Improving Plant Breeding Using Knetminer

Graph theory has become a powerful tool to quickly make sense of a large amount of interconnected data. Plant genomes are an ideal case for graph theory applications as whole genome duplications and allopolyploidy have resulted in extensive gene multiplications so that annotations can be lifted over via edges in the graph of all combined annotations. We have created two KNetMiner instances based on the annotations for Cicer arietinum (chickpea) and Brassica oleracea (kale, cabbage, cauliflower etc.) and used those knowledge networks to find novel candidate genes for various traits related to agriculture such as disease resistance and yield. We show that a graph based approach is ideal to transfer annotations between closely related species and genes, which benefits plant breeders as previously uncharacterised but known genes are characterised by using the annotation of close neighbors. Additional KnetMiner instances for other crop plants will assist future plant breeding efforts by mining novel candidate genes.