A new molecular approach to simultaneously detect disease-causing viruses in raspberries and strawberries

Description

The aim of this project is to use high-throughput sequencing (HTS) techniques to develop a process for detecting and identifying viruses (PDIV). The process will combine HTS with innovative bioinformatic analysis tools so that, using a single analysis, plant pathologists will be able to receive, via a user-friendly interface, a quantitative verdict regarding the presence and identity of all viruses infecting the sample.

To ensure the accurate detection and identification of all raspberry and strawberry viruses, the project will develop

• a benchmark collection of viral sequences derived from the genome for each known virus, and
• a collection of asymptomatic samples and symptomatic samples infected with one or more viruses.

A database with all the HTS sequences will be linked to a database of the agronomic, environmental, and diagnostic parameters for each sample. During trials, these sequence collections and databases will be used to demonstrate the superior diagnostic efficacy of the PDIV compared to reference protocols used by Laboratoire d’expertise et de diagnostic en phytoprotection (LEDP).
An economic analysis will compare the costs of implementing and utilizing the LEDP and PDIV tests, and the benefits derived from using the PDIV.

Objective(s)

• Develop a fast and sensitive molecular detection methodology able to accurately identify raspberry and strawberry viruses. This process for detecting and identifying viruses (PDIV), by combining high-throughput sequencing techniques with innovative bioinformatic analysis and machine learning tools, will provide plant pathologists with a quantitative verdict and recommendations.
• Develop a collection of virus-infected samples and genomic sequences for each targeted virus.
• Compare the effectiveness of current diagnostic methods with that of the PDIV.
• Compare the costs of utilizing and implementing current diagnostic methods and the PDIV.
• Train the staff at MAPAQ Laboratoire d’expertise et de diagnostic en phytoprotection and other potential users in the utilization of the PDIV, its database, and its user-friendly interface.