Interpreting fungal ecological contributions through taxonomic and functional profiling of metatranscriptomics

Fungi are ubiquitous across nearly all habitats and play pivotal roles in maintaining ecological sustainability. Understanding the composition and functional roles of active fungal communities is essential for elucidating fundamental ecosystem processes. Fungal metatranscriptomics has emerged as a powerful approach for simultaneously characterizing the composition and in situ functional activities of mycobiomes in diverse ecosystems. However, the bioinformatic techniques and pipelines for metatranscriptomic analysis are still less developed to interpret fungal ecological functions in ecosystems. In this review, we critically synthesize recent technological advances and persistent challenges in the analysis of metatranscriptomic data. Focusing specifically on taxonomic and functional classification, we systematically categorize current workflows into two main computational strategies; assembly-based and alignment-based methods, according to their analytical pipelines and tool implementations. Standardized comparisons show assembly-based pipelines deliver higher taxonomic and functional resolution for fungal metatranscriptomics but require heavy computational resources, while alignment-based pipelines are fast and scalable with fungal-specific marker-based tools performing better, though the performance largely relies on the database comprehensiveness. Notably, major hurdles include fungal transcriptome complexity, database biases, and a lack of standardized analytical workflows, thereby limiting broader application and reproducibility. Looking forward, future progress hinges on integrating long-read sequencing, expanding fungal-specific genomic and functional databases, and leveraging artificial intelligence to overcome current limitations in annotation and classification. Collectively, these developments are crucial for transitioning fungal metatranscriptomics from a descriptive ecology to a predictive science.