Federating access to wheat data services for efficient genome-specific marker design

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Wheat is the most widely grown crop worldwide that provides 20% of the calories to the growing human population. It is estimated that the average person will consume the grain of 50 wheat plants per day (https://www.jic.ac.uk/calculations/), and to support this the UK exports 15-20% (~ 2m tonnes) of its yearly crop to over 20 countries worldwide [1], as well as providing for the UK market. Research into breeding programmes over the last decade has made large improvements in key traits such as yield, and growing ability in tough conditions for world market viability. It is strongly predicted that rapid climate change, newly emerging wheat diseases, and reliance on a small set of wheat varieties will greatly challenge modern day agriculture and food production.The availability of information about wheat genomes and the differences between them (variation) are leading a breakthrough in wheat research. Current services that share information about wheat genomes and these differences give researchers the ability to find regions of interest that match their research goals, and to understand and exploit characteristics of these regions for improving the crop. Such information can then be used in breeding programmes to design genetic markers for traits of interest, akin to marking Points of Interest on a map or navigation system. Once these markers have been discovered, robotic platforms can take this information and can screen thousands of wheat lines a day to look for matches, and hence potential knowledge about how that plant may perform in breeding experiments under different conditions. Tools and resources that harness the power of breeding data and analysis packages, both openly available to academics and industry alike, are key to accelerating wheat breeding programmes in the coming years. There are many web-based databases and information services that exist for housing and exposing wheat data. However, the stages leading up to screening the wheat lines involve intensive and laborious manual processes, and the availability of this information and the way it is represented is not consistent which makes it difficult for researchers and breeders to effectively utilise it for their research. Users must submit information at each step to multiple online or local analysis tools, run multiple queries and analyses, and manually process the results in desktop computer applications to ensure that they can be fed into the next tools in the workflow.Our project will remove these manual steps by developing software to automate the required interactions with commonly used online wheat data resources. As such, we will build software tools that are able to automatically connect each wheat data service in turn to form a workflow, understanding and processing the data produced by a previous service to provide the input data to the next service. This will free up valuable researcher time and, due to the removal of necessary human intervention and management of potentially complex data files, will result in a more robust and reproducible workflow.