Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing

View ORCID ProfileSimone Marini, Rodrigo A. Mora, Christina Boucher, View ORCID ProfileNoelle Noyes, Mattia Prosperi
doi: https://doi.org/10.1101/2021.11.03.467126
Simone Marini
1Department of Epidemiology, University of Florida
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Simone Marini
  • For correspondence: simone.marini@ufl.edu
Rodrigo A. Mora
1Department of Epidemiology, University of Florida
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christina Boucher
2Department of Computer and Information Science and Engineering, University of Florida
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Noelle Noyes
3Department of Veterinary Population Medicine, University of Minnesota
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Noelle Noyes
Mattia Prosperi
1Department of Epidemiology, University of Florida
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: simone.marini@ufl.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Antimicrobial resistance (AMR) is a growing threat to public health and farming at large. In clinical and veterinary practice, timely characterization of the antibiotic susceptibility profile of bacterial infections is a crucial step in optimizing treatment. High-throughput sequencing is a promising option for clinical point-of-care and ecological surveillance, opening the opportunity to develop genotyping-based AMR determination as a possibly faster alternative to phenotypic testing. In the present work, we compare the performance of state-of-the-art methods for detection of AMR using high-throughput sequencing data from clinical settings. We consider five computational approaches based on alignment (AMRPlusPlus), deep learning (DeepARG), k-mer genomic signatures (KARGA, ResFinder) or hidden Markov models (Meta-MARC). We use an extensive collection of 585 isolates with available AMR resistance profiles determined by phenotypic tests across nine antibiotic classes. We show how the prediction landscape of AMR classifiers is highly heterogeneous, with balanced accuracy varying from 0.40 to 0.92. Although some algorithms—ResFinder, KARGA, and AMRPlusPlus– exhibit overall better balanced accuracy than others, the high per-AMR-class variance and related findings suggest that: (1) all algorithms might be subject to sampling bias present both in data repositories used for training and experimental/clinical settings; and (2) a portion of clinical samples might contain uncharacterized AMR genes that the algorithms—mostly trained on known AMR genes—fail to generalize upon. These results lead us to formulate practical advice for software configuration and application, and give suggestions for future study designs to further develop AMR prediction tools from proof-of-concept to bedside.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Back to top
PreviousNext
Posted January 11, 2022.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing
Simone Marini, Rodrigo A. Mora, Christina Boucher, Noelle Noyes, Mattia Prosperi
bioRxiv 2021.11.03.467126; doi: https://doi.org/10.1101/2021.11.03.467126
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing
Simone Marini, Rodrigo A. Mora, Christina Boucher, Noelle Noyes, Mattia Prosperi
bioRxiv 2021.11.03.467126; doi: https://doi.org/10.1101/2021.11.03.467126

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3482)
  • Biochemistry (7329)
  • Bioengineering (5301)
  • Bioinformatics (20212)
  • Biophysics (9985)
  • Cancer Biology (7706)
  • Cell Biology (11273)
  • Clinical Trials (138)
  • Developmental Biology (6425)
  • Ecology (9923)
  • Epidemiology (2065)
  • Evolutionary Biology (13292)
  • Genetics (9353)
  • Genomics (12559)
  • Immunology (7681)
  • Microbiology (18964)
  • Molecular Biology (7421)
  • Neuroscience (40915)
  • Paleontology (298)
  • Pathology (1226)
  • Pharmacology and Toxicology (2130)
  • Physiology (3145)
  • Plant Biology (6842)
  • Scientific Communication and Education (1271)
  • Synthetic Biology (1893)
  • Systems Biology (5299)
  • Zoology (1086)