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.
|Tidsskrift||Computational and Structural Biotechnology Journal|
|Status||Udgivet - 2021|
Bibliografisk noteFunding 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.