Molecular ecological network analyses

By Ye Deng, Yi-Huei Jiang, Yunfeng Yang, Zhili He, Feng Luo, Jizhong Zhou. BMC Bioinformatics, 2012

2,717 citations recorded by the source corpus.

BackgroundUnderstanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.ResultsHere, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).ConclusionsThe RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/afdd935e915f0046b7cd6a44c47f49ff8ce991f6

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Citations in the corpus

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision (2016, methodology): “All RMT calculations were implemented through the Molecular Ecological Network Approach Pipeline at http://ieg2.ou.edu/MENA (Deng et al., 2012).”
NetCoMi: network construction and comparison for microbiome data in R (2020, neutral): “…such as biological network estimation (e.g. [112, 113] for recent contributions) and (differential) biological network analysis [32– 34, 52, 111, 114] are available, NetCoMi offers a unique and modular R software framework that integrates the complete process ofestimating,analysingandcomparing…”
Sparse and Compositionally Robust Inference of Microbial Ecological Networks (2014, background): “network is correlation analysis; that is computing Pearson’s correlation coefficient among all pairs of OTU samples, and an interaction between microbes is assumed when the absolute value of the correlation coefficient is sufficiently high [15, 16].”
ggClusterNet: An R package for microbiome network analysis and modularity‐based multiple network layouts (2022, methodology): “Tools for network analysis of microbiome included web tool MENA [11] (MENAP), R packages (WGCNA [12], igraph [13], ggraph [14], SpiecEasi [15], interactive software (Cytoscape [16] and Gephi [17]), python packages (Net-workX [18] and SparCC [19]), and so forth.”
Rate-specific responses of prokaryotic diversity and structure to nitrogen deposition in the Leymus chinensis steppe (2014, methodology): “…among different species within a community and their responses to environmental changes, the phylogenetic molecular ecological networks (pMENs) (Deng et al., 2012) were constructed following the molecular ecological network analyses pipeline (MENAP) (http://ieg2.ou.edu/MENA/ main.cgi) with…”
Characterization of bacterial communities in soil and sediment of a created riverine wetland complex using high-throughput 16S rRNA amplicon sequencing (2014, methodology): “Nodes (OTUs) are coloured by representative phy etween models within the sample group and between groups are presented with ignificantly lower in Wom samples compared to Wtrans and Ox amples.”
Fungal networks in yield-invigorating and -debilitating soils induced by prolonged potato monoculture. (2013, background): “…composition, diversity and their changes across space, time, or experimental treatments, but cannot be used to reveal interactions among community members, which could be more important to ecosystem functioning than abundance and diversity, especially in complex ecosystems (Deng et al., 2012).”
Continuous‐cropping tobacco caused variance of chemical properties and structure of bacterial network in soils (2018, background): “sources, thus growing well in many habitats and distributing broadly across the soil ecosystem, whereas specialists have special nutritional requirements, thus surviving only in some restricted habitats (Deng et al., 2012; Olesen et al., 2007; Tao et al., 2018).”
Network analysis methods for studying microbial communities: A mini review (2021, methodology): “While the choice for the cut-off is often left to the user, the Molecular Ecological Network Analysis Pipeline (MENAP) [25] offers an automated selection of the optimal correlation threshold via a random matrix theory-based method [31] to simulate a random background.”
Biogeographic patterns of microbial association networks in paddy soil within Eastern China (2020, methodology): “The topological features for individual nodes are useful in accessing nodes’ roles in the network, and the topological features for the network are useful in comparing various MENs identified under different situations (Deng et al., 2012).”
Recent progress in biohydrometallurgy and microbial characterisation (2018, methodology): “Molecular ecological networks analyses (including differential equation-based networks, Bayesian networks, and relevance/co-expression networks), have been applied as a tool to construct ecological networks that illustrate the interactions among the species and genes of microbial communities (Deng e”