Whole-Genome Sequencing and Machine Learning Analysis of Staphylococcus aureus from Multiple Heterogeneous Sources in China Reveals Common Genetic Traits of Antimicrobial Resistance-Reference-Cited by-同舟云学术

Whole-Genome Sequencing and Machine Learning Analysis of Staphylococcus aureus from Multiple Heterogeneous Sources in China Reveals Common Genetic Traits of Antimicrobial Resistance

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

Author:

Wang Wei¹^ORCID,Baker Michelle²,Hu Yue²,Xu Jin¹,Yang Dajin¹,Maciel-Guerra Alexandre³,Xue Ning²,Li Hui¹,Yan Shaofei¹,Li Menghan¹,Bai Yao¹,Dong Yinping¹,Peng Zixin¹,Ma Jinjing¹⁴,Li Fengqin¹^ORCID,Dottorini Tania²^ORCID

Affiliation:

1. NHC Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, China

2. School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Leicestershire, United Kingdom

3. School of Computer Science, University of Nottingham, Nottingham, United Kingdom

4. School of Chemistry and Chemical Engineering, Anqing Normal University, Anqing, Anhui, China

Abstract

Little information is available on the epidemiology and characterization of Staphylococcus aureus in China. The role of food is a cause of major concern: staphylococcal foodborne diseases affect thousands every year, and the presence of resistant Staphylococcus strains on raw retail meat products is well documented.

Funder

Ministry of Science and Technology of the People's Republic of China

UK Research and Innovation

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.01185-20

Reference137 articles.

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