Rapid in-field Nanopore-based identification of plant and animal pathogens

气传作物疾病对粮食安全构成严重威胁，并造成毁灭性的产量损失和对杀虫剂的过度依赖。及早发现使农民能够采取预防措施，大大减少损失和成本。目前的检测制度通常依赖于专家对植物损害的病原体进行鉴定。最近，其他分子技术也出现了。然而，这些方法面临着同样的问题--只针对单一物种，需要相对大量的致病物质。最近，TGAC一直在研究一种名为Air-Seq的方法，该方法寻求通过对空气中存在的生物颗粒进行测序来识别病原体。这克服了与当前技术相关的两个问题，因为它是无偏见的(不受物种的限制)，并且需要非常少量的材料。我们的最终目标是将样本收集、测序和分析放在一个可以在现场部署的盒子里。成功的关键是紧凑的测序技术，这是最近以牛津纳米孔技术(ONT)MinION的形式出现的。Minion是一种新的紧凑、低成本的测序技术，提供长时间读取(数千个DNA碱基)和流操作模式，使数据能够在生成时进行分析。这些特性使其非常适合现场使用。然而，产生测序数据的过程的一部分涉及将来自DNA传感小孔的电信号转换成碱基(字母)序列，这是通过互联网的“基本呼叫”服务来执行的。对于现场部署，这是不能令人满意的，因为我们不能依赖高速、可靠的数据连接。我们认为需要一种全新的方法，利用原始信号数据来鉴定物种，而不是根据碱基序列进行搜索。在这个项目中，我们将开发一个工具，搜索纳米孔信号数据，寻找感兴趣的病原体的特征信号痕迹，建立过程中丰度水平的报告。

Airborne crop diseases pose a serious threat to food security and are responsible for devastating loss of yield and over-reliance on pesticides. Early detection enables farmers to take preventative action, drastically reducing damage and cost. Current detection regimes often rely on expert identification of the pathogen from plant damage. More recently, other molecular techniques have emerged. However, these methods suffer the same problems - being specific for a single species and a need for relatively large quantities of pathogenic material. Recently, TGAC has been working on an approach dubbed Air-seq that seeks to identify pathogens through sequencing of biological particles present in air. This overcomes both problems associated with current techniques as it is unbiased (not limited by species) and requires very small quantities of material. Our ultimate aim is to put sample collection, sequencing and analysis in a single box that can be deployed in the field. Key to success is a compact sequencing technology and this has recently emerged in the form of Oxford Nanopore Technologies' (ONT) MinION.The MinION is a new compact, low-cost sequencing technology that offers long reads (thousands of bases of DNA) and a streamed mode of operation enabling analysis of data as it is generated. These attributes make it ideally suited to in-field use. However, part of the process of generating sequencing data involves converting an electrical signal from the DNA sensing pore into a sequence of bases (letters) and this is performed via an internet 'basecalling' service. For in-field deployment, this is unsatisfactory, as we cannot rely on high speed, reliable data connections. We believe a completely new approach is required in which we utilise the raw signal data in order to identify species, instead of searching against basecalled sequence.In this project, we will develop a tool that searches Nanopore signal data looking for the characteristic signal traces of pathogens of interest, building up a report on abundance levels in the process.