摘要
      抗生素耐药性已经升级为一个具有广泛公共卫生意义的重大问题。定期监测来自人类、动物和环境来源的微生物和宏基因组中的抗生素耐药性基因（ARGs），对于了解ARGs的流行病学和预测新的抗生素耐药性决定因素的出现至关重要。使用抗生素耐药性数据库和计算机预测工具对微生物ARGs进行基于全基因组测序（WGS）的鉴定，可以显著加快对不同生态位中ARGs的监测和表征。从WGS数据注释ARGs的主要障碍是，大多数基因组数据库包含片段化的基因/基因组（由于不完全组装）。在此，我们描述了一种原位细菌抗生素耐药性扫描（BacARscan）(http://proteininformatics.org/mkumar/bacarscan/)可以检测、预测和表征组学数据集中的ARGs，包括短测序、读取和片段重叠群。在一个独立的非冗余数据集上进行的基准测试显示，BacARscan的性能优于其他现有方法，在ARG和非ARG蛋白的组合数据集上具有近92%的精度和95%的F-测量。与其他ARG注释方法相比，BacARscan最显著的改进之一是它能够以同等的效率在基因组和短读序列库上工作，并且不需要组装短读。因此，BacARscan可以帮助监测动物、人类和环境环境中微生物种群和宏基因组样本中ARGs的流行率和多样性。作者打算在发现新的ARG时不断更新当前版本的BacARscan。可执行版本、源代码、用于开发的序列和使用说明可在(http://www.proteininformatics.org/mkumar/bacarscan/downloads.html)和GitHub存储库(https://github.com/mkubiophysics/BacARscan).
Abstract
Antibiotic resistance has escalated as a significant problem of broad public health significance. Regular surveillance of antibiotic resistance genes (ARGs) in microbes and metagenomes from human, animal and environmental sources is vital to understanding ARGs’ epidemiology and foreseeing the emergence of new antibiotic resistance determinants. Whole-genome sequencing (WGS)-based identification of the microbial ARGs using antibiotic resistance databases and in silico prediction tools can significantly expedite the monitoring and characterization of ARGs in various niches. The major hindrance to the annotation of ARGs from WGS data is that most genome databases contain fragmented genes/genomes (due to incomplete assembly). Herein, we describe an insilicoBacterial Antibiotic Resistance scan (BacARscan) (http://proteininformatics.org/mkumar/bacarscan/) that can detect, predict and characterize ARGs in -omics datasets, including short sequencing, reads, and fragmented contigs. Benchmarking on an independent non-redundant dataset revealed that the performance of BacARscan was better than other existing methods, with nearly 92% Precision and 95% F-measure on a combined dataset of ARG and non-ARG proteins. One of the most notable improvements of BacARscan over other ARG annotation methods is its ability to work on genomes and short-reads sequence libraries with equal efficiency and without any requirement for assembly of short reads. Thus, BacARscan can help monitor the prevalence and diversity of ARGs in microbial populations and metagenomic samples from animal, human, and environmental settings. The authors intend to constantly update the current version of BacARscan as and when new ARGs are discovered. Executable versions, source codes, sequences used for development and usage instructions are available at (http://www.proteininformatics.org/mkumar/bacarscan/downloads.html) and GitHub repository (https://github.com/mkubiophysics/BacARscan).

https://academic.oup.com/biomethods/article/7/1/bpac031/6854969?login=false