Identification of pathogenic fungi causing ocular infections using full rRNA operon sequencing with Oxford Nanopore Technologies

Although fungal eye infections are a major cause of visual impairment worldwide, standard clinical laboratory methods remain slow, insensitive, and limited in their taxonomic resolution. Sequencing of the full ribosomal RNA (rRNA) operon provides a comprehensive marker for fungal identification. In this study, twenty fungal isolates associated with ocular infections were obtained from Srinagarind Hospital, Thailand, and characterized using four identification approaches. Initial hospital-based routine identification relied on conventional morphological methods and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). To enhance resolution and to develop a comprehensive analytical pipeline, we further employed full rRNA operon sequencing using Oxford Nanopore Technologies (ONT), analyzed through three bioinformatic pipelines: EPI2ME/Minimap2, NGSpeciesID with BLASTn, and internal transcribed spacer (ITS)-based phylogenetic analysis coupled with phylogenetic analysis. All isolates yielded complete operon sequences, thus ensuring comprehensive coverage of the target regions. NGSpeciesID produced high-confidence consensus sequences and species-level classifications for nearly all isolates (except one <i>Candida</i> specimen). Of these, 15 of the 20 isolates showed exhibited concordance with hospital identifications at the genus level (≥97% identity). This approach successfully resolved closely related <i>Aspergillus</i> taxa (<i>i.e.</i>, <i>A. terreus</i>, <i>A. luchuensis</i>, <i>A. oryzae</i>), reclassified <i>Curvularia</i> isolates as <i>Bipolaris maydis</i>, and confirmed species-level assignments for <i>Fusarium</i> and <i>Rhodotorula</i>. By contrast, the EPI2ME workflow produced more variable classifications, providing species-level assignments for <i>Aspergillus</i> and <i>Rhodotorula</i> but mixed genus/species profiles for several isolates, including seven isolate assignments unique to this method. ITS-based phylogenetic reconstruction recovered all expected clades, with <i>Curvularia</i> isolates clustering within their genus. However, node support varied substantially, highlighting the limited discriminatory power of ITS alone, which constrains taxonomic resolution to the species-complex level rather than consistently achieving the species-level identification of <i>Aspergillus</i> isolates. Overall, ONT-based full-operon sequencing demonstrates strong potential for fungal diagnostics, its performance depends on bioinformatic pipelines, database quality, and sequencing errors. Species-level resolution is particularly limited in <i>Aspergillus</i>, while incomplete reference datasets hinder the classification of isolates such as <i>Curvularia</i>. To improve reliability and clinical application, it will be essential to expand curated full-length rRNA references, integrate complementary loci, and refine analytical strategies.