Department of Systems Biotechnology and Center for Antibiotic Resistome, Chung-Ang University, Anseong, 17546, Republic of Korea.
ChunLab, Inc., Seoul, 06194, Republic of Korea.
J Microbiol. 2021 Mar;59(3):270-280. doi: 10.1007/s12275-021-0652-4. Epub 2021 Feb 23.
Whole genome and metagenome sequencing are powerful approaches that enable comprehensive cataloging and profiling of antibiotic resistance genes at scales ranging from a single clinical isolate to ecosystems. Recent studies deal with genomic and metagenomic data sets at larger scales; therefore, designing computational workflows that provide high efficiency and accuracy is becoming more important. In this review, we summarize the computational workflows used in the research field of antibiotic resistome based on genome or metagenome sequencing. We introduce workflows, software tools, and data resources that have been successfully employed in this rapidly developing field. The workflow described in this review can be used to list the known antibiotic resistance genes from genomes and metagenomes, quantitatively profile them, and investigate the epidemiological and evolutionary contexts behind their emergence and transmission. We also discuss how novel antibiotic resistance genes can be discovered and how the association between the resistome and mobilome can be explored.
全基因组和宏基因组测序是强大的方法,可在从单个临床分离物到生态系统的范围内全面编目和分析抗生素耐药基因。最近的研究涉及更大规模的基因组和宏基因组数据集;因此,设计提供高效率和准确性的计算工作流程变得更加重要。在这篇综述中,我们总结了基于基因组或宏基因组测序的抗生素抗性组研究领域中使用的计算工作流程。我们介绍了在这个快速发展的领域中成功应用的工作流程、软件工具和数据资源。本综述中描述的工作流程可用于从基因组和宏基因组中列出已知的抗生素耐药基因,对其进行定量分析,并研究其出现和传播背后的流行病学和进化背景。我们还讨论了如何发现新的抗生素耐药基因,以及如何探索抗性组和可移动组之间的关联。