NanoARG: a web service for detecting and contextualizing antimicrobial resistance genes from nanopore-derived metagenomes

Abstract

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 . Electronic supplementary material The online version of this article (10.1186/s40168-019-0703-9) contains supplementary material, which is available to authorized users.

Critical evaluation of short, long, and hybrid assembly for contextual analysis of antibiotic resistance genes in complex environmental metagenomes

“…Past work by Arumugam et al 54 has shown, in less diverse communities, that frame shift errors present in long-read assemblies can affect translation. However, recent work by Arango-Argoty et al 57 , which examined ARG detection in complex environmental samples, did not find frame shift errors to be problematic and further demonstrated that the number of ARGs detected did not increase with additional error correction. Furthermore, Canu performs a read polishing step as a part of its assembly pipeline.…”

Resistome metagenomics from plate to farm: The resistome and microbial composition during food waste feeding and composting on a Vermont poultry farm

“…The main advantage of long-read sequencing is the illumination of genetic context; by sequencing longer read fragments, users are not limited to simply detecting the presence of ARGs but can also identify gene clusters that show co-occurrence with other resistance genes or mobile elements that are the likely mechanisms of transfer. Specific tools have been developed for this purpose, such as the accessible web service NanoARG [64] or the interactive software MEGAN-LR [65]. These new sequencing technologies have already been used for rapid detection of ARGs, where the Oxford Nanopore MinION was successfully used in a veterinary hospital to detect ARGs, assess their taxonomy and origin, and to inform mitigation strategies [65,66].…”

Globally Abundant “ Candidatus Udaeobacter” Benefits from Release of Antibiotics in Soil and Potentially Performs Trace Gas Scavenging

“…In addition, putatively novel resistance mechanisms were identified with deepARG (59) using a minimal sequence identity of 30% and a probability of Ͼ90% as thresholds. Additional MGEs were identified via DIAMOND blastx (-e 0.00001 -id 50 -subject-cover 50) against the MGE database of nanoARG (60). Furthermore, the MAG was screened for secondary metabolite biosynthesis clusters via antiSMASH 5.1 (61).…”