Artificial Intelligence: Using BLAST Algorithm for DNA Classification of Thicket Vegetation

Abstract

Artificial Intelligence (AI) has revolutionized molecular ecology by enhancing DNA classification methods. This study explores the application of the Basic Local Alignment Search Tool (BLAST) algorithm, an AI-powered bioinformatics tool, for DNA classification within the unique ecosystem of Eastern Cape Thickets. Our focus was on leveraging BLAST's capabilities to identify and characterize plant species based on genetic sequences. We collected plant samples from diverse locations within the Eastern Cape Thickets and employed advanced DNA extraction and sequencing techniques to acquire a comprehensive dataset. BLAST analysis played a central role in comparing our sequences with the GenBank database to determine species identities and potential functions. The BLAST algorithm demonstrated remarkable efficiency in identifying known species, confirming their presence in the thicket ecosystem. Notably, BLAST's utility extended beyond species confirmation, as it unveiled sequences with limited matches, suggesting the existence of novel genetic elements specific to the region. By harnessing AI, BLAST facilitated accurate species classification and functional annotation. Our study showcases the potency of AI-driven DNA classification using the BLAST algorithm in elucidating the genetic makeup of the Eastern Cape Thickets. The algorithm's proficiency in decoding intricate genetic information, elucidating functional attributes, and predicting potential adaptations underscores its pivotal role in modern molecular ecology research. These findings contribute to the broader understanding of genetic diversity within this ecosystem and highlight the transformative impact of AI-driven bioinformatics tools on ecological studies and conservation strategies.