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，每年马铃薯收获的产值为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，需要快速和准确地识别受病毒感染的作物，这在经济上和确保持续为不断增长的人口提供足够的粮食都很重要。目前的病毒鉴定技术只能检测到单个或最多只能检测到非常少量的相关病毒。这使得疾病诊断缓慢而昂贵。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本中所含的遗传物质进行排序是可能的。这种遗传物质包括寄主植物的RNA和感染该植物的多种病毒（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，这就提供了一个非常大的数据集，包含数百万个RNA的短读。一个主要的困难是在混合数据集中识别病毒序列，以及在足够短的时间内做到这一点以允许成功诊断疾病的能力。理想情况下，我们需要一种软件工具，它可以采集RNA样本，并在几天而不是几周内生成一份病毒清单。到目前为止，还没有一种软件能够在足够短的时间内成功地诊断出植物病毒。该项目的目的是开发一种软件，可以从混合样本中提取数百万个短读RNA，并生成一份病毒清单，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)鉴定和去除植物宿主RNA读段和(b)鉴定已知和潜在的新病毒。在混合RNA样本中鉴定RNA病毒是困难的，因为它们的序列高度可变性，这意味着即使参考数据库中存在相关序列，差异也可能太大，无法通过比对来检测相似性。此外，将短RNA序列与参考基因组比对并组装的比对方法对于诊断目的来说太慢了。在这个项目中，我们将开发一种生物信息学工具来克服这两个问题。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，并被分成长度为k的多个子字符串。这样，一个序列可以用k-mer谱表示，这些谱可以比较，以确定哪些物种存在于混合样本中。此外，我们将测试一种快速比对器的使用，如果有参考基因组，它将使我们能够更快地识别宿主RNA。我们将集成这些方法来创建一个管道。该工具将通过用于密集数据分析的开放平台Galaxy提供，使研究人员可以广泛使用。它将被设计为非专业用户使用。该工具将在受感染的覆盆子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卫生、检疫和认证程序，用于阻止作物疾病的传播。

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.