The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming . To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerised for easy use . KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists discover and understand the biological stories of complex traits and diseases across species . It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches . KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organisations to accelerate gene discovery . The software is generic and customizable and can therefore be readily applied to new species and data types, for example it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research . Here we give an overview of the main approaches to using KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL . KnetMiner is available at: https: //knetminer.org.
Index: Gene discovery, bioinformatics, candidate gene prioritization, data integration, exploratory data mining, gene network, information visualisation, knowledge discovery, knowledge graph, systems biology