Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates

Michael Feldgarden(National Institutes of Health), Vyacheslav Brover(National Institutes of Health), Daniel H. Haft(National Institutes of Health), Arjun Prasad(National Institutes of Health), Douglas J. Slotta(National Institutes of Health), Igor Tolstoy(National Institutes of Health), Gregory H. Tyson(Center for Veterinary Medicine), Shaohua Zhao(Center for Veterinary Medicine), Chih-Hao Hsu(Center for Veterinary Medicine), Patrick F. McDermott(Center for Veterinary Medicine), Daniel A. Tadesse(Center for Veterinary Medicine), Cesar A. Morales(Food Safety and Inspection Service), Mustafa Simmons(Food Safety and Inspection Service), Glenn E. Tillman(Food Safety and Inspection Service), Jamie Wasilenko(Food Safety and Inspection Service), Jason P. Folster(Centers for Disease Control and Prevention), William Klimke(National Institutes of Health)
Antimicrobial Agents and Chemotherapy
August 14, 2019
Cited by 1,437Open Access
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Abstract

isolates phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene detection system. Most gene calls were identical, but there were 1,229 gene symbol differences (8.8%) between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that ResFinder found, while ResFinder missed 216 loci that AMRFinder identified. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system.


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