Predicting Antimicrobial Resistance Using Partial Genome Alignments-Reference-Cited by-同舟云学术

Predicting Antimicrobial Resistance Using Partial Genome Alignments

Published:2021-06-29 Issue:3 Volume:6 Page:
ISSN:2379-5077
Container-title:mSystems
language:en
Short-container-title:mSystems

Author:

Aytan-Aktug D.¹^ORCID,Nguyen M.²³,Clausen P. T. L. C.¹^ORCID,Stevens R. L.²⁴⁵,Aarestrup F. M.¹,Lund O.¹^ORCID,Davis J. J.²³⁶^ORCID

Affiliation:

1. National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark

2. Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, USA

3. Data Science and Learning Division, Argonne National Laboratory, Argonne, Illinois, USA

4. Computing Environment and Life Sciences Directorate, Argonne National Laboratory, Argonne, Illinois, USA

5. Department of Computer Science, University of Chicago, Chicago, Illinois, USA

6. Northwestern Argonne Institute for Science and Engineering, Evanston, Illinois, USA

Abstract

Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission.

Funder

DOD | Defense Advanced Research Projects Agency

HHS | NIH | National Institute of Allergy and Infectious Diseases

Novo Nordisk Fonden

Publisher

American Society for Microbiology

Subject

Computer Science Applications,Genetics,Molecular Biology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics,Biochemistry,Physiology,Microbiology

Link

https://journals.asm.org/doi/pdf/10.1128/mSystems.00185-21

Reference66 articles.

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3. Antimicrobial resistance: risk associated with antibiotic overuse and initiatives to reduce the problem

4. Adaptive and Mutational Resistance: Role of Porins and Efflux Pumps in Drug Resistance

5. Sequencing-based methods and resources to study antimicrobial resistance

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