MetaPointFinder: Detection of Antimicrobial Resistance-Conferring Point Mutations (ARMs) from Metagenomic Reads

This download contains the scripts, metadata and files to generate the data for figures and tables in the MetaPointFinder manuscript, first revision. Read the file "readme.txt" in the zip file on how to generate all data files. Warning. you need at least 10 tb of disk space and several week of computing time. Abstract Background: Many clinically important antimicrobial resistance (AMR) phenotypes such as fluoroquinolones and rifamycins are driven by antimicrobial resistance-conferring mutations (ARMs) in conserved chromosomal loci (e.g., gyrA, parC, rpoB). Resistome profiling by metagenomics sequencing is a promising AMR surveillance tool as it is organism-agnostic, but currently, existing metagenomic AMR surveillance pipelines are only able to identify acquired AMR genes but not point mutations associated with AMR. This is a serious gap in metagenomics-based AMR surveillance, as the true extent of AMR may be underestimated. Methods: We developed MetaPointFinder (v1), a read-based method that can process both long and short metagenomic reads. The tool identifies resistance determining region carrying reads using DIAMOND (translated protein) or KMA (nucleotide) mapping against complete reference gene sequences from AMRFinderPlus, followed by classifying resistant, wild-type and unknown variants by inspecting individual pairwise sequence alignments for known resistance conferring mutations in each read per antibiotic class. The tool outputs ARMs per read, per gene and per antibiotic class. Results: We benchmark our tool using simulated reads with different lengths and error rates from complete gene and complete reverse-translated protein references, and with mixtures of real reads from 42 isolates with known resistance mutations. We show that MetaPointFinder outperforms MuMaMe for detection of ARMs in these simulated read data, genomic mixtures and real metagenomics data. In proof-of-concept analyses, MetaPointFinder identified known AMR-associated mutations and quantified resistant/susceptible read counts and ratios from metagenomic samples. Conclusion: MetaPointFinder complements gene-centric resistome profiling by capturing chromosomal mutation-based AMR directly from metagenomes.