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Sequencing-based methods and resources to study antimicrobial resistance.

Manish Boolchandani1, Alaric W D'Souza1, Gautam Dantas2,3,4,5

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Antimicrobial resistance (AMR) poses significant health and economic burdens. This review details methods for identifying and understanding AMR, focusing on sequencing technologies for resistance discovery.

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Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Antimicrobial resistance (AMR) leads to severe illness, death, and economic losses by making bacteria resistant to antibiotics.
  • Effective clinical treatment of resistant infections and public health strategies to curb resistance spread necessitate understanding AMR.
  • Next-generation sequencing (NGS) technologies enhance the detection and study of AMR.

Purpose of the Study:

  • To provide a comprehensive review of methods for identifying and characterizing antimicrobial resistance.
  • To highlight the role of sequencing-based approaches in AMR discovery.
  • To discuss essential tools and databases for AMR research.

Main Methods:

  • Overview of traditional antimicrobial susceptibility testing (AST).
  • Detailed examination of sequencing-based methods for AMR identification.
  • Discussion of emerging deep-learning approaches for AMR analysis.

Main Results:

  • Sequencing technologies offer powerful capabilities for detecting and characterizing AMR.
  • Various bioinformatics tools and databases are crucial for analyzing AMR data.
  • A range of methods, from traditional to advanced, exist for AMR assessment.

Conclusions:

  • Accurate identification and characterization of AMR are critical for combating resistant infections.
  • Sequencing-based methods are increasingly important for AMR discovery and surveillance.
  • Integrated approaches utilizing diverse tools and databases are vital for effective AMR research and control.