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Life sciences · Journal article
Microbial Genomics · September 16, 2026
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Sequence-based typing (SBT) via Sanger sequencing has been the standard for describing Legionella pneumophila 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), L. pneumophila presents two challenges for this adaptation: multiple copies of one locus ( mompS ) and extensive heterogeneity in a second locus ( neuA/neuAh ). 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 ( E pidemiology of L egionella: G enome-b A sed T yping; https://github.com/CDCgov/el_gato ), which offers several advantages over existing methods: (1) a novel approach for resolving multiple mompS alleles identified in the same isolate, (2) the ability to capture diverse neuA/neuAh 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 in silico SBT tools (‘mompS’, hereafter referred to as the mompS tool and ‘legsta’) 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 P =2.48×10 −3 ) and legsta (adjusted P =9.90×10 −55 ) in ST identification. Furthermore, el_gato’s determination of ST was not significantly different from Sanger sequencing (adjusted P =1.00). In summary, el_gato improves in silico SBT and, given its performance, is poised to support the public health community.