The same features of the Genpax analysis that mean that all strains of a species can be compared in one location with a common analysis resource (as opposed to others, such as those that must use multiple reference genomes or where resolution reduces with increasing numbers and diversity of strains), and its open scalability means it will generate equally comparable and integratable findings from information generated and submitted from different laboratories. This is enabled

Genpax analysis was created for the real-world problem from the bottom-up with scalability as an essential requirement. Scalability is a constraint for existing solutions, the three main reasons being: • The statistical nature of relationship determinations that are impossible to scale (they are ‘NP-hard’ problems) • The loss of resolution with increasing diversity and number of compared strains with common genome SNP • Data generated with more than one reference genome when

With some variation by species and genome size, most bacterial pathogens normally change at a rate of up to 10 SNP changes per genome per year. A clinical outbreak spanning weeks or months will include strains diverging at double this rate but also contain strains that differ by less because 99% of strains replicate without error. Meanwhile, sequencing, assembly, and mapping errors can generate tens of errors per MB, resulting in noise that exceeds the essential signal to ide

While the costs and ease of sequencing have been falling, one common obstacle to adoption is the requirement for local genomics staff and infrastructure. High costs of IT staff, bioinformaticians, and genomic epidemiologists (plus sufficient staff to provide continuity of service) and the initial and ongoing costs of computational infrastructure often provide a barrier to entry. Initial capital and equipment costs that are commonly encountered, particularly in resource-limite