ARGA, a pipeline for primer evaluation on antibiotic resistance genes

Wei, Ziyan; Wu, Yueni; Feng, Kai; Yang, Min; Zhang, Yu; Tu, Qichao; Wang, Jun; Deng, Ye

doi:10.1016/j.envint.2019.04.030

Cited by 16 publications

(17 citation statements)

References 34 publications

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“…Previously, 51F and 280R were shown to be of good quality for the sul1 gene amplification . Since the reverse primer used for nested PCR should be the same as that used for fusion PCR, a new forward nested PCR primer (149F: 5′-GACGCCCTGTCCSRTCWGAT-3′) was designed utilizing the DegePrime program based on the SDARG database . Potential hairpin formation and self-annealing of primer 149F were excluded by OligoCalc .…”

Section: Methodsmentioning

confidence: 99%

“…21 Since the reverse primer used for nested PCR should be the same as that used for fusion PCR, a new forward nested PCR primer (149F: 5′-GACGCCCTGTCCSRTCWGAT-3′) was designed utilizing the DegePrime program 35 based on the SDARG database. 36 Potential hairpin formation and self-annealing of primer 149F were excluded by OligoCalc. 37 The coverage and specificity of primer 149F calculated by an in-house ARGA pipeline (http:// mem.rcees.ac.cn:8083) 36 were 93.70 and 94.62%, respectively.…”

Section: Methodsmentioning

confidence: 99%

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High-Throughput Single-Cell Technology Reveals the Contribution of Horizontal Gene Transfer to Typical Antibiotic Resistance Gene Dissemination in Wastewater Treatment Plants

Wei

Feng

Yang

et al. 2021

Environ. Sci. Technol.

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The spread of antibiotic resistance genes (ARGs) has gained much attention worldwide, while the contribution of vertical gene transfer (VGT) and horizontal gene transfer (HGT) is still elusive. Here, we improved an emerging high-throughput single-cell-based technology, emulsion, paired isolation, and concatenation polymerase chain reaction (epicPCR), by lengthening the sequence of ARG in the fused ARG-16S rRNA fragments to cover the variance of both ARG and its hosts. The improved epicPCR was applied to track the hosts of a widely detected ARG, sul1 gene, in five urban wastewater treatment plants (UWTPs) during two seasons. The sul1 host bacteria were highly diverse and mostly classified as Proteobacteria and Bacteroidetes. Clear seasonal divergence of α-diversity and interaction networks were present in the host community. The consensus phylogenetic trees of the sul1 gene and their host demonstrated incorrespondence on the whole and regularity on abundant groups, suggesting the important role of both HGT and VGT, respectively. The relative importance of these two ways was further measured; HGT (54%) generally played an equal or even more important role as VGT (46%) in UWTPs. The application of the improved epicPCR technology provides a feasible approach to quantify the relative contributions of VGT and HGT in environmental dissemination of ARGs.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

High-Throughput Single-Cell Technology Reveals the Contribution of Horizontal Gene Transfer to Typical Antibiotic Resistance Gene Dissemination in Wastewater Treatment Plants

Wei

Feng

Yang

et al. 2021

Environ. Sci. Technol.

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“…Specifically, on the basis of assembly-based strategy, ARGs-OAP (v1.0 and v2.0, http://smile.hku.hk/SARGs) (Yang et al, 2016;Yin et al, 2018), ARGs-OSP (http://smile.hku.hk/SARGs) (Zhang et al, 2020), PathoFact (https://git-r3lab.uni.lu/laura. denies/PathoFact/) (de Nies et al, 2021), ARG analyzer (ARGA, http://mem.rcees.ac.cn:8083/) (Wei et al, 2019), Resistance Gene Identifier (RGI, https://github.com/arpcard/ rgi) (Alcock et al, 2020), ResFinder (https://cge.cbs.dtu.dk/ services/ResFinder/), DeepARG (https://bench.cs.vt.edu/ deeparg) (Arango-Argoty et al, 2018), and HMD-ARG (http:// www.cbrc.kaust.edu.sa/HMDARG/) (Li et al, 2021a) were developed and have been widely applied to detect potential ARGs from the gene datasets predicted from metagenomic contigs (Figure 1B). Together with the ARG database, ARGs-OAP was designed as an online pipeline to fast annotate and classify ARG-like sequences from metagenomic data (Yang et al, 2016).…”

