Designing a Diagnostic Tool for the Detection of Viral Diseases in Agricultural Crops

Research question: Can RNA-extraction/amplification protocols, sequencing and accelerated bioinformatics pipelines be developed to provide viral indexing of plant material for diagnostic purposes in crops?

This project would suit a student from either a biological background or a computer science/maths background as the proportion of computing vs biology can be adjusted to suit the applicant.

Background: All types of agricultural crops are susceptible to virus infection, and they contribute to significant economic losses for farmers and food producers. Current detection procedures are slow and expensive. Methods for simultaneous detection of multiple viruses using are currently being developed, and next generation sequencing (NGS) has the potential to be used for this purpose (Boonham et al., 2014). Recently viral disease diagnosis, resulting in actionable clinical management within 48 hours, has been achieved using NGS of human samples (Wilson et al., 2014). Sequencing mixed RNA samples from infected plant material can be used for virus detection, but this technique relies on RNA extraction/amplification protocols, and rapid bioinformatics pipelines that have not been developed for plants. This is the focus of this PhD project.

Project Outline:

1. Investigation of RNA extraction and amplification protocols: In clinical samples there is a linear correlation between number of aligned reads and viral titre (Naccache et al., 2014). We will undertake a number of pilot RNA-sequencing experiments to explore the effect of sampling from different plant tissues, and using different extraction and enrichment protocols. We will test the efficacy of using commercial Flinders Technology AssociatesTM (FTA) technology for in-field sample collection. FTA is a paper based system designed to fix and store nucleic acids from fresh tissue pressed into sample cards. For each extraction protocol we will assess the quality of the RNA extracted. A number of different random amplification protocols will also be tested. Samples from the ‘best’ RNA extraction and amplification protocols will be taken forward for RNA-sequencing experiments.

2. Design and implementation of a bioinformatics pipeline: We will use NGS data from crops, to develop a new plant virus diagnostics pipeline. This will include two key features (a) the use of a k-mer frequency method for rapid virus sequence identification (e.g. Trifonov & Rabadan (2010)) and (b) the use of a speedy alignment tool such as SNAP (Zaharia et al., 2011) to achieve faster alignments of reads to reference genomes where available. The tool will be unique in that it will be specific for plants and plant viruses. The pipeline will be developed using the Galaxy workflow platform (Goecks et al., 2010).

3. Real time application of complete pipeline: With the optimized RNA collection, extraction and amplification protocol and bioinformatics pipeline in place, we will apply it to the viral diagnosis of symptomatic and asymptomatic crop plants, to test its efficacy for real time viral diagnosis.

Outcomes: (i) Robust RNA extraction and enrichment protocols for a variety of crop plants (including blueberry and potato), that provide sufficient viral titre for multiplex viral diagnosis of plant material (ii) A bioinformatics pipeline that takes raw sequence reads and produces a viral diagnostic index. (iii) Real time application of the system to infected and asymptomatic plants to test efficacy.