Are there changes in the virus during the attenuation process that can be used to improve future vaccines?

Using deep learning (DL) to identify patterns of attenuation in existing deep sequencing datasets

We will use a novel supervised deep learning tools to identify patterns in existing deep sequencing viral genome datasets to identify patterns of mutations in either or both disease causing and vaccine viruses. We will use pre-existing serological and molecular data to define labels for machine learning. Using bioinformatics techniques, we will characterise regions of secondary, tertiary and protein coding structures to identify areas that may play roles in pathogenicity.  This will be compared with field data from different pathogenic IBV viruses to explore differences between different viral serotypes.

Anticipated outcomes

WP1 will use DL to identify patterns present in virus genetic material that are shared between attenuated or disease-causing viruses. We will correlate these predictions with bioinformatics analysis characterising location relative to secondary and tertiary structure and genetic distance from known pathogenic IBV isolates.