The advancements in Whole Genome Sequencing (WGS) have increased the amount of genomic information available for epidemiological analyses. WGS opens many avenues for investigation into the tracking of pathogens, but the rapid advancements in WGS could soon lead to a situation where traditional analytical techniques might become computationally impractical. For example, the traditional method to determine the origin of an isolate is to use phylogenetic analyses. However, phylogenetic analyses become computationally prohibitive with larger datasets and are best for retrospective epidemiology. Therefore, I investigated if there might be less computationally demanding methods of analysing the same data to obtain similar conclusions. This thesis describes a proof-of-principle method for evaluating if such alternative analysis techniques might be viable. In this thesis Methicillin resistant Staphylococcus aureus (MRSA) was used, and single nucleotide polymorphism (SNP) and insertion/deletion (indel) genomic variation. I move away from whole genome analysis techniques, such as phylogenetic analysis, and instead focus on individual SNPs. I showed that genetic signals (such as SNPs and indels) can be utilised in novel ways to rapidly produce a summary of the possible geographic origin of an isolate with a minimal demand on computational power. The methods described could be added to the suite of analytical epidemiological tools and are a promising indication of the viability of developing cheap, rapid diagnostic tools to be implemented in healthcare institutions. Furthermore, the principles behind the development of the methods described in this thesis could have much wider applications than just MRSA. This implies that further work based on the principles described in this thesis on alternative pathogens could prove to be promising avenues of investigation.
|Date of Award||29 Jun 2016|
|Supervisor||Richard James (Supervisor) & Nicholas Priest (Supervisor)|
- Statistical analysis