Background: Direct and indirect selection pressures imposed by antibiotics and co-selective agents and horizontal gene transfer are fundamental drivers of the evolution and spread of antibiotic resistance. Therefore, effective environmental monitoring tools should ideally capture not only antibiotic resistance genes (ARGs), but also mobile genetic elements (MGEs) and indicators of co-selective forces, such as metal resistance genes (MRGs). A major challenge towards characterizing the potential human health risk of antibiotic resistance is the ability to identify ARG-carrying microorganisms, of which human pathogens are arguably of greatest risk. Historically, short reads produced by next-generation sequencing technologies have hampered confidence in assemblies for achieving these purposes.
Results: Here, we introduce NanoARG, an online computational resource that takes advantage of the long reads produced by nanopore sequencing technology. Specifically, long nanopore reads enable identification of ARGs in the context of relevant neighboring genes, thus providing valuable insight into mobility, co-selection, and pathogenicity. NanoARG was applied to study a variety of nanopore sequencing data to demonstrate its functionality. NanoARG was further validated through characterizing its ability to correctly identify ARGs in sequences of varying lengths and a range of sequencing error rates.
Conclusions: NanoARG allows users to upload sequence data online and provides various means to analyze and visualize the data, including quantitative and simultaneous profiling of ARGs, MRGs, MGEs, and putative pathogens. A user-friendly interface allows users the analysis of long DNA sequences (including assembled contigs), facilitating data processing, analysis, and visualization. NanoARG is publicly available and freely accessible at https://bench.cs.vt.edu/nanoarg .
Keywords: Antibiotic resistance; Metagenomics; Metal resistance; Mobile genetic elements; Nanopore sequencing.