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

TL;DR

FUNGAR is a new open-source pipeline that detects antifungal resistance mutations directly from metagenomic short reads, overcoming assembly limitations and aiding rapid resistance profiling in fungi.

Contribution

It introduces a novel method for identifying resistance mutations directly from short reads, tailored for fungi, using translated alignments and curated resistance data.

Findings

Detects resistance mutations directly from metagenomic data

Produces structured reports linking variants to antifungal drugs

Easily customizable for new species or resistance databases

Abstract

Motivation: Antifungal resistance has become an increasing global concern in both clinical and environmental health. Detecting known resistance mutations directly from sequencing reads, in special metagenomic samples, remains a major challenge. As fungal pathogens are often neglected compared with bacterial pathogens, 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 produces structured, reproducible reports linking detected variants to their associated antifungal drugs and can be easily customized for new species or databases.