Computational Network Inference for Bacterial Interactomics

Katherine James, Jose Muñoz-Muñoz

Research output: Contribution to journalReview articlepeer-review

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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.
Original languageEnglish
Article numbere01456-21
Pages (from-to)1-15
Number of pages15
JournalmSystems
Volume7
Issue number2
Early online date30 Mar 2022
DOIs
Publication statusPublished - 26 Apr 2022

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