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

小麦是世界上种植最广泛的农作物，为不断增长的人口提供20%的卡路里。据估计，平均每人每天将消耗50个小麦植株(https://www.jic.ac.uk/calculations/)，)的谷物，为了支持这一点，英国每年向全球20多个国家出口15%-20%(约200万吨)的作物[1]，并供应英国市场。在过去十年中，对育种计划的研究在产量等关键性状以及在恶劣条件下为世界市场生存而增长的能力方面取得了重大改进。人们强烈预测，快速的气候变化、新出现的小麦病害以及对少数小麦品种的依赖将极大地挑战现代农业和粮食生产。有关小麦基因组的信息及其差异(变异)正在引领小麦研究的突破。目前共享小麦基因组信息和这些差异的服务使研究人员能够找到与他们的研究目标相匹配的感兴趣区域，并了解和利用这些区域的特征来改进作物。然后，这些信息可以用于育种计划，为感兴趣的特征设计遗传标记，类似于在地图或导航系统上标记感兴趣的点。一旦发现了这些标记，机器人平台就可以利用这些信息，每天筛选数千个小麦品系来寻找匹配，从而潜在地了解这种植物在不同条件下的育种实验中的表现。利用育种数据和分析程序包的力量的工具和资源，对学术界和工业界都是公开可用的，是在未来几年加快小麦育种计划的关键。有许多基于网络的数据库和信息服务，用于存储和公布小麦数据。然而，筛选小麦品系的各个阶段涉及密集而费力的人工过程，而且这些信息的可用性和表示方式并不一致，这使得研究人员和育种者很难有效地将其用于他们的研究。用户必须在每个步骤将信息提交到多个在线或本地分析工具，运行多个查询和分析，并在桌面计算机应用程序中手动处理结果，以确保它们可以提供给工作流程中的下一个工具。我们的项目将通过开发软件来自动化与常用在线小麦数据资源的必要交互，从而消除这些手动步骤。因此，我们将构建能够依次自动连接每个小麦数据服务的软件工具，以形成工作流，理解和处理前一个服务产生的数据，以向下一个服务提供输入数据。这将解放宝贵的研究人员时间，由于消除了必要的人为干预和潜在复杂数据文件的管理，将产生更强大和可重复的工作流程。

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.