Rapid genomic detection of aquaculture pathogens

Abstract

Aquaculture is the world’s fastest growing food sector increasingly and is recognized for its potential to alleviate poverty and hunger in small-scale systems. However, progress is limited by diseases and lack of knowledge and tools to identify fish pathogens, track their origin and manage their spread. Whole genome sequencing informs how pathogens change and move through environments, permitting implementation of evidence-based biosecurity to minimize disease impact. Offsite sequencing services are expensive and cause prohibitive delays. The project proposes leveraging offline supervised machine learning associated with the MinION portable sequencing device for low-cost diagnostics of fish pathogens in remote locations, allowing real-time disease investigation and data-driven management.