Bioinformatics tools for plant genetic resources

现代农业需要对消费者（如风味、形状、质地等）和生产者（如高产、抗虫害）具有改进性能的作物品种，并减少对环境的影响（如减少化肥或农药投入）。利用现代生物技术支持的传统育种，这些发展都是可能的，而不需要转基因（GM）植物。这些改进的特性在“基因库”中发现，基因库是从野生或古老的作物品种中采集的数千种植物样本的集合，以及世界各地数十年选择性育种产生的许多品种。在未来的育种计划中利用这种潜在有用的生物多样性的问题是确定使用哪些样本。解决方案是对每个样品进行“基因指纹”，并对上述所有有用的特性进行精确测量。这些实验原则上可以告诉我们哪些植物可能携带潜在有用的基因。然而，这些大量的潜在有用信息仍然难以使用（数千个样本中的数百个测量值意味着数百万个数据点），因为计算机数据库存储，分析和显示结果的改进落后于我们进行实验室实验的能力。该项目建议通过开发一个功能强大、多功能和可访问的计算机数据库和相关计算工具来弥合这一差距，这些数据库和工具可应用于从作物基因库收集的数据，以确定有希望的植物样本，供进一步的实验分析。所有这些计算资源都将免费提供给世界遗传资源界。

Modern agriculture needs crop varieties with improved performance for the consumer (e.g. flavour, shape, texture etc) and the producer (e.g. high yield, resistance to pests), and reduced environmental impact (e.g. lower fertiliser or pesticide input). These developments are all possible using conventional breeding backed by modern biotechnology, without the need for genetically modified (GM) plants. These improved properties are found in 'genebanks', which are collections of thousands of plant samples taken from the wild or old crop varieties, together with the many cultivars resulting from decades of selective breeding around the World. The problem with harnessing this potentially useful biodiversity in future breeding programmes is working out which samples to use. The solution is to 'genetically fingerprint' every sample and take accurate measurements of all the useful properties mentioned above. These experiments can tell us in principle which plants are likely to carry potentially useful genes. However, this huge quantity of potentially useful information remains difficult to use (hundreds of measurements in thousands of samples means millions of data points), because improvements in computer databases to store, analyse and display the results have lagged behind our ability to do the lab experiments. This project proposes to bridge that gap by developing a powerful, versatile and accessible computer database and associated computational tools, which can be applied to data collected from crop plant genebanks, to identify promising plant samples for further experimental analysis. All of these computational resources will be freely available to the World's genetic resources community.