MetaAMRSpotter: a shell-scripted workflow for the detection of AMR hotspots in metagenomes

Antimicrobial Resistance is recognized as one of the top global health threats, as microorganisms like bacteria, viruses, fungi, and parasites are increasingly resistant to antimicrobial treatments. The World Health Organization reports that 1 in 5 infections globally now exhibit reduced susceptibility to conventional antibiotics. This resistance leads to persistent infections, severe illnesses, increased disease transmission, and even death. Current methods of detecting AMR are complex, costly, and impose a heavy burden, particularly on economically disadvantaged populations. We present MetaAMRSpotter , a computational pipeline designed to identify pathogens, detect AMR genes, and determine the antibiotics to which these genes are resistant. The pipeline integrates nine different bioinformatics tools, including FastQC, Trimmomatic, Bowtie2, Spades, Quast, MetaPhlAn, and Abricate, to streamline the analysis. The workflow is initiated with a single input and processes metagenomic raw data from various sample types. The entire pipeline is coded using shell scripting and can be operated on desktop Linux systems and high-performance computing clusters. MetaAMRSpotter was tested on diverse metagenome datasets downloaded from NCBI SRA such as chicken-gut, goat-gut, cow-gut, sheep faecal, human-gut and sequenced lake water sample. The pipeline successfully identified microorganisms from phyla including Actinobacteria , Bacteroidetes , Cyanobacteria , Firmicutes , and Proteobacteria . AMR genes such as tet(G) (resistant to tetracycline), FloR (resistant to chloramphenicol/florfenicol), mph(F) (resistant to erythromycin), and erm(55) (resistant to clarithromycin) were detected, along with their corresponding resistant antibiotics. MetaAMRSpotter offers a user-friendly, robust solution for efficient AMR detection across a wide range of sample types. It simplifies the complex process of metagenomic analysis and can uncover hidden AMR hotspots, making it a valuable tool for researchers and clinicians. While the pipeline has demonstrated its versatility, further testing with larger datasets is essential to validate its scalability. Nonetheless, this open-source pipeline has the potential to facilitate rapid and accurate AMR detection, contributing to global efforts in combating AMR.