Antimicrobial resistance has become one of the most pressing threats in modern medicine, and researchers in India have now unveiled an open-source computational pipeline designed to make surveillance of resistant microbes faster and more accessible. The tool, called MetaAMRSpotter, is a shell-scripted workflow that identifies pathogens in metagenomic samples, detects antimicrobial resistance genes, and lists the antibiotics to which those genes are likely to confer resistance. Described in BMC Methods, the pipeline chains together nine widely used bioinformatics programs into a single automated command that can run on an ordinary desktop Linux machine or on a high-performance computing cluster.
The motivation behind the work is stark. The World Health Organization reports that one in five infections globally now shows reduced susceptibility to conventional antibiotics, and resistance leads to persistent infections, severe illness, increased transmission and death. The authors of the new study also cite projections that antimicrobial resistance could contribute to ten million deaths annually by 2050. While metagenomic next-generation sequencing allows culture-independent detection of all genetic material in a sample, the analysis of the resulting data remains a significant bottleneck, and existing workflows such as ARGprofiler, Nullarbor, ARIBA and CARD-based tools differ in scope, computational demands and ease of deployment.
MetaAMRSpotter was built to close that gap. It integrates FastQC for quality assessment of raw sequencing reads, Trimmomatic for trimming low-quality reads and adapters, Bowtie2 for aligning quality-filtered reads to reference databases, SPAdes for de novo assembly, QUAST for evaluating assembly quality, MetaPhlAn for taxonomic profiling, and three Abricate-based screening steps covering antimicrobial resistance genes, plasmid markers and virulence factors. The assembled contigs are screened against multiple curated databases, including CARD, PlasmidFinder and VFDB, which the authors argue gives broader coverage than tools tied to a single reference repository. The entire workflow is coded in shell scripting, takes a single input directory of raw data, and generates seven structured, tool-specific output directories for each sample.
Technical choices in the pipeline reflect the challenges of metagenomic data. The authors selected SPAdes over assemblers such as SKESA or Velvet because its multi-sized de Bruijn graph approach handles the low and uneven coverage typical of complex microbial communities, allowing recovery of contigs from both dominant and low-abundance species. MetaPhlAn was chosen for its species-level resolution even with limited reads, reducing false positives. Notably, the pipeline does not impose automated sample rejection thresholds based on assembly metrics such as N50 or mapping rates; instead it retains all assembled data for downstream annotation while generating detailed QUAST quality reports, leaving interpretation to the user.
To test the workflow, the team applied it to a range of shotgun-sequenced metagenomes, including chicken-gut, goat-gut, cow-gut, sheep-faecal and human-gut datasets downloaded from the NCBI Sequence Read Archive, plus an in-house sequenced lake water sample. The water sample was collected from Hennagara Lake in Bangalore, a site chosen for its proximity to industries and potential sewage contamination. DNA was extracted with a DNeasy PowerSoil Kit and sequenced on an Illumina NovaSeq6000 platform, yielding 27,465,700 reads and 4.147 gigabases of data, with 92.21 percent of bases at quality score Q30 or higher. The data were deposited in NCBI SRA under accession number SRR32050611.
Across the tested samples, the pipeline identified microorganisms from phyla including Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes and Proteobacteria. The freshwater library was dominated by Proteobacteria, Actinobacteria, Nitrospirae and Firmicutes, with resistance markers erm(55) and erm(F) detected, suggesting potential resistance to clarithromycin, clindamycin, macrolides and streptogramins. The poultry samples carried a suite of tetracycline-resistance genes, tet(W/L/M/A/40/O/32/44), along with erm(B), vat(E) and the beta-lactamase cfxA6. In goat-gut samples, the team detected aac(6′)_E111, an aminoglycoside acetyltransferase targeting amikacin, kanamycin and tobramycin, the macrolide efflux pump mef(A), and blaOXA-1044, a carbapenem-hydrolyzing beta-lactamase.
