Given the important role food plays in health and wellbeing, the past decades have seen considerable experimental efforts dedicated to mapping the chemical composition of food ingredients. As the composition of raw food is genetically predetermined, here we ask, to what degree can we rely on genomics to predict the chemical composition of natural ingredients. We therefore developed tools to unveil the chemical composition of 75 edible plants’ genomes, finding that genome-based annotations increase the number of compounds linked to specific plants by 42 to 100%. We rely on Gibbs free energy to identify compounds that accumulate in plants, i.e., those that are more likely to be detected experimentally. To quantify the accuracy of our predictions, we performed untargeted metabolomics on 13 plants, allowing us to experimentally confirm the detectability of the predicted compounds. For example, we find 59 novel compounds in corn, predicted by genomics annotations and supported by our experiments, but previously not assigned to the plant. Our study shows that genome-based annotations can lead to an integrated metabologenomics platform capable of unveiling the chemical composition of edible plants, and the biochemical pathways responsible for the observed compounds.
Genomics-based annotations help unveil the molecular composition of edible plants