Rapid in-field Nanopore-based identification of plant and animal pathogens

Airborne crop pathogens pose a serious threat to food security and are responsible for devastating loss of yield and over-reliance on pesticides. Early detection enables farmers to take prophylactic action, drastically reducing damage and cost. Current detection regimes often rely on expert identification of the pathogen from plant damage. More recently, techniques have emerged utilising PCR or antibody-based assays. However, these methods suffer the same problems - being specific for a single species and a need for relatively large quantities of pathogenic material. Recently, TGAC has been working on an approach dubbed Air-seq that seeks to identify pathogens through sequencing of biological particles present in air. This overcomes both problems associated with current techniques as it is unbiased and requires very small quantities of material. Our ultimate aim is to put sample collection, sequencing and analysis in a single box that can be deployed in the field. Key to success is a compact sequencing technology and this has recently emerged in the form of Oxford Nanopore Technologies' (ONT) MinION. The MinION is a compact, low cost single molecule sequencing technology that offers multi-kilobase reads and a streamed mode of operation enabling analysis of data as it is generated. These attributes make it ideally suited to in-field use. However, ONT's basecalling utilises a cloud-based system in which pore electrical signal data is uploaded and basecalled sequence downloaded. For in-field deployment, this is unsatisfactory, as we cannot rely on high bandwidth data connections. We believe a completely new approach is required in which we utilise the raw signal data in order to identify species, instead of searching against basecalled sequence. In this project, we will develop a tool that searches Nanopore signal data looking for the characteristic signal traces of pathogens of interest, building up a report on abundance levels in the process.