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Interpreting whole genome data

Describe the data (Output from BIGsDB) - What do you think it is telling us?

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Describe the data (Output from BIGsDB) - What do you think it is telling us?

  • The results of the analysis are visualised as networks; note that the output from the genomic analysis is not presented as categorical data but that the relationships between the strains are displayed as continuous data. The larger the number of genes, the higher the resolution. The approach therefore allows focusing in or out to assess different degrees of relatedness.

  • The “outbreak” in Hall A involves two meningococcal strains (see the nodes at the bottom of the diagram), the strain responsible for the first case was also identified from a close contact.

  • The strains isolated from cases 3 and 6 are closely related to each other – the outbreak in the wider university is linked to this strain.

  • The two cases of meningococcal disease occurring in two nearby towns are caused by strains highly related to the strains isolated from cases 3 and 6 – using this gene-based genomics approach has enabled the identification of transmission networks. Collecting this type of data from other clusters will provide a more detailed picture of meningococcal disease transmission and provide evidence on which to base guidance / policy.

  • The strains isolated from carriers are much more distant from the disease strains, but at least one is related to a disease-causing reference strain. This shows that there were a number of different meningococcal strains circulating in the University at the time of the outbreak. The clustering of carriage strains with a disease causing strain may suggest that the carried strains have the potential to cause serious disease, although greater experience is needed to make reliable interpretations regarding predicted virulence.

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