Developing integrated crop knowledge networks to advance candidate gene discovery

A - Papers appearing in refereed journals

Hassani-Pak, K., Castellote, M., Esch, M., Hindle, M. M., Lysenko, A., Taubert, J. and Rawlings, C. J. 2016. Developing integrated crop knowledge networks to advance candidate gene discovery. Applied and Translational Genomics. 11 (December), pp. 18-26.

AuthorsHassani-Pak, K., Castellote, M., Esch, M., Hindle, M. M., Lysenko, A., Taubert, J. and Rawlings, C. J.

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 ( and the knowledge resources ( 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.

Keywordsbioinformatics; knowledge network; data integration; gene discovery; knowledge discovery; crop genomics
Year of Publication2016
JournalApplied and Translational Genomics
Journal citation11 (December), pp. 18-26
Digital Object Identifier (DOI)
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeWheat
From data to knowledge / the ONDEX System for integrating Life Sciences data sources
[20:20 Wheat] Maximising yield potential of wheat
[20:20 Wheat] Protecting yield potential of wheat
QTLNetMiner: Mining Candidate Gene Networks From Genetic Studies of Crops and Animals
Bioinformatics [do not make public]
Publisher's version
Copyright license
Output statusPublished
Publication dates
Online02 Nov 2016
Publication process dates
Accepted24 Oct 2016

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