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

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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.