A novel core genome approach to enable prospective and dynamic monitoring of infectious outbreaks

van Aggelen, H. C.; Kolde, R.; Chamarthi, H.; Fan, Y.; Fallon, J. T.; Huang, W.; Wang, G.; Fortunato-Habib, M. M.; Carmona, J. J.; Gross, B. D.

doi:10.1101/421388

bioRxiv · 10.1101/421388

A novel core genome approach to enable prospective and dynamic monitoring of infectious outbreaks

van Aggelen, H. C.·Kolde, R.·Chamarthi, H.·Fan, Y.·Fallon, J. T.·Huang, W.·Wang, G.·Fortunato-Habib, M. M.·Carmona, J. J.·Gross, B. D.

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Abstract

Whole-genome sequencing is increasingly adopted in clinical settings to identify pathogen transmissions. Currently, such studies are performed largely retrospectively, but to be actionable they need to be carried out prospectively, in which samples are continuously added and compared to previous samples. To enable prospective pathogen comparison, genomic relatedness metrics based on single nucleotide differences must be consistent across time, efficient to compute and reliable for a large variety of samples. The choice of genomic regions to compare, i.e., the core genome, is critical to obtain a good metric.\n\nWe propose a novel core genome method that selects conserved sequences in the reference genome by comparing its k-mer content to that of publicly available genome assemblies. The conserved-sequence genome is sample set-independent, which enables prospective pathogen monitoring. Based on clinical data sets of 3436 S. aureus, 1362 K. pneumoniae and 348 E. faecium samples, we show that the conserved-sequence genome disambiguates same-patient samples better than a core genome consisting of conserved genes. The conserved-sequence genome confirms outbreak samples with high accuracy: in a set of 2335 S. aureus samples, it correctly identifies 44 out of 45 outbreak samples, whereas the conserved gene method confirms 38 out of 45 outbreak samples.

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van Aggelen, H. C., Kolde, R., Chamarthi, H., Fan, Y., Fallon, J. T., Huang, W., Wang, G., Fortunato-Habib, M. M., Carmona, J. J., Gross, B. D.. 2018-09-19. A novel core genome approach to enable prospective and dynamic monitoring of infectious outbreaks. https://doi.org/10.1101/421388

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