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bioRxiv · 10.1101/2020.04.02.017004

KnetMiner - Intelligent search and visualisation of connected data to explain complex traits and diseases

Hassani-Pak, K.·Singh, A.·Brandizi, M.·Hearnshaw, J.·Phillips, A. L.·Doonan, J. H.·Rawlings, C.
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Abstract

Generating new ideas and scientific hypotheses is often the result of extensive literature and database reviews, overlaid with scientists own novel data and a creative process of making connections that were not made before. We have developed a comprehensive approach to guide this technically challenging data integration task and to make knowledge discovery and hypotheses generation easier for plant and crop researchers. KnetMiner can digest large volumes of scientific literature and biological research to find and visualise links between the genetic and biological properties of complex traits and diseases. Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size QTL. We have developed KnetMiner knowledge graphs and applications for a range of species including plants, crops and pathogens. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. KnetMiner is available at http://knetminer.org.

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Hassani-Pak, K., Singh, A., Brandizi, M., Hearnshaw, J., Phillips, A. L., Doonan, J. H., Rawlings, C.. 2020-04-03. KnetMiner - Intelligent search and visualisation of connected data to explain complex traits and diseases. https://doi.org/10.1101/2020.04.02.017004

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