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

Keywan Hassani-Pak*, Ajit Singh, Marco Brandizi, Joseph Hearnshaw, Jeremy D. Parsons, Sandeep Amberkar, Andrew L. Phillips, John H. Doonan, Chris Rawlings

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Citations (SciVal)
121 Downloads (Pure)

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. KnetMiner is available at: https://knetminer.org.

Original languageEnglish
Pages (from-to)1670-1678
Number of pages9
JournalPlant Biotechnology Journal
Volume19
Issue number8
Early online date05 Apr 2021
DOIs
Publication statusPublished - 24 Aug 2021

Keywords

  • bioinformatics
  • candidate gene prioritization
  • data integration
  • exploratory data mining
  • gene discovery
  • gene network
  • information visualization
  • knowledge discovery
  • knowledge graph
  • systems biology
  • Genetic Association Studies
  • Humans
  • Plant Breeding
  • Multifactorial Inheritance
  • COVID-19
  • SARS-CoV-2

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