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

Abstract

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.

PHI-base in 2022: a multi-species phenotype database for Pathogen–Host Interactions

“…Knowledge graphs provide additional data tools to investigate large-scale datasets. To enhance the querying and display of PHI-base data we plan to build multi-species pathogen-host gene networks jointly with KnetMiner ( 58 ). KnetMiner provides researchers with integrated data that connect genetic, omics and phenotypic information from a wide range of public databases.…”

Modelling of gene loss propensity in the pangenomes of three Brassica species suggests different mechanisms between polyploids and diploids

“…The assemblies, annotations, PAV‐matrices and other supporting data are available at https://doi.org/10.26182/5f1936836a1c4 and http://brassicagenome.net/databases.php . JBrowse (Buels et al ., 2016 ) and KnetMiner (Hassani‐Pak et al ., 2020 ) instances are available at http://brassicagenome.net/databases.php .…”

Bluster or Lustre: Can AI Improve Crops and Plant Health?

“…While Venkatesan et al [70] developed Agronomic Linked Data (AgroLD), a knowledge-based system that uses Semantic Web technologies and standard ontologies to integrate and query data for various plant species, including corn, rice and wheat. KnetMiner [71] represents another plant specific and extensive knowledge base that was developed with the aim to accelerate the gene-trait discovery process. Graph databases are emerging as an important technical area in the plant science domain, and we expect the adoption of such databases within the plant science community to continue to grow.…”