KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

Abstract

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

A co-expression network in hexaploid wheat reveals mostly balanced expression and lack of significant gene loss of homeologous meiotic genes upon polyploidization

“…Our WGCNA co-expression network and GO enrichment data has been integrated with the wheat knowledge network [ 132 ] to make it publicly accessible in a larger biological context and to make it searchable through the KnetMiner web application (http://knetminer.rothamsted.ac.uk; [ 133 ]. KnetMiner can be searched with keywords (incl.…”

“…The term ‘weight value’ in the input files for Cytoscape refers to the connection strength between two nodes (genes) in terms of correlation value obtained from the topological overlap matrices (TOM). The co-expression network data has also been integrated with the wheat knowledge network [ 132 ] to make it publicly accessible through the KnetMiner web application (http://knetminer.rothamsted.ac.uk; [ 133 ]. The data was semantically modelled as nodes of type Gene, Co-Expression-Module, Co-Expression-Study, GOterm; connected by relations of type part-of and enriched.…”

A Dual Strategy of Breeding for Drought Tolerance and Introducing Drought-Tolerant, Underutilized Crops into Production Systems to Enhance Their Resilience to Water Deficiency

“…For this, the identification of candidate genes that are expressed in drought-stress conditions is a major strategy, and genomic technologies such as microarray and transcriptomic analyses have been useful in the identification of these genes [ 101 , 128 ]. These technologies include new strategies in the identification of candidate genes associated with important agronomic traits in species with genome sequencing that have been developed as novel in-silico platforms for gene discovery and that help scientists to identify candidate genes through the knowledge available in genetic databases and public information, allowing candidate gene prioritization [ 129 ]. Although the advance of genomic technology has allowed the identification of QTLs for drought tolerance, this information has been underutilized in the generation of new cultivars with drought tolerance [ 113 ].…”

KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

“…Several efforts have constructed KGs by ingesting and transforming scientific literature, 34,35 some with a few additional types of data also included, such as confirmed case and mortality data; 36 clinical information, drug trial, and sequencing data; 37 drug, drug trial and genome sequence data; 38 diseases, chemicals, and genes 39 . Other KG efforts ingest a wider array of data, including diseases, genes, proteins and their structural data, drugs, and drug side effects; 40 pathways, proteins, genes, drugs, diseases, anatomical terms, phenotypes, microbiome; 41 genes, proteins, diseases, phenotypes, genome sequences; 42,43 geographic, viral genes, genes and proteins. 44 Several projects have focused specifically on integrating a wide variety of COVID-19 data to create KGs to investigate drug repurposing.…”