The sheep-faecal, cow-gut and human-gut samples each showed distinct resistance profiles. Sheep samples contained tetracycline protection and efflux genes tet(W) and tet(40), the aminoglycoside acetyltransferase aac(6′)_E111, mef(A), and erm(F), which methylates 23S rRNA to block clindamycin, erythromycin and streptogramin B binding. Bovine samples yielded multiple tetracycline-resistance genes including tet(W/Q/S/40/O/32/44), the lincosamide-nucleotidyltransferase lnu(C), and the streptomycin adenyltransferase ant(6)-Ib. The human intestinal sample carried tet(W/Q/O), the sulfonamide-resistant dihydropteroate synthase sul2, the cephalosporin-hydrolyzing beta-lactamase cepA-44, and aadS, which adenylylates streptomycin. The authors highlight the detection of blaOXA-1044 in goat gut as particularly concerning because carbapenems are last-resort antibiotics, raising the possibility of zoonotic transmission of high-priority resistance genes from livestock to humans, though this remains a hypothesis requiring further study.
The team also benchmarked MetaAMRSpotter against established alternatives. They report a runtime of approximately two hours on a desktop system, with a precision of 98 percent and recall of 95 percent for AMR gene detection. By comparison, they cite ResFinder completing analysis in 1.5 hours on a server with 90 percent precision and 85 percent recall, the CARD Resistance Gene Identifier achieving 92 percent precision and 88 percent recall but demanding more computational resources, ARIBA finishing in 1.2 hours with 89 percent precision and 90 percent recall, and the deep-learning tool DeepARG reaching 97 percent precision and 94 percent recall while being less user-friendly. These figures come from the authors’ own comparison and would benefit from independent validation.
The authors are candid about the tool’s limitations. Users must currently install the nine component tools before running an analysis, although a one-step installation script is in development. The pipeline processes metagenomic samples sequentially rather than in parallel, and the team acknowledges that a shell-script approach has constraints in scalability and fault tolerance that matter for large-scale projects; a transition to a more robust implementation enabling parallelization is planned. Further validation with larger datasets from underrepresented regions and microbiomes, such as soil, is described as essential to establish broader applicability, and poor-quality input reads could lead to missed detections. The identified AMR genes will also require in-vitro validation in future studies.
Despite these caveats, the researchers see broad potential applications in clinical diagnostics, environmental monitoring and food safety, from detecting resistance genes in patient samples and water or agricultural runoff to screening animal products for compliance with safety standards. The pipeline is open source, published on protocols.io and available on GitHub, and the authors position it as a supportive tool aligned with WHO action plans on antimicrobial resistance, providing foundational surveillance data rather than a replacement for laboratory confirmation. As resistance continues to spread through human, animal and environmental reservoirs, accessible tools that lower the technical barrier to metagenomic AMR screening could play a growing role in global monitoring efforts.
Subject of Research: A shell-scripted computational pipeline for detecting antimicrobial resistance genes and pathogens in metagenomic datasets
Article Title: MetaAMRSpotter: a shell-scripted workflow for the detection of AMR hotspots in metagenomes
Article References: Sureshkumar, P., Niranjan, V., Karunakaran, C., Setlur, A. S., Biswas, S., SH, V., Vinod, S., & Joel, R. (2026). MetaAMRSpotter: a shell-scripted workflow for the detection of AMR hotspots in metagenomes. BMC Methods, 3(1), Article 32. https://doi.org/10.1186/s44330-026-00087-2
Image Credits: AI Generated
DOI: 10.1186/s44330-026-00087-2
Keywords: antimicrobial resistance, metagenomics, bioinformatics pipeline, shell scripting, AMR genes, One Health, SPAdes, MetaPhlAn, Abricate, NCBI SRA, surveillance, open source
News Source: Juliet Wilcox. (October 29, 2026). New Shell-Script Pipeline Speeds Detection of AMR Genes in Metagenomes. Scienmag.
