A bioinformatics tool for the accelerated diagnosis of multiple viral infections in crops using next generation sequencing

在英国，感染作物的病毒在产量和质量方面造成重大损失。在英国，马铃薯收获的产值为每年6.84亿英镑，但由于某些病毒造成的损失估计为3000万英镑。因此，需要快速和准确地鉴定受病毒感染的作物在经济上是重要的，并确保持续供应足够的食物给不断增长的人口。这使得疾病诊断缓慢且昂贵。植物病毒可以从它们的遗传物质中识别出来，通常是RNA。对从受感染植物中提取的样品中所含的遗传物质进行测序是可能的。这种遗传物质包括宿主植物的RNA和感染植物的多种病毒（和其他生物体）的RNA的混合集合。技术可以用来对这种混合的遗传物质进行测序，这提供了一个非常大的数据集，包含数百万个RNA短读段。一个主要困难是识别混合数据集中的病毒序列，以及在足够短的时间内识别病毒序列以成功诊断疾病的能力。理想情况下，我们需要一种软件工具，可以在几天而不是几周内提取RNA样本并生成病毒列表。到目前为止，还没有软件可以在足够短的时间内成功诊断植物病毒。该项目的目的是开发一种软件，该软件将从混合样品中提取数百万个RNA短读段，并产生一个病毒清单，以进行准确诊断，从而可以部署有效的疾病治疗。该软件包括两个要素：（a）识别和删除植物宿主RNA读段;（B）识别已知和潜在的新病毒。在混合RNA样品中鉴定RNA病毒是困难的，因为它们的高序列变异性意味着即使相关序列存在于参考数据库中，差异也可能太大而无法通过比对检测相似性。此外，比对方法（其中短RNA读数针对参考基因组进行比对并组装）对于诊断目的来说太慢。在这个项目中，我们将开发一个生物信息学工具，将克服这两个问题。我们将使用一种称为k-mer计数的方法来识别存在的病毒。RNA序列可以被视为字符串，并被分成多个长度为K的子串。以这种方式，序列可以由k-mer谱表示，并且可以比较这些谱以鉴定混合样品中存在哪些种类。此外，我们将测试使用快速比对器，这将使我们能够更快地识别宿主RNA，如果参考基因组可用。我们将整合这些方法来创建一个管道。该工具将通过Galaxy提供，这是一个用于密集数据分析的开放平台，使研究人员可以广泛使用。它将被设计为由非专家用户使用。该工具将在来自受感染的树莓植物和马铃薯植物材料的RNA序列数据上进行测试。该工具将直接应用于植物健康、检疫和认证程序，用于阻止作物病害的传播。

Viruses that infect crop plants in the UK causes significant losses in terms of yield and quality. In the UK, the production value of the potato harvest is £684 million per year, but the losses due to some viruses are estimated to be £30 million. Hence, the need to quickly and accurately identify virus infected crops is of importance both economically, and to ensure the continued supply of food adequate for a growing population.Current techniques for virus identification only allow the detection of single or at best a very small numbers of related viruses. This makes disease diagnosis slow and expensive. Plant viruses can be identified from their genetic material, which is commonly RNA. It is possible to sequence the genetic material contained within samples extracted from an infected plant. This genetic material comprises a mixed collection of the host plant's RNA and the RNA of multiple viruses (and other organisms) that infect the plant. Technology can be used to sequence this mixed genetic material, which gives a very large data set of millions of short reads of RNA. A major difficulty is the identification of virus sequences within the mixed data set, and the ability to do this in a short enough time period to allow for successful disease diagnosis. Ideally we require a software tool that can take RNA samples and produce a list of viruses present with a matter of days rather than weeks. To date there is no software that can make successful plant virus diagnosis in a sufficiently short timeframe. The aim of the project is to develop software that will take the millions of short reads of RNA from a mixed sample and produce a list of viruses present for accurate diagnosis and so that effective disease treatments can be deployed.The software comprises two elements (a) identification and removal of plant host RNA reads and (b) identification of known and potential new viruses. The identification of RNA viruses in mixed RNA samples is difficult, due to their high sequence variability meaning that even if a related sequence is present in a reference database the differences may be too great to detect the similarity by alignment. In addition alignment methods, in which short RNA reads are aligned against a reference genome and assembled, are too slow for diagnostic purposes. In this project we will develop a bioinformatics tool that will overcome both of these problems. We will use a method known as k-mer counting to identify the viruses present. RNA sequences can be treated as character strings and divided into multiple substrings of length K. In this way a sequence can be represented by k-mer profiles, and these profiles can be compared to identify which species are present in a mixed sample. In addition we will test the use of a speedy aligner that will enable us to identify the host RNA more quickly if a reference genome is available. We will integrate these methods to create a pipeline. The tool will be delivered through Galaxy, an open platform for intensive data analysis, making it widely available to researchers. It will be designed to be used by the non-expert user. The tool will be tested on RNA sequence data from infected raspberry plants and from potato plant material. The tool will have direct applications in plant health, quarantine and certification procedures, used to stop the spread of crop diseases.