Section: Bioinformatic Tools Used For Detecting Potential Antibiotics Resistance Genes Based On Metagenomic Datamentioning

confidence: 99%

“…Furthermore, PathoFact was designed and developed to solve the virulence factors (VFs) and ARGs of pathogenic microorganisms; this an easy-to-use, modular, and reproducible tool can predict VFs, bacterial toxins, and ARG from metagenomic data with high accuracy (de Nies et al, 2021). Moreover, on the basis of an updated database, ARGA was developed to assess the primer of ARGs and identify and annotate ARGs from environmental metagenomes (Wei et al, 2019). It should be noted that the identification of potential ARGs usually depends on the search results.…”

Section: Bioinformatic Tools Used For Detecting Potential Antibiotics Resistance Genes Based On Metagenomic Datamentioning

confidence: 99%

“…The latest version of SARG (v2.0) has tripled the sequences of the first version, improved the coverage of ARG detection, and annotated the high-throughput raw reads by using a similarity search strategy in diverse environmental metagenomes (Zhao et al, 2020). Based on ARDB, an updated SDARG, including 1,260,069 protein sequences and 1,164,479 nucleotide sequences from 448 types of ARGs belonging to 18 categories of antibiotics, was constructed and used in ARGA (Wei et al, 2019). Moreover, as a companion database to DeepARG, DeepARG-DB was designed to improve the quality of the model (Arango-Argoty et al, 2018).…”

Section: Bioinformatic Databases Used For Identifying Antibiotics Resistance Genes and Mgesmentioning

confidence: 99%

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Utilizing Metagenomic Data and Bioinformatic Tools for Elucidating Antibiotic Resistance Genes in Environment

Peng

Mao

Zhang

et al. 2021

Front. Environ. Sci.

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Antibiotics resistance genes (ARGs) are mainly caused by the extensive use and abuse of antibiotics and have become a global public health concern. Owing to the development of high-throughput sequencing, metagenomic sequencing has been widely applied to profile the composition of ARGs, investigate their distribution pattern, and track their sources in diverse environments. However, the lack of a detailed transmission mechanism of ARGs limits the management of its pollution. Hence, it’s essential to introduce how to utilize the metagenomic data to obtain an in-depth understanding of the distribution pattern and transmission of ARGs. This review provides an assessment of metagenomic data utilization in ARG studies and summarizes current bioinformatic tools and databases, including ARGs-OAP, ARG analyzer, DeepARG, CARD, and SARG, for profiling the composition of ARGs and tracking the source of ARGs. Several bioinformatic tools and databases were then benchmarked. Our results showed that although SARG is a good database, the application of two or more bioinformatic tools and databases could provide a comprehensive view of ARG profiles in diverse environmental samples. Finally, several perspectives were proposed for future studies to obtain an in-depth understanding of ARGs based on metagenomic data. Our review of the utilization of metagenomic data together with bioinformatic tools and databases in ARG studies could provide insights on exploring the profiles and transmission mechanism of ARG in different environments that mitigate the spread of ARGs and manage the ARGs pollution.

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Approaches of Bioinformatics in Antibacterial Drug Development

Dewangan,

Rawat

2024

Natural Products for Antibacterial Drug Development: Recent Advancement of Computational Approach

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ARGA, a pipeline for primer evaluation on antibiotic resistance genes

Cited by 16 publications

References 34 publications

High-Throughput Single-Cell Technology Reveals the Contribution of Horizontal Gene Transfer to Typical Antibiotic Resistance Gene Dissemination in Wastewater Treatment Plants

High-Throughput Single-Cell Technology Reveals the Contribution of Horizontal Gene Transfer to Typical Antibiotic Resistance Gene Dissemination in Wastewater Treatment Plants

Utilizing Metagenomic Data and Bioinformatic Tools for Elucidating Antibiotic Resistance Genes in Environment

Approaches of Bioinformatics in Antibacterial Drug Development

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