{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T18:28:10Z","timestamp":1786472890127,"version":"build-2736575974"},"reference-count":54,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Genetic characterization of pathogen isolates is increasingly important in healthcare settings, yet the bioinformatic workflows required can be complex and resource-intensive. Here we analyzed 19 environmental\n                    <jats:italic>Legionella pneumophila<\/jats:italic>\n                    isolates using whole genome sequencing (WGS) and compared results from an established multi-step bioinformatic pipeline with several accessible, single-step analysis tools. All approaches produced consistent clustering patterns, resolving the isolates into two major genetic clusters. No clear association between spatial sampling distance and genetic relatedness was observed within this dataset: one cluster was detected in two settlements 50 km apart, while both clusters also co-occurred on a single hospital floor. Across this dataset, all WGS-based approaches provided clustering resolution broadly comparable to MLST\/cgMLST. These findings indicate that accessible WGS analysis tools can reproduce the main genomic relationships inferred by more complex workflows while reducing analytical complexity and required bioinformatic expertise. Based on these observations, we discuss how rapid low-complexity WGS workflows may support future same-day bacterial isolate characterization in applied settings.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0355944","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T17:29:01Z","timestamp":1786469341000},"page":"e0355944","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rapid and consistent genetic clustering of environmental Legionella pneumophila isolates using accessible single-step WGS analysis 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