COVID19 Genomics

The genome of the SARS-CoV-2 virus is emerging as key information source for outbreak tracing, forecasting how the virus mutates and clinical decision making. This page summaries our act-ivies in supporting genomics being used for public health decision making.


H&B’s eHealth research team has developed a new bioinformatics approach to support the Australian Centre for Disease Preparedness in choosing the right viruses isolate for testing vaccine candidates. Accepted in Transboundary and Emerging Diseases Journal [1], the approach combines large volumes of internationally available virus genomes and machine learning with laboratory observations and epidemiological insights to choose an isolate that is representative of the currently circulating and likely emerging versions of the virus. It embodies CSIRO’s strength of moving at speed while honouring the scientific process of peer reviewed, transparent publications.

Visualising genomic virus signatures

Based on this work, we have build a freely available visualisation page for tracking the genomic signature and their distances between virus isolates.

Figure 1: Webpage for viewing the genomic distances of all GISAID entries in a Phylogenetic-tree-free approach

Robust sharing and continuously analysing genomic data

Based on the difficulties we experienced in doing the genomic signature work, we propose a cloud-based architecture for sharing and continuously analysing SARS-Cov-2 sequences. The concept is based on our serverless Beacon (sBeacon) work and will enable the sharing of insights without having to give up ownership or access control of the contributed data itself. The cloud-native architecture allows the economical scaling to potentially millions of data-points and provides an appropriate environment for highly sensitive clinical data.

Figure 2: Cloud-native scalable and privacy preserving framework for analysing COVID-19 data

CRISPR-based Diagnostics

Based on our CRISPR target-site detection tools, we built a webpage for designing CRISPR-targets that are able to differentiate between similar viruses that would form false negatives and combine variations that should be flagged as positives.

Figure 3: SAUTE: Webservice for optimising CRISPR-diagnostics sgRNAs


[1] Bauer et al. Supporting pandemic response using genomics and bioinformatics: a case study on the emergent SARS-CoV-2 outbreak Transboundary and Emerging Diseases 2020