BIOINFORMATICS IN MYCOLOGY: TECHNIQUES FOR ACCURATE FUNGAL IDENTIFICATION

Bioinformatics employs computational methods to address complex biotechnological problems and represents a key outcome of molecular-level genetic research. This paper focuses on applying the Basic Local Alignment Search Tool (BLAST) for fungal identification. BLAST performs sequence analysis through pairwise or multiple comparisons of nucleic acids (NAs) and other sequences, offering significant bioinformatic insights. Unlike conventional phenotypic methods, which often misidentify closely related species and pose risks in medical mycology, BLAST utilizes genetic information to achieve species-specific identification with greater accuracy. In this study, a complete fungal genome sequence from GenBank (used here illustratively instead of 18S rRNA) was analyzed, yielding hits corresponding to Aspergillus niger based on the maximum score (4.093e+05), E-value (0.0), and 100% identification match. The results highlight the potential of bioinformatic approaches for precise fungal identification and taxonomy. With ongoing development of more functional and accurate algorithms, bioinformatics is increasingly advocated for rapid, reliable, and cost-effective mycological analyses. Improved computational techniques are essential to meet the growing demand for authentic and timely results in fungal research