Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions

Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) represents a promising way finding such chemistry. However, due to the lack detailed biosynthetic knowledge majority predicted BGCs, large number possible combinations, this is not simple task. This problem becoming ever more pressing with increased availability omics sets. Current tools effective at identifying valid automatically, manual verification considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier prioritise true relative others. Based on standardising commonly used score, we introduce new, score an Input-Output Kernel Regression approach. Finally, present NPLinker, software framework link data. Results verified publicly available include validated links.