TY - JOUR
T1 - Sequencing-based methods and resources to study antimicrobial resistance
AU - Boolchandani, Manish
AU - D’Souza, Alaric W.
AU - Dantas, Gautam
N1 - Funding Information:
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.
Publisher Copyright:
© 2019, Springer Nature Limited.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85065823370&partnerID=8YFLogxK
U2 - 10.1038/s41576-019-0108-4
DO - 10.1038/s41576-019-0108-4
M3 - Review article
C2 - 30886350
AN - SCOPUS:85065823370
VL - 20
SP - 356
EP - 370
JO - Nature Reviews Genetics
JF - Nature Reviews Genetics
SN - 1471-0056
IS - 6
ER -