ArticleiScience2023
NCRD: A non-redundant comprehensive database for detecting antibiotic resistance genes.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comparative genomic analysis of Herbaspirillum strains associated with banana sheath rot reveals potential virulence factors.BMC genomics · 2026Article
- Genomic characterization of avian pathogenicApplied and environmental microbiology · 2026Article
- DeepSEA: an alignment-free explainable approach to annotate antimicrobial resistance proteins.BMC bioinformatics · 2025Article
- Impact of urbanization on antimicrobial resistance in soil microbial communities.Scientific reports · 2025Article
- Strategies to Name Metallo-β-Lactamases and Number Their Amino Acid Residues.Antibiotics (Basel, Switzerland) · 2023Review
- Application of next-generation sequencing to identify different pathogens.Frontiers in microbiology · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibiotic resistance genes (ARGs) are emerging pollutants present in various environments. Identifying ARGs has become a growing concern in recent years. Several databases, including the Antibiotic Resistance Genes Database (ARDB), Comprehensive Antibiotic Resistance Database (CARD), and Structured Antibiotic Resistance Genes (SARG), have been applied to detect ARGs. However, these databases have limitations, which hinder the comprehensive profiling of ARGs in environmental samples. To address these issues, we constructed a non-redundant antibiotic resistance genes database (NRD) by consolidating sequences from ARDB, CARD, and SARG. We identified the homologous proteins of NRD from Non-redundant Protein Database (NR) and the Protein DataBank Database (PDB) and clustered them to establish a non-redundant comprehensive antibiotic resistance genes database (NCRD) with similarities of 100% (NCRD100) and 95% (NCRD95). To demonstrate the advantages of NCRD, we compared it with other databases by using metagenome datasets. Results revealed its strong ability in detecting potential ARGs.
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Registered trials
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