{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T16:06:25Z","timestamp":1789574785898,"version":"build-2803163510"},"reference-count":52,"publisher":"Microbiology Society","issue":"9","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"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":[[2026,9,16]]},"abstract":"<jats:p>\n                    Sequence-based typing (SBT) via Sanger sequencing has been the standard for describing\n                    <jats:italic>Legionella pneumophila<\/jats:italic>\n                    relatedness for two decades. SBT involves sequencing seven loci, identifying alleles using the United Kingdom Health Security Agency database and inferring the corresponding sequence type (ST). While similar SBT approaches for other organisms can be easily adapted to whole-genome sequencing (WGS),\n                    <jats:italic>L. pneumophila<\/jats:italic>\n                    presents two challenges for this adaptation: multiple copies of one locus (\n                    <jats:italic>mompS<\/jats:italic>\n                    ) and extensive heterogeneity in a second locus (\n                    <jats:italic>neuA\/neuAh<\/jats:italic>\n                    ). Although several computational methods have been proposed to address these issues, a WGS-based replacement with equal resolution to traditional SBT has been elusive. To address this gap, we developed el_gato (\n                    <jats:bold>E<\/jats:bold>\n                    pidemiology of\n                    <jats:italic>\n                      <jats:bold>L<\/jats:bold>\n                      egionella\n                    <\/jats:italic>\n                    :\n                    <jats:bold>G<\/jats:bold>\n                    enome-b\n                    <jats:bold>A<\/jats:bold>\n                    sed\n                    <jats:bold>T<\/jats:bold>\n                    yping;\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/CDCgov\/el_gato\" xlink:type=\"simple\">https:\/\/github.com\/CDCgov\/el_gato<\/jats:ext-link>\n                    ), which offers several advantages over existing methods: (1) a novel approach for resolving multiple\n                    <jats:italic>mompS<\/jats:italic>\n                    alleles identified in the same isolate, (2) the ability to capture diverse\n                    <jats:italic>neuA\/neuAh<\/jats:italic>\n                    alleles, (3) fast single-threaded execution with an average of ~27 s per sample, (4) easy installation via Bioconda or Docker\/Singularity and (5) an updated database as of May 2026. el_gato works with either paired-end short reads or genome assemblies, performing more accurately with paired-end short reads at least 250 bp in length. We compared el_gato against two other\n                    <jats:italic>in silico<\/jats:italic>\n                    SBT tools (\u2018mompS\u2019, hereafter referred to as the mompS tool and \u2018legsta\u2019) using a dataset of 441 isolates with STs previously determined by Sanger sequencing. el_gato correctly identified the ST for 98.9% of the test isolates, compared to 95.2% for the mompS tool and 42.2% for legsta, demonstrating a significant improvement compared to the mompS tool (adjusted\n                    <jats:italic>P<\/jats:italic>\n                    =2.48\u00d710\n                    <jats:sup>\u22123<\/jats:sup>\n                    ) and legsta (adjusted\n                    <jats:italic>P<\/jats:italic>\n                    =9.90\u00d710\n                    <jats:sup>\u221255<\/jats:sup>\n                    ) in ST identification. Furthermore, el_gato\u2019s determination of ST was not significantly different from Sanger sequencing (adjusted\n                    <jats:italic>P<\/jats:italic>\n                    =1.00). In summary, el_gato improves\n                    <jats:italic>in silico<\/jats:italic>\n                    SBT and, given its performance, is poised to support the public health community.\n                  <\/jats:p>","DOI":"10.1099\/mgen.0.001822","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T15:15:48Z","timestamp":1789571748000},"source":"Crossref","is-referenced-by-count":0,"title":["\u2018Epidemiology of \n               L\n            \n            egionella: Genome-bAsed Typing\u2019 (el_gato) \u2013 a new bioinformatic tool for identifying sequence-based types of Legionella pneumophila from whole-genome sequencing data"],"prefix":"10.1099","volume":"12","author":[{"given":"Alan J.","family":"Collins","sequence":"first","affiliation":[{"name":"Applied Bioinformatics Laboratory (ASRT, Inc.), Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8140-9468","authenticated-orcid":false,"given":"Dev","family":"Mashruwala","sequence":"additional","affiliation":[{"name":"Applied Bioinformatics Laboratory (ASRT, Inc.), Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasanta","family":"Chivukula","sequence":"additional","affiliation":[{"name":"Applied Bioinformatics Laboratory (ASRT, Inc.), Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalia A.","family":"Kozak-Muiznieks","sequence":"additional","affiliation":[{"name":"Division of Bacterial Diseases, Centers for Disease Control and Prevention, Atlanta,, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lavanya","family":"Rishishwar","sequence":"additional","affiliation":[{"name":"Applied Bioinformatics Laboratory (ASRT, Inc.), Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily T.","family":"Norris","sequence":"additional","affiliation":[{"name":"Applied Bioinformatics Laboratory (ASRT, Inc.), Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melisa J.","family":"Willby","sequence":"additional","affiliation":[{"name":"Division of Bacterial Diseases, Centers for Disease Control and Prevention, Atlanta,, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7037-134X","authenticated-orcid":false,"given":"Jennafer A.P.","family":"Hamlin","sequence":"additional","affiliation":[{"name":"Division of Bacterial Diseases, Centers for Disease Control and Prevention, Atlanta,, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Will A.","family":"Overholt","sequence":"additional","affiliation":[{"name":"Applied Bioinformatics Laboratory (ASRT, Inc.), Atlanta, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"345","reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-90-4-656"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM197712012972201"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM197712012972202"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.1128\/aem.41.1.9-16.1981"},{"key":"R5","volume-title":"A Consensus Study Report of the National Academies of Sciences, Engineering, Medicine","year":"2020"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2024.1415157"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.1038\/srep33998"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.1128\/aem.50.5.1128-1131.1985"},{"key":"R9","doi-asserted-by":"publisher","DOI":"10.1164\/arrd.1982.126.6.1050"},{"key":"R10","article-title":"Legionnaire\u2019s Disease Surveillance Summary Report, United States 2018-2019","author":"Barskey","year":"2022"},{"key":"R11","article-title":"Legionnaires\u2019 disease - 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