Enhanced Hybrid Adaptive DNA Compression: Accelerating Genomic Data Compression through Parallel Processing - Thammuluri, Rajesh; Gottala Surendra Kumar; Bellamgubba Anoch; Ramesh Babu Mallela; Anuj Rapaka; Veera V. Rama Rao M. (International Journal of Computational and Experimental Science and Engineering, 2025)
The exponential growth in genomic data due to advancements in sequencing technologies has necessitated the development of efficient compression algorithms tailored to the unique characteristics of DNA sequences. This paper presents an enhanced version of the Hybrid Adaptive DNA Compression (HADC) algorithm, originally introduced by Elnady et al. [1]. The enhanced HADC leverages parallel processing techniques, including multithreading, to optimize computationally intensive tasks such as k-mer hash table construction and action sequence generation. Experimental results demonstrate significant improvements, including a 30% reduction in compression time for datasets such as HS8 and TAIR10, while maintaining the original algorithm’s high compression ratio. These improvements ensure scalability and computational efficiency, addressing the growing demands of genomic data compression. The proposed enhancements, validated through quantitative analysis across diverse datasets, confirm the robustness of the improved HADC algorithm in processing large-scale genomic data, making it a valuable tool for bioinformatics applications