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Sequencing-based methods and resources to study antimicrobial resistance

Nature Reviews Genetics (2019) | Download Citation


Antimicrobial resistance extracts high morbidity, mortality and economic costs yearly by rendering bacteria immune to antibiotics. Identifying and understanding antimicrobial resistance are imperative for clinical practice to treat resistant infections and for public health efforts to limit the spread of resistance. Technologies such as next-generation sequencing are expanding our abilities to detect and study antimicrobial resistance. This Review provides a detailed overview of antimicrobial resistance identification and characterization methods, from traditional antimicrobial susceptibility testing to recent deep-learning methods. We focus on sequencing-based resistance discovery and discuss tools and databases used in antimicrobial resistance studies.

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The authors thank K. Sukhum and M. Pandey for reading through a draft of this paper. This work was supported in part by awards to G.D. through the National Institute of Allergy and Infectious Diseases (NIAID), the Eunice Kennedy Shriver National Institute of Child Health & Human Development and the National Center for Complementary and Integrative Health of the US National Institutes of Health (NIH) under award numbers R01AI123394, R01HD092414 and R01AT009741, respectively. A.W.D. received support from the Institutional Program Unifying Population and Laboratory-Based Sciences Burroughs Wellcome Fund Grant to Washington University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

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Nature Reviews Genetics thanks J. Parkhill, E. Ruppé and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Author notes

  1. These authors contributed equally: Manish Boolchandani, Alaric D’Souza.


  1. The Edison Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA

    • Manish Boolchandani
    • , Alaric W. D’Souza
    •  & Gautam Dantas
  2. Department of Pathology & Immunology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA

    • Gautam Dantas
  3. Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA

    • Gautam Dantas
  4. Department of Molecular Microbiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA

    • Gautam Dantas


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M.B. and A.W.D. researched the literature and wrote the article. All authors substantially contributed to discussions of the content and reviewed and/or edited the manuscript before submission.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Gautam Dantas.


Antimicrobial resistance

Bacterial ability to survive or grow in otherwise lethal or inhibitory antimicrobial concentrations.

Antimicrobial susceptibility testing

(AST). Challenge of bacteria with antimicrobials to determine whether they have phenotypic antimicrobial resistance.

Horizontal gene transfer

(HGT). Passage of resistance genes from one bacterium to another when neither bacteria is the parent or daughter cell. This process usually occurs through transduction, conjugation or transformation.


Collections of genes from all organisms of a given habitat or sample.

Resistance exchange networks

Interconnected groups of environments or bacteria that transfer resistance genes with each other.


The evolutionary ancestral relationships between organisms.


Contiguous sequences assembled from sequencing reads.

De Bruijn graph

(DBG). Directional graphing algorithm commonly used for short-read assembly.

Euler’s path

A walk through a directed graph that crosses each edge in the graph only once. Euler’s path is used to reconstruct genome sequences from De Bruijn graphs.

Isolate assembly

Gathering of sequencing reads from a bacterial isolate into longer contiguous sequences representative of their state within the bacterium.


All antimicrobial resistance genes within a given sample of bacteria.


Identification and labelling of genes within a genome.

Burrows–Wheeler transform

A reversible data transformation algorithm to organize text with repeated sequences for efficient compression. This algorithm is implemented in bioinformatics software owing to frequent repeated sequences in biological data.

Metagenomic assembly

Deconvolution and assembly of sequencing reads from a metagenomic sample.

Hidden Markov model

(HMM). A probabilistic model of antimicrobial resistance process where hidden states emit observable outputs. These models are commonly used for sequence annotation.


A community of microorganisms from a given habitat or sample.

Functional metagenomics

A biological assay in which a metagenomic library of DNA is expressed in a naive host and then the host is exposed to a selection pressure to select for DNA that confers a fitness advantage against the selection pressure.


The collection and organization of biological data in a data structure useful for future analysis.

Carbapenem resistance

Resistance against the broad-spectrum carbapenem class of β-lactam antimicrobials, which are often used as drugs of last resort.

Methicillin resistance

Resistance against methicillin, a narrow-spectrum penicillin derivative. Methicillin resistance is often seen in the context of methicillin-resistant Staphylococcus aureus (MRSA), a common human pathogen. This resistance is commonly gained by horizontal transfer of a modified target protein (see Fig. 1b and 1c).

Deep learning

An extension of representational machine learning methods where the algorithm uses multiple transformation layers between raw data and output rather than one layer. This often improves results for more complex machine learning tasks.

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