Developing integrated crop knowledge networks to advance candidate gene discovery

Keywan Hassani-Pak*, Martin Castellote, Maria Esch, Matthew Hindle, Artem Lysenko, Jan Taubert, Christopher Rawlings

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

Original languageEnglish
Pages (from-to)18-26
Number of pages9
JournalApplied and Translational Genomics
Volume11
DOIs
Publication statusPublished - 1 Dec 2016

Keywords

  • Bioinformatics
  • Data integration
  • Gene discovery
  • Knowledge discovery, crop genomics
  • Knowledge network

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