Multiple hypervariable markers improve mycobiome classification in metatranscriptome and metagenome data

Profiling the taxonomic and functional composition of mycobiome using metagenomic and metatranscriptomic sequencing is advancing our understanding of fungal functions in ecosystems. However, the sensitivity and accuracy of mycobiome classification using genome- or core protein-based approaches, is limited by the availability of reference genomes and the resolution of sequence databases. To address this, we propose the MicroFisher, a novel tool to identify taxonomically useful reads from metagenomic or metatranscriptomic data, enabling taxonomic identification of community members based on multiple hypervariable markers. We applied MicroFisher to profile the simulated fungal communities to assess the performance of the developed tool, and found higher performance in fungal prediction and abundance estimation compared to existing tools. In addition, we also used metagenomes from forest soil and metatranscriptomes of root eukaryotic microbes to test our method and found that MicroFisher provided more accurate profiling of environmental microbiomes compared to other classification tools. MicroFisher leverages high-resolution hypervariable marker gene databases and weighted integration algorithms to deliver more accurate fungal community classification compared to existing state-of-the-art tools. Additionally, it enables the detection of rare taxa, which is challenging with other available tools. Thus, MicroFisher serves as a novel pipeline for classification of fungal communities from metagenomes and metatranscriptomes.