Multi-omic analysis tools for microbial metabolites prediction

Brief Bioinform. 2024 May 23;25(4):bbae264. doi: 10.1093/bib/bbae264.

Abstract

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

Keywords: gene cluster mining; gut microbiota; machine learning; microbial metabolism; multi-omics; natural product.

Publication types

  • Review

MeSH terms

  • Algorithms
  • Bacteria / classification
  • Bacteria / genetics
  • Bacteria / metabolism
  • Computational Biology / methods
  • Genomics / methods
  • Humans
  • Metabolome
  • Metabolomics* / methods
  • Multiomics
  • Proteomics / methods
  • Software*