Chapter title |
Genomic Epidemiological Analysis of Antimicrobial-Resistant Bacteria with Nanopore Sequencing.
|
---|---|
Chapter number | 16 |
Book title |
Nanopore Sequencing
|
Published in |
Methods in molecular biology, January 2023
|
DOI | 10.1007/978-1-0716-2996-3_16 |
Pubmed ID | |
Book ISBNs |
978-1-07-162995-6, 978-1-07-162996-3
|
Authors |
Suzuki, Masato, Hashimoto, Yusuke, Hirabayashi, Aki, Yahara, Koji, Yoshida, Mitsunori, Fukano, Hanako, Hoshino, Yoshihiko, Shibayama, Keigo, Tomita, Haruyoshi |
Abstract |
Antimicrobial-resistant (AMR) bacterial infections caused by clinically important bacteria, including ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) and mycobacteria (Mycobacterium tuberculosis and nontuberculous mycobacteria), have become a global public health threat. Their epidemic and pandemic clones often accumulate useful accessory genes in their genomes, such as AMR genes (ARGs) and virulence factor genes (VFGs). This process is facilitated by horizontal gene transfer among microbial communities via mobile genetic elements (MGEs), such as plasmids and phages. Nanopore long-read sequencing allows easy and inexpensive analysis of complex bacterial genome structures, although some aspects of sequencing data calculation and genome analysis methods are not systematically understood. Here we describe the latest and most recommended experimental and bioinformatics methods available for the construction of complete bacterial genomes from nanopore sequencing data and the detection and classification of genotypes of bacterial chromosomes, ARGs, VFGs, plasmids, and other MGEs based on their genomic sequences for genomic epidemiological analysis of AMR bacteria. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 7 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 86% |
Scientists | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 8 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Professor | 1 | 13% |
Student > Postgraduate | 1 | 13% |
Unknown | 6 | 75% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 2 | 25% |
Unknown | 6 | 75% |