2026-10-13T07:25:30Z https://data.mendeley.com/oai

oai:data.mendeley.com/gtm274bsfc.1 2026-07-23T22:40:35Z

Hendricks, Shuaib The use of a metabologenomics approach for the discovery of antimicrobial natural products - Thesis Supplementary Data Mendeley Data This dataset supports a multi-omics investigation into the biosynthetic potential and metabolite production of selected microbial strains under varying culture conditions. The primary hypothesis of this study is that strain repositioning combined with the OSMAC (One Strain Many Compounds) approach and integrated metabolomic and genomic analyses can reveal hidden metabolic diversity in actinobacteria. The dataset contains processed LC-MS/MS metabolomics data, whole genome sequencing (WGS) assemblies, genome annotations, and integrated metabologenomics outputs. Metabolomic data were generated using LC-MS/MS and processed into feature-based molecular networks using GNPS2. These data include MGF files, feature tables, metadata, and annotation outputs from tools such as SIRIUS and CANOPUS. Multivariate and univariate statistical analyses (e.g., PCA, PCoA, random forest classification, ANOVA, PERMANOVA) were performed to identify metabolite patterns associated with specific strains and culture conditions. 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. Chemistry Biochemistry Molecular Biology Analytical Chemistry Drug Discovery Bioinformatics Mass Spectrometry Microbial Genomics Natural Product Actinomycete Untargeted Metabolomics Multiomics Prins, Alaric Watson, Daniel Le Roes-HIll, Marilize Beukes, Denzil Dataset doi:10.17632/gtm274bsfc.1 oai:data.mendeley.com/gtm274bsfc.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/gtm274bsfc 2026-07-23T22:40:35Z