Understanding avian influenza variants in New Zealand: a collaborative project between Dragonfly Data Science and the Ministry for Primary Industries
Avian flu
High pathogenicity avian influenza (HPAI)—also known as avian flu–is a viral disease that is affecting domestic and wild birds around the world.
New Zealand remains free from high pathogenicity avian flu, while its low-pathogenic counterpart is endemic within the country. However, the full distribution and genetic structure of the variants present in New Zealand remain largely unknown.
To address this knowledge gap, a new project has been launched by the Ministry for Primary Industries (MPI) and Dragonfly, aimed at sequencing 300 virus isolates collected by MPI since the 1970s.
Building a database of New Zealand variants
This project uses modern genomic sequencing methods to map the genetic landscape of virus variants in New Zealand, providing crucial data on the number of variants, their locations, and their evolution over time.
Dragonfly has been engaged to build a comprehensive searchable database for this project. We are also assisting in creating an automated classification system to quickly identify subtypes from new samples. This system will enhance our understanding of the variants endemic to New Zealand and allow for rapid identification and response in the event of an incursion by a highly-pathogenic variant.
The availability of this detailed database will be invaluable for distinguishing between an imported highly-pathogenic strain and one that may arise from mutation or recombination of existing low-pathogenic variants.
Tools for rapid identification
Given that new pathogenic variants can potentially infect new hosts, including mammals and humans, this project is particularly timely. For instance, a current outbreak in the USA involves a variant (H5N1) infecting cattle.
By employing modern genomics, this project aims to reduce reaction times significantly, streamlining sample processing and data analysis.
Preliminary tests indicate that avian influenza subtypes can be identified within 48 hours, a capability that mirrors similar approaches successfully implemented for COVID-19 samples by ESR.
The technology
MPI uses a Nanopore sequencer, which processes multiple prepared virus samples at the same time. Over several hours, the sequencing programme streams reads of DNA strands from the samples into a folder on an attached computer. These reads can ultimately be assembled into entire virus genomes. But this assembly process is complex and time-consuming. Our challenge was to recognise the particular subtype of virus in real time, quickly alerting us to any highly-pathogenic variants.
We decided to use a k-mer based approach. A k-mer is a short, fixed-size (say 10-30 base pairs) section of overlapping DNA. Any section of DNA can be converted into a set of k-mers, and counting the number of unique k-mers in all the incoming reads produces a k-mer distribution. By comparing it to k-mer distributions from viruses that have already been recognised, we could quickly establish which virus the reads most resembled.
We wrote text user interface (TUI) that screens the incoming files and updates the results in only a few seconds. The programme can be run on the sequencing computer via a remote login, and also works on the New Zealand scientific computing infrastructure (NESI), where MPI stores and processes many of the results. This approach will let us rapidly subtype large databases of flu viruses.
The programme is written in Python, and makes use of a number of open-source packages to produce the user interface and process the k-mers.