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. 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 often proposed as an ideal 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 regions in reads using DIAMOND (translated protein) and KMA (nucleotide), and classifies known resistant versus wild-type variants by aligning the sequences using pwalign and assessing the observed mutations based on known resistance mutations in the AMRFinderPlus database. The tool outputs ARMs per read, per gene and per antibiotic class. 
Results: In proof-of-concept analyses, MetaPointFinder identified known AMR-associated mutations and quantified resistant/susceptible read counts and ratios from metagenomic samples with corroborating phenotypic resistance data when available. We benchmark our tool using simulated reads from DNA and from reverse-translated protein references with read lengths of 100–5000 bp and error rates between 0 and 30%, simulating both Illumina and Nanopore error rates. We show that MetaPointFinder outperforms any available method for detection of ARMs in metagenomics data. 
Conclusion: MetaPointFinder complements gene-centric resistome profiling by capturing chromosomal mutation-based AMR directly from metagenomes.