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Overview of bioinformatic methods for analysis of antibiotic resistome from genome and metagenome data

  • Review
  • Omics in Microbial Ecology
  • Published:
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Abstract

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.

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Acknowledgments

This work was supported by the Korea Ministry of Environment (MOE) as “the Environmental Health Action Program (2016001350004)” and by the “Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ015130032020)” Rural Development Administration, Republic of Korea.

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  1. Department of Systems Biotechnology and Center for Antibiotic Resistome, Chung-Ang University, Anseong, 17546, Republic of Korea

    Kihyun Lee & Chang-Jun Cha

  2. ChunLab, Inc., Seoul, 06194, Republic of Korea

    Kihyun Lee

  3. Division of Life Sciences, Jeonbuk National University, Jeonju, 54896, Republic of Korea

    Dae-Wi Kim

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  1. Kihyun Lee
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  2. Dae-Wi Kim
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  3. Chang-Jun Cha
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Correspondence to Chang-Jun Cha.

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Lee, K., Kim, DW. & Cha, CJ. Overview of bioinformatic methods for analysis of antibiotic resistome from genome and metagenome data. J Microbiol. 59, 270–280 (2021). https://doi.org/10.1007/s12275-021-0652-4

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  • DOI: https://doi.org/10.1007/s12275-021-0652-4

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