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 actives in supporting genomics being used for public health decision making.
Monitoring: Visualising genomic virus signatures
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 , 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.
Based on this work, we have build a freely available visualisation page for tracking the genomic signature and their distances between virus isolates. A GitHub issue tracker is maintained and monitored to allow community volunteers to contribute.
Tracking: 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. Covid Beacon is based on our serverless Beacon (sBeacon) work and it enables 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. A GitHub issue tracker is maintained and monitored to allow community volunteers to contribute.
Diagnostics: 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.