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

Project awarded US$100K Inspire Challenge 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 sequenced 30 bacterial genomes and welcomed a new PhD student, Suvra Das, from Bangladesh, to the team. Under Associate Professor Andrew Barnes at the University of Queensland, she will research processing methods for DNA extraction and library preparation to optimize the 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.

March 2020

COVID-19 adaptations

Although the project was affected by the COVID-19 pandemic, the first year’s activities were primarily focused on laboratory and computer-based activities to generate the fish pathogen sequences, and, therefore, the team experienced fewer disruptions than field-based work.

However, various travel and workshop were adapted or postponed. A workshop that was originally planned to take place in person in Bangladesh has been converted to a virtual format and will occur in early 2021.

Watch the video below to hear from WorldFish Scientist Dr. Jerome Delamare-Deboutteville about the impacts of COVID-19 and progress throughout 2020:

June 2020

Generation of aquatic pathogen genomic typing data

The team completed 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 was used to build the reference training database for machine learning algorithms.
  2. Raw nanopore read data for model development. This data was 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

To address the base-call error rate (<5 percent) of the MinION sequencing technology, the team developed a new bioinformatics software package that leverages machine learning to identify fish pathogens.

Two approaches were compared. In the first approach, hidden Markov models (HMMs) were used to compare experimental data to a reference database of hierarchical regions of differentiation. The second approach considered 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 provided a position-specific scoring system that can account for base-calling inaccuracies and were trained on sequences from isoclinal pathogens obtained using the MinION.

Example of an accurate genome sequence.

July 2020 - February 2021

Development of machine learning tools and cloud-based database

The team is creating a cloud-based database that features a large collection of fish pathogen genomes. The point and click user interface will be designed for public use, and the site is expected to be accessible in 2021.

October 2020

Sample collection

In place of in-person training workshops, the team collected samples from researchers at five leading universities and various private sector actors in Malaysia. These samples will be processed and used in an adapted virtual version of the workshop in early 2021.

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

December 2020 - June 2021

Engagement in joint initiative to increase aquaculture sustainability in Sub-Saharan Africa

Working through the WorldFish office in Egypt, the team is engaging 12 Master’s students (six from the College of Basic and Applied Sciences of the University of Ghana and six students from the College of Agriculture & Veterinary Sciences of the University of Nairobi) in a six-month intensive training on general aquaculture.

This effort is a part of a joint project led by WorldFish and the Norwegian Veterinary Institute to support aquatic animal health research, education, and management in Sub-Saharan Africa.

February 2021

Virtual training workshop

Using the samples collected from universities and private sector actors in Malaysia in October 2020, the team led a virtual training workshop on how to process, sequence, and upload the genomic information to the cloud-based database.

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 ...