ArticleBMC bioinformatics2012
Molecular ecological network analyses.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 869 papers.
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Who cites it
869 citing papers in PubMed.
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- Vertical Stratification in Composition, Metabolic Potential and Co-Occurrence Networks of Archaeal Communities in Mangrove Sediments, Zhanjiang, China.Ecology and evolution · 2026Article
- Network characteristics and driving factors of leaf trait networks along a gradient of rocky desertification.Plant diversity · 2026Article
- A Two-Step SeCys Oxidation Pathway Bridging Soil Selenium Bioavailability and Rice Selenium Biofortification.Environmental microbiology · 2026Article
- Convergent Bacterial but Divergent Fungal Communities in the Tobacco Rhizosphere Under Intensive Management on Contrasting Soils.Microorganisms · 2026Article
- Microbiome analysis and epigenetic patterns revealed distinct differences between two elm species with contrasting Dutch elm disease resistance.Microbiome · 2026Article
- Synthetic communities and metabolite-driven enrichment of keystone rhizosphere microbial taxa suppress bacterial wilt disease in peanut.Plant communications · 2026Article
- Hydrological Regime Shifts Reshape Assembly Dynamics of Multi-Trophic Microbial Communities in Pumping Station Forebays.Environmental microbiology · 2026Article
- Multiomics reveals postherbivory tea leaves restructure endophytic microbiota to resist disease and boost saccharide metabolism to promote growth.Horticulture research · 2026Article
809 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.