amR is a metapackage that provides a single installation point for the amR suite of packages for antimicrobial resistance (AMR) prediction in bacterial pathogens.
The amR suite consists of three packages that work together:
| Package | Description | Repository |
|---|---|---|
| amRdata | Data curation and feature extraction from bacterial genomes | JRaviLab/amRdata |
| amRml | Machine learning models for AMR prediction | JRaviLab/amRml |
| amRviz | Interactive dashboard for exploring results | JRaviLab/amRviz |
# Install amR metapackage
if (!requireNamespace("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("JRaviLab/amR")
# Then install all packages in the suite
library(amR)
installAMR()You can also install packages individually:
remotes::install_github("JRaviLab/amRdata")
remotes::install_github("JRaviLab/amRml")
remotes::install_github("JRaviLab/amRviz")# Load all packages
library(amRdata)
library(amRml)
library(amRviz)
# 1. Prepare data with amRdata
# features <- prepareFeatures(...)
# 2. Train ML models with amRml
# results <- runMLPipeline(...)
# 3. Explore results with amRviz
# launchDashboard(...)┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ amRdata │ --> │ amRml │ --> │ amRviz │
│ │ │ │ │ │
│ - Genomes │ │ - Train LR │ │ - Dashboard │
│ - Features │ │ - Evaluate │ │ - Plots │
│ - Metadata │ │ - Top feats │ │ - Export │
└─────────────┘ └─────────────┘ └─────────────┘
amR: an R package suite to predict antimicrobial resistance in bacterial pathogens
Abhirupa Ghosh^, Evan P. Brenner^, Emily A. Boyer, Alexander P. McKim, Charmie K. Vang, Ethan P. Wolfe, David Mayer, Raymond L. Lesiyon, Janani Ravi. bioRxiv (2026). doi: 10.64898/2026.07.10.734579
^ Co-first authors
Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz
Abstract
Motivation: Identifying bacterial antimicrobial resistance (AMR) is critical for diagnostics and treatment, but resistance is a complex trait arising from myriad mechanisms spanning multiple molecular scales. Existing computational approaches often function as black boxes and rarely explore cross-species or multi-drug patterns. We developed amR, an integrated R package suite that provides a complete framework from bacterial genome data curation to interpretable AMR predictions, enabling identification of resistance mechanisms across species and drugs.
Results: The amR R package suite contains three modular packages. amRdata downloads genomes and paired antimicrobial susceptibility testing data from BV-BRC and processes them, constructs pangenomes, and extracts features at gene/protein cluster, protein domain, annotated Clusters of Orthologous Groups and ResFinder AMR-associated features, and structural variant scales; data are stored in memory-efficient formats (Parquet, DuckDB). amRml trains interpretable machine learning models per species-drug combination, calculates feature importance and performance metrics, and provides rich ground for hypothesis generation and mechanism discovery. amRviz provides an interactive Shiny dashboard to explore metadata distributions and model performance across species and drugs, visualize top predictive AMR features, and analyze cross-model patterns across geographic/temporal strata. We apply the suite to Shigella sonnei, achieving a median Matthews Correlation Coefficient of 0.89 across 23 drugs and drug classes. With thousands of genomes, multi-scale features, and interpretable models, amR provides an accessible, comprehensive framework for AMR research. The amR package suite is installable via GitHub (https://github.com/JRaviLab/amR; BSD-3-Clause license).
If you use the amR suite in your research, please cite:
Ghosh A^, Brenner EP^, Boyer EA, McKim AP, Vang CK, Wolfe EP, Mayer D, Lesiyon RL, Ravi J.
amR: an R package suite to predict antimicrobial resistance in bacterial pathogens.
bioRxiv. 2026. DOI: 10.64898/2026.07.10.734579.
^ Co-first authors
Looking for a cool application of this amR prediction framework? Check out our recent work on predicting AMR in ESKAPE pathogens: Ghosh^, Brenner^, Vang^, Wolfe^, et al., bioRxiv 2025.
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
BSD 3-Clause License. See LICENSE for details.
Corresponding author: Janani Ravi (janani.ravi@cuanschutz.edu)
Lab website: https://jravilab.github.io
Please note that amR is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.