{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T05:05:24Z","timestamp":1791867924887,"version":"4.3.4"},"reference-count":40,"publisher":"Microbiology Society","issue":"11","license":[{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,11,1]]},"abstract":"<jats:p>\n                    Assembly of bacterial short-read whole-genome sequencing data frequently results in hundreds of contigs for which the origin, plasmid or chromosome, is unclear. Complete genomes resolved by long-read sequencing can be used to generate and label short-read contigs. These were used to train several popular machine learning methods to classify the origin of contigs from\n                    <jats:italic>Enterococcus faecium<\/jats:italic>\n                    ,\n                    <jats:italic>Klebsiella pneumoniae<\/jats:italic>\n                    and\n                    <jats:italic>Escherichia coli<\/jats:italic>\n                    using pentamer frequencies. We selected support-vector machine (SVM) models as the best classifier for all three bacterial species (F1-score\n                    <jats:italic>E. faecium<\/jats:italic>\n                    =0.92, F1-score\n                    <jats:italic>K. pneumoniae<\/jats:italic>\n                    =0.90, F1-score\n                    <jats:italic>E. coli<\/jats:italic>\n                    =0.76), which outperformed other existing plasmid prediction tools using a benchmarking set of isolates. We demonstrated the scalability of our models by accurately predicting the plasmidome of a large collection of 1644\n                    <jats:italic>E. faecium<\/jats:italic>\n                    isolates and illustrate its applicability by predicting the location of antibiotic-resistance genes in all three species. The SVM classifiers are publicly available as an R package and graphical-user interface called \u2018mlplasmids\u2019. We anticipate that this tool may significantly facilitate research on the dissemination of plasmids encoding antibiotic resistance and\/or contributing to host adaptation.\n                  <\/jats:p>","DOI":"10.1099\/mgen.0.000224","type":"journal-article","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T13:13:42Z","timestamp":1541078022000},"source":"Crossref","is-referenced-by-count":137,"title":["mlplasmids: a user-friendly tool to predict plasmid- and chromosome-derived sequences for single species"],"prefix":"10.1099","volume":"4","author":[{"given":"Sergio","family":"Arredondo-Alonso","sequence":"first","affiliation":[{"name":"1\u200bDepartment of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malbert R. C.","family":"Rogers","sequence":"additional","affiliation":[{"name":"1\u200bDepartment of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johanna C.","family":"Braat","sequence":"additional","affiliation":[{"name":"1\u200bDepartment of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tess D.","family":"Verschuuren","sequence":"additional","affiliation":[{"name":"2\u200bJulius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janetta","family":"Top","sequence":"additional","affiliation":[{"name":"1\u200bDepartment of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jukka","family":"Corander","sequence":"additional","affiliation":[{"name":"3\u200bFaculty of Medicine, Department of Biostatistics, University of Oslo, Oslo, Norway"},{"name":"4\u200bDepartment of Mathematics and Statistics, University of Helsinki, Helsinki, Finland"},{"name":"5\u200bInfection Genomics, Wellcome Trust Sanger Institute, Hinxton, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rob J. 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