{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:45:22Z","timestamp":1761864322945,"version":"3.40.5"},"reference-count":54,"publisher":"Microbiology Society","issue":"8","license":[{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>Whole-genome sequencing is becoming the <jats:italic>de facto<\/jats:italic> standard for bacterial outbreak surveillance and infection prevention. This is accompanied by a variety of bioinformatic tools and needs bioinformatics expertise for implementation. However, little is known about the concordance of reported outbreaks when using different bioinformatic workflows. In this multi-centre proficiency testing among 13 major Dutch healthcare-affiliated centres, bacterial whole-genome outbreak analysis was assessed. Centres who participated obtained two randomized bacterial datasets of Illumina sequences, a <jats:italic>\n                     <jats:named-content content-type=\"species\">\n                        <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/doi.org\/10.1601\/nm.10789\" xlink:type=\"simple\">Klebsiella pneumoniae<\/jats:ext-link>\n                     <\/jats:named-content>\n                  <\/jats:italic> and a Vancomycin-resistant <jats:italic>Enterococcus faecium,<\/jats:italic> and were asked to apply their bioinformatic workflows. Centres reported back on antimicrobial resistance, multi-locus sequence typing (MLST), and outbreak clusters. The reported clusters were analysed using a method to compare landscapes of phylogenetic trees and calculating Kendall\u2013Colijn distances. Furthermore, fasta files were analysed by state-of-the-art single nucleotide polymorphism (SNP) analysis to mitigate the differences introduced by each centre and determine standardized SNP cut-offs. Thirteen centres participated in this study. The reported outbreak clusters revealed discrepancies between centres, even when almost identical bioinformatic workflows were used. Due to stringent filtering, some centres failed to detect extended-spectrum beta-lactamase genes and MLST loci. Applying a standardized method to determine outbreak clusters on the reported <jats:italic>de novo<\/jats:italic> assemblies, did not result in uniformity of outbreak-cluster composition among centres.<\/jats:p>","DOI":"10.1099\/mgen.0.000612","type":"journal-article","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T14:17:46Z","timestamp":1628259466000},"source":"Crossref","is-referenced-by-count":8,"title":["Centre-specific bacterial pathogen typing affects infection-control decision making"],"prefix":"10.1099","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0443-6250","authenticated-orcid":false,"given":"Jordy P. 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