Network analyses in microbiome based on high-throughput multi-omics data

Abstract

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.

Holo-Omics: Integrated Host-Microbiota Multi-omics for Basic and Applied Biological Research

“…Statistical advances in the last decade including development of deep learning methods are helping address the challenges posed by the high dimensionality and complex correlation structure of the data. Development of such methods is an area of active research where several advances have been made in integrating host-microbiome data ( Bersanelli et al., 2016 ; Heintz-Buschart et al., 2016 ; Liu et al., 2020 ).…”

“…Here we propose that hologenomics (the combined genetic content of the host and the microbiota ) can be expanded to the holo-omic level by the incorporation of data from multiple omic levels from both host and microbiota domains ( Limborg et al., 2018 ) ( Figure 1 B). This approach is inspired by elements originating from systems biology (e.g., metagenomics systems biology [ Greenblum et al., 2012 ] and the use of multi-omic data integration [ Bersanelli et al., 2016 ; Heintz-Buschart et al., 2016 ; Liu et al., 2020 ]). However, multi-omics implies omic data from only one domain, whereas holo-omics is defined by the incorporation of both host and microbial data.…”

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

“…Databases are launched to share information on gut microbes paired with genome sequences and longitudinal multi-omics data ( 79 ). The intersection of ML with network biology will enrich microbiome research where many microorganisms still remain understudied ( 80 , 81 ). Transfer learning, a branch of ML, provides opportunity to transfer the learned information from a well-studied species to an understudied species ( 82 ).…”

Unveiling the Ocean Viromes of Challenger Deep, the Deepest Place in the World’s Oceans

“…Viral genes, predicted by Pfamscan, occurring in the same contig were defined as an interaction. The gene co-occurrence network was constructed using a custom perl script and the edges of the co-occurrence network were weighted by the frequency of co-occurrence [63,64].…”