BioAlignNet: A GPU‐Accelerated Framework for Efficient Global Sequence Alignment Across Genomics and Proteomics Scales

Sequence alignment is one of the fundamental concepts in bioinformatics used in genome assembly, molecular evolution, and drug designing. Traditional algorithms like Needleman–Wunsch (NW) and Smith–Waterman (SW) are precise but computationally expensive. This renders them impractical whenever dealing with large‐scale datasets resulting from next‐generation sequencing (NGS) technologies. Towards addressing this challenge, we propose BioAlignNet, a novel GPU‐accelerated framework designed for efficient global sequence alignment of DNA/RNA/Protein sequences. BioAlignNet utilizes graph theory to model the alignments as shortest‐path problems in directed weighted graphs. Through incorporating an optimized and parallel edge weight computation and a parallel Shortest Path Faster Algorithm (SPFA), the framework achieves low computational complexity of where and are the lengths of the input sequences, and is the number of GPU threads per block. Rather than introducing a new alignment scoring scheme, BioAlignNet contributes a formally verified execution model for exact global alignment by recasting dynamic programming as a shortest‐path problem whose correctness is preserved under asynchronous GPU parallelism. Along with the low computational complexity, the framework also maintains biological integrity. Promising innovations include the implementation of the modular concept to accommodate different sequence types, substitution matrices, and scoring schemes, CUDA‐based parallelism of edge weight calculation, and traceback methods to build accurate alignments. BioAlignNet's evaluations conducted on various datasets show a remarkable runtime enhancement reaching up to 177 times speedup over CPU implementations, maintaining alignment optimality while giving better coverage and percentage identity. Experimental evaluations on real‐world datasets and validations through statistical significance tests demonstrate its scalability and robustness, making it suitable for genomics and proteomics. With its incorporation of biological relevance and computational optimization, BioAlignNet offers an efficient method for the alignment of large‐scale genome sequences to promote the development of molecular biology research.