Innovation – Open view page
General information
| Innovation ID | 249 |
| Version ID | 183 |
| Innovation Title | Rapid genomic detection of aquaculture pathogens |
| Innovation description | Tilapia is a major contributor to fish supplies and livelihoods, so diseases in tilapia are an important concern. Early detection of pathogens and their genetic variations are critical to inform disease management and control measures. By leveraging portable sequencing technology and supervised machine learning classification, we develop a genome-wide analysis tool enabling real-time fish pathogens diagnostics and timely investigation of the origins and spread of disease outbreaks. |
| Reporting Staff | Jerome Delamare-Deboutteville (WF) |
| Year (Reporting) | 2019 |
| Reporting status | Approved |
| Innovation Type | Research and Communication Methodologies and Tools |
Stage reached
| Stage of Innovation | Stage 1: discovery/proof of concept |
| Year (Stage) | 2019 |
| Stage Description | We have collected two types of genomic data from 25 bacterial isolates: 1) highly accurate sequence data derived from long and short read sequencing that will be used for building the reference training database for machine learning algorithms 2) raw nanopore read data from the same pathogens for model development |
| Has a lead organization | yes |
| Lead Organization | WorldFish |
| Top 5 contributors | University of Exeter; University of Queensland; Wilderlab NZ Ltd; Centre for Environment, Fisheries and Aquaculture Science; Mahidol University, Faculty of science, Center of Excellence for Shrimp Molecular Biology and Biotechnology |
Contributions and mapping
| All partners | University of Queensland-Academic Institutions (universities, colleges, etc.); WorldFish-CGIAR Center/Program (Center) |
| Main CRP | CGIAR Research Program on Fish Agri-Food Systems |
| Flagship project | FP1-Sustainable aquaculture |
| Cluster | FP1-2-Fish health, nutrition and feeds |
| Other CRPs-Flagships-Clusters | Big Data in Agriculture,MDL-3-Inspire |
Scope
| Geographic scope | Global |
| Regions | |
| Countries |
Targeted outcomes
| Main Sub-IDO | Reduced livestock and fish disease risks associated with intensification and climate change |
| Other Sub-IDO | Increased capacity for innovation in partner development organizations and in poor and vulnerable communities |
| Other Sub-IDO | Reduced production risk |
Evidences
| Evidences | Lab-in-a-backpack: Rapid Genomic Detection to revolutionize control of disease outbreaks in fish farming [http://blog.worldfishcenter.org/2020/03/lab-in-a-backpack-rapid-genomic-detection-to-revolutionize-control-of-disease-outbreaks-in-fish-farming/] 2019 WINNER Rapid genomic detection of aquaculture pathogens [https://bigdata.cgiar.org/inspire/inspire-challenge-2019/rapid-genomic-detection-of-aquaculture-pathogens/] Delamare-Deboutteville, J. Barnes, A., 2019. Rapid genomic detection of aquaculture pathogens. [https://hdl.handle.net/20.500.12348/3826] "Jerome Delamare-Deboutteville, Andrew Barnes. (10/10/2019). Rapid genomic detection of aquaculture pathogens. Malaysia: WorldFish (WF)." |
Linked Elements
| Milestones |
| Outcome Impact Case |
| Policy |