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*

*Kontaktforfatter for dette arbejde

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Abstrakt

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.

OriginalsprogEngelsk
TidsskriftComputational and Structural Biotechnology Journal
Vol/bind19
Sider (fra-til)2687-2698
ISSN2001-0370
DOI
StatusUdgivet - 2021

Bibliografisk note

Funding Information:
This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German 582 Research Foundation) – Projektnummer 395357507 – SFB 1371. Michael Lauber was supported by the Hanns-Seidel-Stiftung.

Publisher Copyright:
© 2021

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