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Computational Network Inference for Bacterial Interactomics

Lookup NU author(s): Dr Katherine JamesORCiD, Dr Jose Munoz Munoz

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Abstract

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.


Publication metadata

Author(s): James K, Munoz-Munoz J

Publication type: Review

Publication status: Published

Journal: mSystems

Year: 2022

Volume: 7

Issue: 2

Online publication date: 30/03/2022

Acceptance date: 09/03/2022

ISSN (electronic): 2379-5077

URL: https://doi.org/10.1128/msystems.01456-21

DOI: 10.1128/msystems.01456-21


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