Comparison of Whole-Genome Sequencing Methods for Analysis of Three Methicillin-Resistant Staphylococcus aureus Outbreaks.
Scott A Cunningham, Nicholas Chia, Patricio R Jeraldo, Daniel J Quest, Julie A Johnson, Dave J Boxrud, Angela J Taylor, Jun Chen, Gregory D Jenkins, Travis M Drucker, Heidi Nelson, Robin Patel
Author Information
Scott A Cunningham: Division of Clinical Microbiology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA.
Nicholas Chia: Department of Surgery, Mayo Clinic, Rochester, Minnesota, USA.
Patricio R Jeraldo: Department of Surgery, Mayo Clinic, Rochester, Minnesota, USA. ORCID
Daniel J Quest: Advanced Analytics and Information Support, Information Technology, Mayo Clinic, Rochester, Minnesota, USA.
Julie A Johnson: Bioinformatics Systems, Information Technology, Mayo Clinic, Rochester, Minnesota, USA.
Dave J Boxrud: Minnesota Department of Health, Saint Paul, Minnesota, USA.
Angela J Taylor: Minnesota Department of Health, Saint Paul, Minnesota, USA.
Jun Chen: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
Gregory D Jenkins: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
Travis M Drucker: Bioinformatics Systems, Information Technology, Mayo Clinic, Rochester, Minnesota, USA.
Heidi Nelson: Department of Surgery, Mayo Clinic, Rochester, Minnesota, USA.
Robin Patel: Division of Clinical Microbiology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA patel.robin@mayo.edu.
Whole-genome sequencing (WGS) can provide excellent resolution in global and local epidemiological investigations of outbreaks. A variety of sequencing approaches and analytical tools have been used; it is not clear which is ideal. We compared two WGS strategies and two analytical approaches to the standard method of SmaI restriction digestion pulsed-field gel electrophoresis (PFGE) for typing Forty-two isolates from three outbreaks and 12 reference isolates were studied. Near-complete genomes, assembled with paired-end and long-mate-pair (8 kb) libraries were first assembled and analyzed utilizing an in-house assembly and analytical informatics pipeline. In addition, paired-end data were assembled and analyzed using a commercial software package. Single nucleotide variant (SNP) analysis was performed using the in-house pipeline. Two assembly strategies were used to generate core genome multilocus sequence typing (cgMLST) data. First, the near-complete genome data generated with the in-house pipeline were imported into the commercial software and used to perform cgMLST analysis. Second, the commercial software was used to assemble paired-end data, and resolved assemblies were used to perform cgMLST. Similar isolate clustering was observed using SNP calling and cgMLST, regardless of data assembly strategy. All methods provided more discrimination between outbreaks than did PFGE. Overall, all of the evaluated WGS strategies yielded statistically similar results for typing.
