期刊
BRIEFINGS IN BIOINFORMATICS
卷 22, 期 2, 页码 1639-1655出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa005
关键词
microbiome; multi-omics; co-occurrence; network analysis; integrated analysis
资金
- National Natural Science Foundation of China (NSFC) [61772313]
- Young Scholars Program of Shandong University (YSPSDU) [2015WLJH19]
- Innovation Method Fund of China (Ministry of Science and Technology of China) [2018IM020200]
- National Center for Advancing Translational Sciences [UL1TR002733]
- National Science Foundation [ACI-1548562]
The relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. High-throughput Omics technologies offer an opportunity for understanding the structures and functions of microbiome, but data analysis remains challenging. Network analyses provide an efficient way to understand complex microbial communities.
Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.
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