Amira: detection of single and multi-copy antimicrobial resistance genes directly from long reads using gene-space de Bruijn graphs
Abstract
Accurate detection of antimicrobial resistance (AMR) genes is essential for AMR surveillance and genotypic prediction. Current approaches typically either depend on a correct genome assembly or use k-mers directly from the reads. However AMR genes are often multicopy and are enriched at repeat loci where genome assemblers tend to fail, and k-mer methods struggle to distinguish different copies. We present Amira, which detects AMR genes directly from long reads by building gene-space de Bruijn graphs, allowing precise association of reads with distinct AMR gene copies, enabling accurate assembly. Across simulations, curated samples, and public datasets, Amira improves copy-number recall and nucleotide accuracy compared with existing methods.
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BibTeX
@article{anderson2026amira,
title = {{Amira: detection of single and multi-copy antimicrobial resistance genes directly from long reads using gene-space de Bruijn graphs}},
author = {Daniel Anderson and Leandro Lima and Trieu Le and Louise Judd and Ryan Wick and Zamin Iqbal},
journal = {Genome Biology},
year = {2026},
month = oct,
doi = {10.1186/s13059-026-04301-w},
url = {https://link.springer.com/article/10.1186/s13059-026-04301-w},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Amira: detection of single and multi-copy antimicrobial resistance genes directly from long reads using gene-space de Bruijn graphs
AU - Daniel Anderson
AU - Leandro Lima
AU - Trieu Le
AU - Louise Judd
AU - Ryan Wick
AU - Zamin Iqbal
PY - 2026
DA - 2026/10/17
JO - Genome Biology
DO - 10.1186/s13059-026-04301-w
UR - https://link.springer.com/article/10.1186/s13059-026-04301-w
AB - Accurate detection of antimicrobial resistance (AMR) genes is essential for AMR surveillance and genotypic prediction. Current approaches typically either depend on a correct genome assembly or use k-mers directly from the reads. However AMR genes are often multicopy and are enriched at repeat loci where genome assemblers tend to fail, and k-mer methods struggle to distinguish different copies. We present Amira, which detects AMR genes directly from long reads by building gene-space de Bruijn graphs, allowing precise association of reads with distinct AMR gene copies, enabling accurate assembly. Across simulations, curated samples, and public datasets, Amira improves copy-number recall and nucleotide accuracy compared with existing methods.
SN - 1474-760X
ER -