The use of a metabologenomics approach for the discovery of antimicrobial natural products - Thesis Supplementary Data

Genomic data include polished genome assemblies, quality control metrics, and functional annotations, alongside biosynthetic gene cluster (BGC) predictions and comparative genomics analyses. Phylogenomic and taxonomic analyses (ANI, AAI, dDDH) were used to contextualize strain identity and novelty. BiG-SCAPE outputs were used to group BGCs into gene cluster families for comparative analysis.

Metabologenomics integration was performed to link metabolomic features with predicted BGCs, enabling the identification of candidate gene-metabolite relationships. These results are provided as putative links which could be prioritised for further drug discovery. The data demonstrate that cultivation conditions significantly influence metabolite production profiles, and that integrating metabolomics with genomics enhances the interpretation of microbial chemical diversity.

This dataset can be used for reanalysis of molecular networks, validation of genomic predictions, or further development of metabolite annotation and gene cluster linking approaches. Full methodological details are provided in the associated thesis.