Gene-to-trait knowledge graphs show association of plant photoreceptors with physiological and developmental processes that can confer agronomic benefits

Global population growth, climate change, altered precipitation rates and cropping patterns are increasingly challenging plant scientists to improve crop productivity for food and non-food applications. Hence, there is a pressing need for identifying candidate genes that can be targeted for breeding future crops having enhanced agronomic benefits. Network mapping utilises available data and creates knowledge graphs that aid in visualising association(s) between the individual data items. Here, we have generated gene-to-trait knowledge graphs of known plant photoreceptors using the KnetMiner gene discovery platform which generates biological networks from literature/data sets available in public databases. The resulting knowledge graphs indicate a close association of photoreceptors with various physiological and developmental processes such as shoot architecture, yield, disease resistance and water use efficiency among others, that can confer agronomically important benefits. Such information can be of assistance to plant biologists in the selection of potential gene targets for improving agronomically beneficial traits in plants. This report highlights the potential of machine learning and knowledge graphs as aids in more efficient knowledge discovery and novel decision-making processes which can also be employed for crop breeding or crop engineering.