A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes. Issue 6 (11th August 2021)

• Record Type: Journal Article
• Authors: VanOeffelen, Margo; Nguyen, Marcus; Aytan-Aktug, Derya; Brettin, Thomas; Dietrich, Emily M; Kenyon, Ronald W; Machi, Dustin; Mao, Chunhong; Olson, Robert; Pusch, Gordon D; Shukla, Maulik; Stevens, Rick; Vonstein, Veronika; Warren, Andrew S; Wattam, Alice R; Yoo, Hyunseung; Davis, James J
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 6(2021)
• Publication Date: 2021-08-11
• Subjects: artificial intellignence -- machine learning -- antimicrobial resistance -- epidemiology -- GWAS; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab313
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt .