RNA sequencing (RNA-seq) has become a foundational method for identifying which genes are active within an organism. However, connecting these gene expression profiles to actual biological functions in fungi has been technically difficult. Many existing software tools are designed to handle a wide variety of species, which limits their ability to accommodate the unique genetic characteristics of fungi. Adding to this challenge, a large number of non-model fungal species lack the complete, high-quality reference genomes that most traditional analytical tools depend upon.
To overcome these limitations, a research team led by Professor Hidemasa Bono at Hiroshima University developed a workflow tailored specifically for fungi. The system is designed to support downstream functional analysis even in cases where a reference genome is unavailable. According to Bono, “While RNA sequencing has become easier with next-generation sequencers, existing general-purpose tools fail to capture fungal-specific features, leaving a high proportion of genes functionally uncharacterized. This limitation has been a major obstacle for downstream analyses such as functional enrichment analysis and genome editing applications.” The team’s workflow addresses this gap by improving the accuracy of functional interpretation in fungal transcriptomes and helping identify biologically significant genes that had not been previously recognized.
The study, published in the Journal of Fungi, evaluated the workflow using RNA-seq data from 57 samples of shiitake mushroom (Lentinula edodes strain H600) and 20 samples from the plant pathogen Asian soybean rust (Phakopsora pachyrhizi). These two organisms, representing both an edible mushroom and an agricultural pathogen, provided a meaningful test of the method’s performance. The workflow accurately annotated more than 96% of protein-coding transcripts, surpassing current general annotation tools in functional detection.
In addition to accuracy, the system showed flexibility by handling both short-read RNA-seq and full-length transcript sequencing (Iso-Seq) data. Rather than relying on pre-existing reference genomes, it matched sequences with specialized fungal databases and analyzed patterns of gene expression. Bono explained, “This improvement enables more comprehensive functional enrichment analysis that better reflects actual biological phenomena occurring during fungal development and pathogenesis.”
The new workflow allows researchers to identify functionally important transcripts in a wide range of fungal species. It supports applications such as CRISPR-based genome editing and advances biotechnological exploration, offering a refined approach for understanding fungal biology across diverse species.
Publication details: Morihara N, Bono H. Functional Annotation Workflow for Fungal Transcriptomes. Journal of Fungi. 2026; 12(2):116. https://doi.org/10.3390/jof12020116
To overcome these limitations, a research team led by Professor Hidemasa Bono at Hiroshima University developed a workflow tailored specifically for fungi. The system is designed to support downstream functional analysis even in cases where a reference genome is unavailable. According to Bono, “While RNA sequencing has become easier with next-generation sequencers, existing general-purpose tools fail to capture fungal-specific features, leaving a high proportion of genes functionally uncharacterized. This limitation has been a major obstacle for downstream analyses such as functional enrichment analysis and genome editing applications.” The team’s workflow addresses this gap by improving the accuracy of functional interpretation in fungal transcriptomes and helping identify biologically significant genes that had not been previously recognized.
The study, published in the Journal of Fungi, evaluated the workflow using RNA-seq data from 57 samples of shiitake mushroom (Lentinula edodes strain H600) and 20 samples from the plant pathogen Asian soybean rust (Phakopsora pachyrhizi). These two organisms, representing both an edible mushroom and an agricultural pathogen, provided a meaningful test of the method’s performance. The workflow accurately annotated more than 96% of protein-coding transcripts, surpassing current general annotation tools in functional detection.
In addition to accuracy, the system showed flexibility by handling both short-read RNA-seq and full-length transcript sequencing (Iso-Seq) data. Rather than relying on pre-existing reference genomes, it matched sequences with specialized fungal databases and analyzed patterns of gene expression. Bono explained, “This improvement enables more comprehensive functional enrichment analysis that better reflects actual biological phenomena occurring during fungal development and pathogenesis.”
The new workflow allows researchers to identify functionally important transcripts in a wide range of fungal species. It supports applications such as CRISPR-based genome editing and advances biotechnological exploration, offering a refined approach for understanding fungal biology across diverse species.
Publication details: Morihara N, Bono H. Functional Annotation Workflow for Fungal Transcriptomes. Journal of Fungi. 2026; 12(2):116. https://doi.org/10.3390/jof12020116