang NAR Genom Bioinform Methods Article Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.e. species) of the Species-Centric Networks (SCNs). However, little was understood about the change of correlation between network members (i.e. edges of the SCNs) when the network was disturbed. Here, we introduced a Correlation-Centric Network (CCN) to the microbial research based on the concept of edge networks. In CCN, each node represented a species–species correlation, and edge represented the species shared by two correlations. In this research, we investigated the CCNs and their corresponding SCNs on two large cohorts of microbiome. The results showed that CCNs not only retained the characteristics of SCNs, but also contained information that cannot be detected by SCNs. In addition, when the members of microbial communities were decreased (i.e. environmental disturbance), the CCNs fluctuated within a small range in terms of network connectivity. Therefore, by highlighting the important species correlations, CCNs could unveil new insights when studying not only the functions of target species, but also the stabilities of their residing microbial communities. Oxford University Press 2020-06-25 /pmc/articles/PMC7671402/ /pubmed/33575595 http://dx.doi.org/10.1093/nargab/lqaa042 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods Article
Yang, Pengshuo
Tan, Chongyang
Han, Maozhen
Cheng, Lin
Cui, Xuefeng
Ning, Kang
Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology
title Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology
title_full Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology
title_fullStr Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology
title_full_unstemmed Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology
title_short Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology
title_sort correlation-centric network (ccn) representation for microbial co-occurrence patterns: new insights for microbial ecology
topic Methods Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671402/
https://www.ncbi.nlm.nih.gov/pubmed/33575595
http://dx.doi.org/10.1093/nargab/lqaa042
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