Bioinformatics provides powerful methods for addressing biotechnological challenges, particularly in the identification of microorganisms at the molecular level. This study focuses on the application of BLAST (Basic Local Alignment Search Tool) for species-specific identification of fungi. BLAST performs sequence analysis through pairwise or multiple comparisons of nucleic acids and other genetic sequences, providing key bioinformatic insights. Analyses were conducted using publicly available genetic databases such as GenBank and YeastGenome. Traditional fungal identification methods rely heavily on phenotypic characteristics, which can be ambiguous due to overlapping traits among closely related species. In medical mycology, misidentification poses serious risks, potentially leading to incorrect treatment. For demonstrative purposes, a complete genome sequence of a fungal organism from GenBank was analyzed, and BLAST results matched Aspergillus niger, with a maximum score of 4.093 × 10⁵, an E value of 0.0, and 100% identification accuracy. The study highlights the advantages of molecular-based bioinformatic approaches for rapid, accurate, and reliable fungal identification. Continued development of precise algorithms and reduced analytical costs are anticipated to enhance fungal taxonomy and diagnosis in research and clinical settings.
Mercy Ijeoma Eze (Sun,) studied this question.