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Computational Network Inference for Bacterial Interactomics

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 eukary...

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Detalles Bibliográficos
Autores principales: James, Katherine, Muñoz-Muñoz, Jose
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040873/
https://www.ncbi.nlm.nih.gov/pubmed/35353009
http://dx.doi.org/10.1128/msystems.01456-21
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author James, Katherine
Muñoz-Muñoz, Jose
author_facet James, Katherine
Muñoz-Muñoz, Jose
author_sort James, Katherine
collection PubMed
description 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.
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spelling pubmed-90408732022-04-27 Computational Network Inference for Bacterial Interactomics James, Katherine Muñoz-Muñoz, Jose mSystems Minireview 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. American Society for Microbiology 2022-03-30 /pmc/articles/PMC9040873/ /pubmed/35353009 http://dx.doi.org/10.1128/msystems.01456-21 Text en Copyright © 2022 James and Muñoz-Muñoz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Minireview
James, Katherine
Muñoz-Muñoz, Jose
Computational Network Inference for Bacterial Interactomics
title Computational Network Inference for Bacterial Interactomics
title_full Computational Network Inference for Bacterial Interactomics
title_fullStr Computational Network Inference for Bacterial Interactomics
title_full_unstemmed Computational Network Inference for Bacterial Interactomics
title_short Computational Network Inference for Bacterial Interactomics
title_sort computational network inference for bacterial interactomics
topic Minireview
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040873/
https://www.ncbi.nlm.nih.gov/pubmed/35353009
http://dx.doi.org/10.1128/msystems.01456-21
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