Network analysis methods for studying microbial communities: A mini review.

Computational and Structural Biotechnology Journal
Monica Steffi MatchadoMarkus List

Abstract

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.

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Software Mentioned

CClasso
NetComi
Cytoscape Command line tool
MixMCMC
DIABLO
CoNet
Molecular Ecological Network Analysis Pipeline ( MENAP )
Network
Microbiome Differential Network Estimation ( MDINE )
MixMPLN

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