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 containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore 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 organizations 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 behind 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.
12Generating new ideas and scientific hypotheses is often the result of extensive literature and 13 database reviews, overlaid with scientists' own novel data and a creative process of making 14 connections that were not made before. We have developed a comprehensive approach to guide 15 this technically challenging data integration task and to make knowledge discovery and 16 hypotheses generation easier for plant and crop researchers. KnetMiner can digest large volumes 17 of scientific literature and biological research to find and visualise links between the genetic and 18 biological properties of complex traits and diseases. Here we report the main design principles 19 behind KnetMiner and provide use cases for mining public datasets to identify unknown links 20 between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an 21 evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size 22 QTL. We have developed KnetMiner knowledge graphs and applications for a range of species 23 including plants, crops and pathogens. KnetMiner is the first open-source gene discovery platform 24 that can leverage genome-scale knowledge graphs, generate evidence-based biological networks 25 and be deployed for any species with a sequenced genome. KnetMiner is available at 26 http://knetminer.org. 27 2 KEYWORDS 28 knowledge graph, interactive knowledge discovery, exploratory data mining, omics data 29 integration, candidate gene prioritization, information visualisation, systems biology 30 31
Enormous volumes of COVID-19 research data have been published and this continues to increase daily. This creates challenges for researchers to interpret, prioritize and summarize their own findings in the context of published literature, clinical trials, and a multitude of databases. Overcoming the data interpretation bottleneck is vital to help researchers to be more efficient in their quest to identify COVID-19 risk factors, potential treatments, drug side-effects, and much more. As a proof of concept, we have organized and integrated a range of COVID-19 and human biomedical data and literature into a knowledge graph (KG). Here we present the datasets we have integrated so far and the content of the KG which consists of 674,969 biological concepts and over 1.6 million relationships between them. The COVID-19 KG is available via KnetMiner, an interactive online platform for gene discovery and knowledge mining, or via RDF and Neo4j graph formats which can be searched programmatically through SPARQL and Cypher endpoints. KnetMiner is a road mapped ELIXIR UK service. We hope this integrated resource will enable faster data interpretation and discovery of linkages between genes, drugs, diseases and many more types of information relating to COVID-19.
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