Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.
Curr Opin Chem Biol
; 75: 102324, 2023 08.
Article
em En
| MEDLINE
| ID: mdl-37207402
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Metaboloma
/
Metabolômica
Idioma:
En
Revista:
Curr Opin Chem Biol
Assunto da revista:
BIOQUIMICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Alemanha