Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/84644 |
Resumo: | With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples. |
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Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolismMetabolomicsMicrobiotaMetabolic networksMachine learningDeep neural networksGenome-scale modelsMulti-omics integrationWith the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.This work was funded by the European Molecular Biology Laboratory. MZ-K acknowledges support from the AXA Research Fund.info:eu-repo/semantics/publishedVersionElsevierUniversidade do MinhoBartmanski, Bartosz JanRocha, MiguelZimmermann-Kogadeeva, Maria2023-05-172023-05-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/84644engBartmanski, B. J., Rocha, M., & Zimmermann-Kogadeeva, M. (2023, August). Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism. Current Opinion in Chemical Biology. Elsevier BV. http://doi.org/10.1016/j.cbpa.2023.1023241367-593110.1016/j.cbpa.2023.10232437207402102324https://www.sciencedirect.com/journal/current-opinion-in-chemical-biologyinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-09-30T01:31:08Zoai:repositorium.sdum.uminho.pt:1822/84644Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:46:23.551882Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism |
title |
Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism |
spellingShingle |
Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism Bartmanski, Bartosz Jan Metabolomics Microbiota Metabolic networks Machine learning Deep neural networks Genome-scale models Multi-omics integration |
title_short |
Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism |
title_full |
Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism |
title_fullStr |
Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism |
title_full_unstemmed |
Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism |
title_sort |
Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism |
author |
Bartmanski, Bartosz Jan |
author_facet |
Bartmanski, Bartosz Jan Rocha, Miguel Zimmermann-Kogadeeva, Maria |
author_role |
author |
author2 |
Rocha, Miguel Zimmermann-Kogadeeva, Maria |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Bartmanski, Bartosz Jan Rocha, Miguel Zimmermann-Kogadeeva, Maria |
dc.subject.por.fl_str_mv |
Metabolomics Microbiota Metabolic networks Machine learning Deep neural networks Genome-scale models Multi-omics integration |
topic |
Metabolomics Microbiota Metabolic networks Machine learning Deep neural networks Genome-scale models Multi-omics integration |
description |
With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-17 2023-05-17T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/84644 |
url |
https://hdl.handle.net/1822/84644 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Bartmanski, B. J., Rocha, M., & Zimmermann-Kogadeeva, M. (2023, August). Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism. Current Opinion in Chemical Biology. Elsevier BV. http://doi.org/10.1016/j.cbpa.2023.102324 1367-5931 10.1016/j.cbpa.2023.102324 37207402 102324 https://www.sciencedirect.com/journal/current-opinion-in-chemical-biology |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133032361230336 |