Hiroshima University Scientists Engineer Fungal-Specific RNA-Seq Workflow to Map Gene Functions Without High-Quality Reference Genomes

Hiroshima University researchers develop a fungal-specific workflow to accurately map gene functions in non-model species, aiding CRISPR and biotech research.

By: AXL Media

Published: Mar 31, 2026, 3:21 AM EDT

Source: Information for this report was sourced from Hiroshima University

Addressing the Functional Gap in Fungal Transcriptomics

While next-generation sequencing has made profiling active genes increasingly accessible, determining the specific biological functions of those genes in fungi remains a significant technical bottleneck. Most generalized software tools are optimized for broad categories of life and often fail to capture the unique genetic architecture of the fungal kingdom. Professor Hidemasa Bono of Hiroshima University notes that these general-purpose tools leave a high proportion of fungal genes functionally uncharacterized, an obstacle that has historically hindered downstream applications like medical research and industrial fermentation.

Bypassing the Need for Reference Genomes

A primary challenge in studying non-model fungal species is the lack of high-quality reference genomes, which traditional analysis methods rely on to map active genes. The Hiroshima University team addressed this by creating a fungal-specific workflow that supports functional analysis regardless of whether a genome has been previously sequenced. By comparing RNA sequences against specialized fungal databases rather than a single reference map, the tool can identify active proteins and their roles in species that have never been studied in a laboratory setting.

Validation Through Edible Mushrooms and Crop Pathogens

To evaluate the versatility of the new tool, the researchers processed data from two biologically distinct organisms: the widely cultivated shiitake mushroom and Asian soybean rust, a devastating agricultural pathogen. The study, published in the Journal of Fungi, demonstrated that the workflow maintained high accuracy across these diverse fungal lifestyles. By successfully annotating over 96 percent of protein-coding transcripts, the system provided a much higher resolution of functional detection than existing tools, revealing biologically meaningful differences that were previously overlooked.