Network analysis methods for studying microbial communities: A mini review

Monica Steffi Matchado, Michael Lauber, Sandra Reitmeier, Tim Kacprowski, Jan Baumbach, Dirk Haller, Markus List*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Original languageEnglish
JournalComputational and Structural Biotechnology Journal
Pages (from-to)2687-2698
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2021


  • Microbial co-occurrence networks
  • Microbial interactions
  • Network analysis
  • Trans-kingdom interactions


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