Update authorship and README citation for the amR suite preprint

DESCRIPTION

@@ -8,6 +8,8 @@ Authors@R: c(
     person("Abhirupa", "Ghosh", , "abhirupa.ghosh@cuanschutz.edu", role = "ctb"),
     person("Ethan", "Wolfe", , "ethan.wolfe@cuanschutz.edu", role = "ctb"),
     person("Emily", "Boyer", , "emily.boyer@cuanschutz.edu", role = "ctb"),
+    person("Alexander", "McKim", , "alexander.mckim@cuanschutz.edu", role = "ctb",
+           comment = c(ORCID = "0000-0002-7802-7591")),
     person("Charmie", "Vang", , "charmie.vang@cuanschutz.edu", role = "ctb"),
     person("Raymond", "Lesiyon", , "raymond.lesiyon@cuanschutz.edu", role = "ctb"),
     person("David", "Mayer", , "david.mayer@cuanschutz.edu", role = "ctb")

README.Rmd

@@ -99,13 +99,34 @@ library(amRviz)
 
 ## Citation
 
+**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](https://doi.org/10.64898/2026.07.10.734579)
+
+^ _Co-first authors_
+
+*Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz*
+
+<details>
+<summary>Abstract</summary>
+
+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).
+
+</details>
+
+### How to cite
+
 If you use the amR suite in your research, please cite:
 
-```
-Brenner EP^, Ghosh A^, Wolfe EP, Boyer EA, Vang CK, Lesiyon RL, Mayer DA, Ravi J. (2026).
-amR: An R package suite for antimicrobial resistance prediction in bacterial pathogens.
-https://github.com/JRaviLab/amR
-```
+> 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](https://doi.org/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](https://doi.org/10.1101/2025.07.03.663053).
 

README.md

@@ -87,11 +87,72 @@ library(amRviz)
 
 ## Citation
 
+**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](https://doi.org/10.64898/2026.07.10.734579)
+
+^ *Co-first authors*
+
+*Department of Biomedical Informatics, Center for Health Artificial
+Intelligence, University of Colorado Anschutz*
+
+<details>
+
+<summary>
+
+Abstract
+</summary>
+
+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).
+
+</details>
+
+### How to cite
+
 If you use the amR suite in your research, please cite:
 
-    Brenner EP^, Ghosh A^, Wolfe EP, Boyer EA, Vang CK, Lesiyon RL, Mayer DA, Ravi J. (2026).
-    amR: An R package suite for antimicrobial resistance prediction in bacterial pathogens.
-    https://github.com/JRaviLab/amR
+> 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](https://doi.org/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^,