Analyzing at Scale the Effects of Optimal Global Sequence Alignment on Sequence Similarity Using a GPU-Optimized Implementation of the Needleman-Wunsch Algorithm and the SBERT Module

Global sequence alignment remains a fundamental technique in bioinformatics, yet its large-scale application is often limited by computational demands. In this work, we explore the impact of optimal global sequence alignment on sequence similarity by combining a GPU-accelerated version of the Needleman–Wunsch algorithm with Sentence-BERT (SBERT), a transformer-based embedding model. This setup allows us to perform exhaustive alignments across large datasets while also capturing semantic similarities that traditional scoring schemes may miss. By comparing alignment scores with SBERT-derived similarity metrics, we examine where classical alignment aligns with—or diverges from—contextual similarity. Our results show that while there is general agreement between the two approaches, notable exceptions highlight cases where semantic meaning extends beyond structural alignment. The combination of efficient global alignment and deep semantic modeling offers new insights into the relationship between sequence form and function, opening the door for more nuanced similarity measures in both biological and textual sequence analysis.