László Irinyi

The paper compares methods for identifying fungal species from sequencing data and finds that aligning sequences to a fungi-specific database with coverage cutoffs improves accuracy.

This paper demonstrates that linked machine learning classifiers improve species classification of fungi using error-prone long-reads on extended metabarcodes, achieving high recall and low false positives.

This study evaluates metatranscriptomics for identifying fungal species and subspecies in mixed communities using a mock community of 17 fungal isolates, showing high accuracy at species and strain levels when low-abundance taxa are excluded.