A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes

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.

doi:10.1093/bib/bbab313

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes

Journal Article · Wed Aug 11 00:00:00 EDT 2021 · Briefings in Bioinformatics

DOI:https://doi.org/10.1093/bib/bbab313· OSTI ID:1880127

VanOeffelen, Margo ^[1]; Nguyen, Marcus ^[2]; Aytan-Aktug, Derya ^[3]; Brettin, Thomas ^[2]; Dietrich, Emily M. ^[2]; Kenyon, Ronald W. ^[4]; Machi, Dustin ^[5]; Mao, Chunhong ^[5]; Olson, Robert ^[2]; Pusch, Gordon D. ^[1]; Shukla, Maulik ^[2]; ^[6]; Vonstein, Veronika ^[1]; Warren, Andrew S. ^[4]; Wattam, Alice R. ^[7]; Yoo, Hyunseung ^[2]; ^[8]

Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States)
Univ. of Chicago, IL (United States). Consortium for Advanced Science and Engineering; Argonne National Lab. (ANL), Argonne, IL (United States)
Technical Univ. of Denmark, Lyngby (Denmark)
Univ. of Virginia, Charlottesville, VA (United States). Biocomplexity Institute and Initiative
Univ. of Virginia, Charlottesville, VA (United States)
Univ. of Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Virginia, Charlottesville, VA (United States). Biocomplexity Institute and Initiative
Univ. of Chicago, IL (United States). Consortium for Advanced Science and Engineering; Argonne National Lab. (ANL), Argonne, IL (United States); Northwestern Argonne Institute for Science and Engineering, Evanston, IL (United States)

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. Further, 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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE; Defense Advanced Research Projects Agency (DARPA); National Institute of Allergy and Infectious Diseases (NIAID)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1880127

Journal Information:: Briefings in Bioinformatics, Journal Name: Briefings in Bioinformatics Journal Issue: 6 Vol. 22; ISSN 1467-5463

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
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artificial intelligence
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A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes

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