About event
Although plants are an incredibly rich source of pharmaceutically relevant specialized metabolites, biosynthetic pathway elucidation in plants has proven challenging. Unlike bacteria and many fungal species that contain biosynthetic operons, the genes of a given plant typically scatter randomly across the genome, making pathway discovery via genome mining nearly impossible. My lab is developing generalized workflows for connecting biosynthetic gene sequences (RNAseq data) to their downstream metabolites (LC-MS data). For this, I will demonstrate a top-down approach, which is based on correlating expression levels of enzymes with metabolite abundance across a plant family, and a bottom-up approach, which is based on predicting the substrate specificity and function of individual biosynthetic enzymes directly from their sequences using deep learning.
