Using network analysis to explore co-occurrence patterns in soil microbial communities
Open accessAbstract
Exploring large environmental datasets generated by high-throughput DNA sequencing technologies requires new analytical approaches to move beyond the basic inventory descriptions of the composition and diversity of natural microbial communities. In order to investigate potential interactions between microbial taxa, network analysis of significant taxon co-occurrence patterns may help to decipher the structure of complex microbial communities across spatial or temporal gradients. Here, we calculated associations between microbial taxa and applied network analysis approaches to a 16S rRNA gene barcoded pyrosequencing dataset containing >160 000 bacterial and archaeal sequences from 151 soil samples from a broad range of ecosystem types. We described the topology of the resulting network and defined operational taxonomic unit categories based on abundance and occupancy (that is, habitat generalists and habitat specialists). Co-occurrence patterns were readily revealed, including general non-random association, common life history strategies at broad taxonomic levels and unexpected relationships between community members. Overall, we demonstrated the potential of exploring inter-taxa correlations to gain a more integrated understanding of microbial community structure and the ecological rules guiding community assembly.
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Network-based approaches are likely to provide the best hope of integrating all this information by allowing a complete overview of the rumen microbiome because they can capture fundamental properties including taxonomy, functional similarity, metabolic processes, co-expression, and gene-sharing.
“Network-based approaches are likely to provide the best hope of integrating all this information by allowing a complete overview of the rumen microbiome as they are capable of capturing the fundamental properties of the microbial ecosystem including taxonomy (Barberan et al., 2012), functional similarity (Martiny et al., 2015), metabolic processes (Shlomi et al., 2008), co-expression (Zhang and”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Barberan et al., 2012
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- Authors
- Barberán, A., Bates, S. T., Casamayor, E. O., & Fierer, N.
- Affiliations
- Consejo Superior de Investigaciones Científicas (ES)
- Cooperative Institute for Research in Environmental Sciences (US)
- Corresponding author
- Barberán, Albert
- Venue
- The ISME Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-09-08
- Volume
- 6
- Issue
- 2
- Pages
- 343-351
- ISSN
- 1751-7362 (ISSN-L)
- 1751-7370
- PMC
- PMC3260507
- OpenAlex
- W1966623514
- Cited by
- 3,044
- References
- 54
- Citations by year
- 2026: 322 · 2025: 376 · 2024: 382 · 2023: 336 · 2022: 354 · 2021: 329 · 2020: 222 · 2019: 199 · 2018: 136 · 2017: 111 · 2016: 87 · 2015: 75 · 2014: 48 · 2013: 44 · 2012: 21
- FWCI
- 31.79
- Type
- journal article
- Language
- en
- Update notices
- erratum (2014-03-18)
- Open access
- bronze
- doi.org
- Licence
- Locations
- The ISME Journal (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Mycorrhizal Fungi and Plant Interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- soil microbial communities, microbial interaction networks, community assembly, habitat generalists and specialists, network topology, spatial and temporal gradients, life history strategies
- MeSH terms
- Archaea, Bacteria, RNA, Ribosomal, 16S, Soil Microbiology, Ecosystem, Bacterial Physiological Phenomena
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