Fungar: a pipeline for detecting antifungal resistance mutations directly from metagenomic short reads

Abstract Background Antifungal resistance has become an increasing global concern in both clinical and environmental health. Detecting known resistance mutations directly from metagenomic short-read sequencing data remains a major challenge. Most available tools are designed for bacterial taxa, whereas tools targeting fungi typically require assembled genomes. In metagenomic datasets, assembly-based strategies may result in substantial information loss due to genome fragmentation, low-abundance species, or incomplete recovery of resistance loci. Results Here, we present FUNGAR, an open-source pipeline for the rapid identification of antifungal resistance genes and mutations directly from short-read data. FUNGAR employs translated alignments with DIAMOND and curated data from the FungAMR database to detect amino acid substitutions across all six open reading frames. The pipeline includes a configurable read-support threshold (a value of at least 3 is recommended for metagenomic data), paired-end mate-concordance filtering to reduce false positives, automatic classification of variants by drug-class context (clinical vs. agricultural), and a self-contained HTML report. A companion benchmarking script evaluates precision and false-positive rate across a range of sequencing depths using stochastically generated synthetic reads. Results are compiled into structured, reproducible reports linking detected variants to their associated antifungal compounds. Conclusions To our knowledge, FUNGAR is the first pipeline that detects and annotates known mutations in antifungal resistance genes directly from short-read sequencing data, providing a fast, reproducible, and extensible framework for monitoring emerging antifungal resistance mechanisms in both genomic and metagenomic samples.