ARG-ANNOT, a New Bioinformatic Tool To Discover Antibiotic Resistance Genes in Bacterial Genomes
By Sushim K Gupta, Babu roshan Padmanabhan, S. Diene, R. López-Rojas, M. Kempf, L. Landraud, J. Rolain. Antimicrobial Agents and Chemotherapy, 2013
1,300 citations recorded by the source corpus.
ABSTRACT ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation) is a new bioinformatic tool that was created to detect existing and putative new antibiotic resistance (AR) genes in bacterial genomes. ARG-ANNOT uses a local BLAST program in Bio-Edit software that allows the user to analyze sequences without a Web interface. All AR genetic determinants were collected from published works and online resources; nucleotide and protein sequences were retrieved from the NCBI GenBank database. After building a database that includes 1,689 antibiotic resistance genes, the software was tested in a blind manner using 100 random sequences selected from the database to verify that the sensitivity and specificity were at 100% even when partial sequences were queried. Notably, BLAST analysis results obtained using the rmtF gene sequence (a new aminoglycoside-modifying enzyme gene sequence that is not included in the database) as a query revealed that the tool was able to link this sequence to short sequences (17 to 40 bp) found in other genes of the rmt family with significant E values. Finally, the analysis of 178 Acinetobacter baumannii and 20 Staphylococcus aureus genomes allowed the detection of a significantly higher number of AR genes than the Resfinder gene analyzer and 11 point mutations in target genes known to be associated with AR. The average time for the analysis of a genome was 3.35 ± 0.13 min. We have created a concise database for BLAST using a Bio-Edit interface that can detect AR genetic determinants in bacterial genomes and can rapidly and easily discover putative new AR genetic determinants.
Subfields: Clinical Biochemistry, Molecular Medicine, Pollution
View Paper: https://www.semanticscholar.org/paper/a2fdbd3e9fc03b51f0825473a783a51994913bfe
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Citations in the corpus
- Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in Klebsiella pneumoniae, an urgent threat to public health (2015, neutral): “Gene databases analyzed were AMR alleles [ARG-Annot (70)], plasmid replicons [PlasmidFinder (59)], and virulence andwzi alleles [K.”
- ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads (2017, methodology): “ARIBA can easily be provided with custom reference sequence-sets, and includes support for a number of public databases: ARGANNOT [6], CARD [10], MEGARes [11], and ResFinder [5].”
- Metagenomic analysis reveals wastewater treatment plants as hotspots of antibiotic resistance genes and mobile genetic elements. (2017, methodology): “155 Regarding ARGs annotation, a local database of ARGs was created by downloading all sequences 156 from Antibiotic Resistance Database-ANNOTation (ARG-ANNOT), which is a new platform to 157 detect existing and putative ARGs in bacterial genomes (Gupta et al., 2014).”
- CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database (2019, methodology): “Most notable of these primary AMR databases are ARG-ANNOT (20), ResFinder (21) and increasingly the National Center for Biotechnology Information (NCBI) Pathogen Detection Reference Gene catalog (22).”
- Using Genomics to Track Global Antimicrobial Resistance (2019, neutral): “ARG-ANNOT General AMR – – Own Yes – – Discontinued 2014 2018 (29)”
- PLSDB: a resource of complete bacterial plasmids (2018, methodology): “The sequences were annotated by performing a BLASTn search for resistance factors from ARG-ANNOT (9), CARD (10) and ResFinder (11) with minimal identity and coverage of 95%, virulence factors from VFDB (12) with minimal identity and coverage of 95%, and replicons from PlasmidFinder (13) using the En”
- PointFinder: a novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens (2017, background): “37 Unfortunately, neither ARG-ANNOT nor CARD takes the bacterial species into account.”
- Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates (2019, methodology): “While some tools exclusively use BLAST-based approaches (20), others incorporate hidden Markov model (HMM) approaches (21).”