2019 Winner

Rapid genomic detection of aquaculture pathogens

Malaysia, Bangladesh

The Inspire Challenge is an initiative to challenge partners, universities, and others to use CGIAR data to create innovative pilot projects that will scale. We look for novel approaches that democratize data-driven insights to inform local, national, regional, and global policies and applications in agriculture and food security in real time; helping people–especially smallholder farmers and producers–to lead happier and healthier lives.

This proposal was selected as a 2019 winner, with the team receiving 100,000 USD to put their ideas into practice.

Aquaculture is the world’s fastest growing food sector in the world and has been recognized for its potential to alleviate poverty and hunger. However, fish diseases and a lack of how to identify, track, and contain them can prohibit aquaculture development. This project will pilot a transportable, low-cost diagnostic “lab-in-a-backpack” that will enable users without molecular biology experience to identify fish pathogens in real-time with limited electricity and internet connectivity.

Aquaculture, the farming of aquatic organisms in both coastal and inland areas, accounts for 50 percent of the world’s fish that is used for food today. It is practiced by both some of the poorest farmers in developing countries and by multinational companies.

However, development of aquaculture systems is often limited by fish diseases and a 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. Therefore, 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.

The project will pilot a readily deployable “lab-in-a-backpack” for pond-side identification and quantitation of pathogens affecting tilapia. Equipped with a portable DNA-extraction system, a hand-held DNA sequencer (MinION), a battery-operated minicomputer (MinIT), and an intuitive purpose-built software package, users without experience in molecular biology or bioinformatics will be able to identify fish pathogens from both water samples and infected tissues remotely and in real-time, with limited electricity and internet connectivity.

These tools will enable tilapia breeding, quarantine, and biosecurity centers, as well as academics and vets, to identify causal agents of disease outbreaks in a fraction of the time and cost required for external laboratory analysis; the project’s tests give results in hours rather than weeks or months and cost roughly 40 USD as opposed to more than 100 USD.

Learn more about the project in this WorldFish video:

Core team




Project Partners

Step by step

Oct 2019

US$100K grant

The project was one of four winners of the Inspire Challenge 2019 and was awarded US$100K at the Convention of the CGIAR Platform for Big Data in Agriculture, during 16-18 October, 2019.

January 2020

Bacterial genomes sequencing and expansion of the team

The team has sequenced 30 bacterial genomes and welcomed a new PhD student, Suvra Das, from Bangladesh, to the team.

Under the primary supervision of Associate Professor Andrew Barnes at the University of Queensland, and co-supervision of Dr Jerome Delamare-Deboutteville at WorldFish, and Dr Shaun Wilkinson from Wilderlab, Suvra  will research processing methods for DNA extraction and library preparation to optimize cost and performance of field sequencing tests.

Suvra Das, a PhD student in Andrew Barnes’ aquatic animal health laboratory, performs DNA extraction from fish pure bacterial isolates.

June 2020

Generation of aquatic pathogen genomic typing data

The team will complete 50 bacterial genome sequences, generating two types of data:

  1. Highly accurate sequence data for all target aquatic pathogens derived from long and short read sequencing that will be used to build the reference training database for machine learning algorithms.
  2. Raw nanopore read data for model development. These data will be generated at the University of Queensland, Mahidol University / BIOTEC’s CENTEX Shrimp, and WorldFish.

Nurulhuda Ahmad Fatan from WorldFish demonstrates how to load a library onto the flow cell before starting a sequencing run on the Minion connected to the MinIT.

July 2020

Optimisation of field data acquisition and upload methodology

The team will  compare sample collection and processing methods to optimise cost and performance of the field sequencing workflows.

Sample extraction and library preparation and indexing methods will be compared to ensure that they can be completed in semi-remote locations.

Essential equipment needed to take single colonies from blood agar plates, extract DNA, and prepare a library for sequencing on the Minion, MinIT, and computer (or tablet or mobile phone) to visualise dashboard and launch a sequencing run.

July 2020

Building a software environment for typing pathogens from fuzzy data

Additionally, as a way of addressing the base-calling error rate (<5 percent) of the MinION sequencing technology, the team will develop a new bioinformatics software package that leverages machine learning to identify fish pathogens.

Example of an accurate genome sequence.

Two approaches will be compared. In the first approach, hidden Markov models (HMMs) will be used to compare experimental data to a reference database of hierarchical regions of differentiation. The second approach considers that all genomic regions provide information on strain type. Therefore, a rapid alignment method can be used to bin query samples probabilistically with the correct strain or type.

These models provide a position-specific scoring system that can account for base-calling inaccuracies and will be trained on sequences from isoclonal pathogens obtained using the MinION.

This pipeline is already under development and will be made publicly available as an open-source R package and R Shiny GUI on GitHub and the Comprehensive R Archive Network (CRAN) upon the completion of satisfactory reference bench-marking.

August 2020

Developing a manual and training modules for field samplers

The team will build training modules for field-based samplers composed of factsheets, short video tutorials, and easy-to-follow protocols for end-user software interface for the data-outputs.

The manual will cover the entire process from biological sample collection to performing a sequencing run for analysis.

A field team prepares to collect samples from fish for disease diagnostic investigation.

October 2020

Mid-scale field deployment and testing

Community engagement sessions will be organized with farmers and health specialists in Bangladesh and/or Malaysia to promote the benefits of the technology.

Field sampling kits and data upload interface will be deployed on a farm experiencing an outbreak caused by an know bacterial disease by a known disease to showcase the technology and answer a defined epidemiological research question.

Basic fish dissection for sample collection.

Project News and Resources

Lab-in-a-backpack: Rapid Genomic Detection to revolutionize control of disease outbreaks in fish farming

Lab-in-a-backpack: Rapid Genomic Detection to revolutionize control of disease outbreaks in fish farming

A winning 2019 Inspire Challenge project led by WorldFish, the University of Queensland, and Wilderlab is revolutionizing aquaculture disease control ...
VIDEO: Q&A with Inspire Challenge winner: Rapid genomic detection of aquaculture pathogens

VIDEO: Q&A with Inspire Challenge winner: Rapid genomic detection of aquaculture pathogens

Live Q&A with Jérôme Delamare-Deboutteville (WorldFish), Andrew Barnes (University of Queensland), and Shaun Wilkinson (Wilderlab) about their 2019 Inspire Challenge ...