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Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery

Microbial specialized metabolites are key mediators in host-microbiome interactions. Most of the chemical space produced by the microbiome currently remains unexplored and uncharacterized. This situation calls for new and improved methods to exploit the growing publicly available genomic and metabol...

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Detalles Bibliográficos
Autores principales: Louwen, Joris J. R., van der Hooft, Justin J. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8407348/
https://www.ncbi.nlm.nih.gov/pubmed/34427506
http://dx.doi.org/10.1128/mSystems.00726-21
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author Louwen, Joris J. R.
van der Hooft, Justin J. J.
author_facet Louwen, Joris J. R.
van der Hooft, Justin J. J.
author_sort Louwen, Joris J. R.
collection PubMed
description Microbial specialized metabolites are key mediators in host-microbiome interactions. Most of the chemical space produced by the microbiome currently remains unexplored and uncharacterized. This situation calls for new and improved methods to exploit the growing publicly available genomic and metabolomic data sets and connect the outcomes to structural and functional knowledge inferred from transcriptomics and proteomics experiments. Here, we first describe currently available approaches that support the comprehensive mining of metabolomics and genomics data. Next, we provide our vision on how to move forward toward the automated linking of omics data of specialized metabolites to their structures, biosynthesis pathways, producers, and functions.
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spelling pubmed-84073482021-09-09 Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery Louwen, Joris J. R. van der Hooft, Justin J. J. mSystems Commentary Microbial specialized metabolites are key mediators in host-microbiome interactions. Most of the chemical space produced by the microbiome currently remains unexplored and uncharacterized. This situation calls for new and improved methods to exploit the growing publicly available genomic and metabolomic data sets and connect the outcomes to structural and functional knowledge inferred from transcriptomics and proteomics experiments. Here, we first describe currently available approaches that support the comprehensive mining of metabolomics and genomics data. Next, we provide our vision on how to move forward toward the automated linking of omics data of specialized metabolites to their structures, biosynthesis pathways, producers, and functions. American Society for Microbiology 2021-08-24 /pmc/articles/PMC8407348/ /pubmed/34427506 http://dx.doi.org/10.1128/mSystems.00726-21 Text en Copyright © 2021 Louwen and van der Hooft. 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 Commentary
Louwen, Joris J. R.
van der Hooft, Justin J. J.
Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery
title Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery
title_full Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery
title_fullStr Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery
title_full_unstemmed Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery
title_short Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery
title_sort comprehensive large-scale integrative analysis of omics data to accelerate specialized metabolite discovery
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8407348/
https://www.ncbi.nlm.nih.gov/pubmed/34427506
http://dx.doi.org/10.1128/mSystems.00726-21
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