An organism’s genome is the complete set of genetic material within a cell, acting as the blueprint for its development, function, and reproduction. Advances in next-generation sequencing have made genome sequencing faster and more cost effective, enabling researchers to obtain complete genomic data and identify novel genes for functional characterisation and genome editing.

Fungi are an exceptionally diverse group of organisms found across nearly all environments, where they play essential roles in ecosystem processes. Despite this, fungal genomes remain relatively uncharacterised compared to other kingdoms of life. Many fungal genes lack functional annotation, limiting our understanding of their biological roles and their potential applications in industry.

A recent study published in the Open Access Journal of Fungi addresses this gap by developing a workflow to elucidate the functions of previously uncharacterised fungal genes. By improving the interpretation of fungal genetic data this approach could accelerate discoveries in biotechnology, agriculture, and environmental science.

Professor Hidemasa Bono, author of the study, hopes the findings of the study can open doors to future research:

“Our approach enables accurate functional interpretation of fungal transcriptomes and facilitates the identification of biologically meaningful genes that were previously overlooked.”

Challenges interpreting RNA sequencing data in fungi

In modern biology, RNA sequencing is a widely used tool which allows researchers to identify genes that are actively expressed in a cell at a given time. This provides valuable insights into how organisms may respond to different external factors such as stress and what genes may be important at different stages of an organism’s lifecycle.

However, RNA sequencing alone does not elucidate gene function due to the vast amount of raw data it produces. Instead, accurate functional annotation is essential for interpretation of biological function.

This challenge is extremely relevant in fungi. Compared to well-studied organisms, such as humans or mice, fungi lack high-quality reference genomes to compare data to. Instead, annotation relies on species that are distant relatives or haven’t been extensively studied.

Additionally, fungi possess distinct metabolic pathways and gene clusters, that are absent in other species and may not be well studied. As a result, many fungal genes remain poorly understood or completely uncharacterised. Prof. Bono highlights these limitations:

“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 uncharacterised.”

A novel fungal-specific workflow

To address the limitations associated with annotating fungal genomes, this study developed a workflow specifically tailored to fungal genomes. Unlike other tools, this approach is designed to work even when no reference genome is available.

The workflow combines four main components:

• Sequence comparison to compare genes to well-characterised, similar proteins that predict their function.
• Functional assignment to assign labels that describe what the protein does and where it acts in the cell.
• Feature identification to detect common structural elements within proteins that provide clues about their functions.
• Activity profiling to connect gene expression to specific biological patterns that describes conditions when the gene may be active.

The researchers compared their newly designed workflow with a traditional approach that matches proteins to known sequences in a large database. Overall, integrating these approaches into a combined workflow provides a more comprehensive and accurate picture of fungal gene function than existing approaches.

Testing the workflow on real fungal genome data

To evaluate the effectiveness of the developed workflow, the authors applied it to RNA sequencing data from two distinct fungal species:

• Lentinula edodes (shiitake mushroom), a food and mushroom used in traditional Chinese medicine.
• Phakopsora pachyrhizi, the pathogen responsible for Asian soybean rust.

In both cases, the workflow successfully annotated over 96% of protein-coding gene sequences, substantially outperforming traditional fungal genome annotation methods, which typically annotate only around 66% of genes.

More importantly, the workflow provided a higher level of functional insight. Instead of only identifying broad biological categories it was able to detect specific cellular processes, such as:

• Cellular structure shifts such as functions related to the cell membrane / cell wall remodelling during transition from mycelium to fruiting body.
• Identification of numerous fungal metabolic pathways.
• Protein regulation mechanisms in fungi.

These findings unveil some key mechanisms and provide researchers with a better understanding of how fungi grow, adapt, and interact with their environments.

Discovering hidden biological signals

One of the most effective aspects of the workflow is its ability to uncover previously overlooked genes. In particular, very specific biological functions, linked to a small number of genes, could be identified. Processes involving manageable numbers of genes are extremely desirable for follow-up research as they are easier to validate experimentally.

In the dataset for shiitake mushrooms, the analysis revealed genes linked to developmental stages, such as the transition from mycelium to fruiting body. Traditional methods prioritised genes relating to metabolic pathways, however, these results were related to more general processes and are more difficult to interpret. Some of the novel genes highlighted are associated with stress responses and reproduction, making them strong candidates for further studies.

Similarly, in the soybean rust pathogen, the workflow identified genes involved in infection-related processes. These findings could be used by researchers to further study how fungal pathogens infect crops and how they might be controlled.

Many of these discoveries came from fungal-specific databases, highlighting how general genome analysis tools can overlook important information unique to fungi. This underscores the importance of developing specialised workflows to better uncover and understand fungal biological mechanisms.

Applications of the developed workflow

Improved functional annotation of organism genomes have practical implications beyond basic research. By identifying genes involved in specific processes researchers have the potential to:

• Select targets for genome editing (using techniques such as CRISPR).
• Promote genes that enhance desirable traits in fungi, such as mushroom growth or metabolite production.
• Target certain genes to develop strategies that combat fungal diseases in crops.
• Discover novel enzymes that may have industrial use.

This ability to more accurately pinpoint functionally important genes opens the door to future research avenues, enabling more efficient functional studies and more targeted applications such as genome editing and pathway characterisation.

Expanding our understanding of fungal biology

Despite the success of the developed workflow, the study highlights how much remains unknown about fungal genomes. Many identified genes still lack experimental validation, and functional predictions often rely on similarities to other organisms. Nevertheless, this research represents an important advancement. By tailoring analytical tools to the unique biology of fungi, researchers can extract far more meaningful insights from existing data.

As sequencing technologies continue to advance and more datasets become available, approaches like the one demonstrated here can play a crucial role in transforming raw genetic information into real-world applications.

By refining how RNA sequencing data are interpreted, this workflow helps uncover hidden patterns within complex datasets, advancing our understanding of fungal biology. In doing so, it opens new opportunities for innovation in medicine, agriculture, and biotechnology.

More studies on genetics and phenotype prediction can be found across the Open Access journals Genes and Journal of Fungi. Alternatively, you can access the full MDPI journal list here.