Abstract

Fungal eye infections are a significant cause of visual impairment worldwide, yet standard clinical laboratory methods are often time-consuming, insensitive, and limited in taxonomic resolution. Sequencing the full-length 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 complementary analytical workflows. 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-length rRNA operon sequencing (4.8–5.4 kb) using Oxford Nanopore Technologies (ONT), analyzed through three bioinformatic pipelines; (i) EPI2ME with Minimap2 alignment against the NCBI 16S_18S_28S_ITS database; (ii) NGSpeciesID with BLASTn searches against the curated NCBI Fungal RefSeq database; and (iii) ITS region–based consensus reconstruction (derived from method ii) coupled with phylogenetic analysis. All isolates yielded complete operon sequences, ensuring comprehensive coverage of the target regions. NGSpeciesID produced high-confidence consensus sequences and species-level classifications for all isolates (except one mixed Candida specimen). Of these, 15 out of 20 isolates showed concordance with hospital identifications at the genus level (≥97% identity). This approach successfully resolved closely related Aspergillus taxa (A. terreus, A. luchuensis, A. oryzae), reclassified Curvularia isolates as Bipolaris maydis, and confirmed species-level assignments for Fusarium and Rhodotorula. By contrast, the EPI2ME workflow generated more heterogeneous outputs: while it provided species-level assignments for Aspergillus and Rhodotorula, several groups displayed mixed genus/species profiles, and seven isolates were assigned genera reported exclusively by this method. ITS-based phylogenetic reconstruction recovered all expected clades, with Curvularia 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 species-level identification in Aspergillus isolates. ONT-based full-operon sequencing shows clear diagnostic potential for fungal diagnostics, but its accuracy is constrained by bioinformatic pipelines, database quality, and sequencing errors. Species-level resolution is particularly limited in Aspergillus , while incomplete reference datasets hinder classification of isolates like Curvularia . Expanding curated full-length rRNA references, integrating complementary loci, and refining analytical strategies will be essential to improve reliability and clinical application.