Enhanced Hybrid Adaptive DNA Compression: Accelerating Genomic Data Compression through Parallel Processing

Authors

  • Rajesh Thammuluri Shri Vishnu Engineering College for Women
  • Gottala Surendra Kumar
  • Bellamgubba Anoch
  • Ramesh Babu Mallela
  • Anuj Rapaka
  • Veera V. Rama Rao M.

DOI:

https://doi.org/10.22399/ijcesen.943

Keywords:

DNA compression, genomic data, multithreading, hybrid adaptive compression, scalable algorithms

Abstract

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

References

Elnady, S., Sayed, S., & Salah, A. (2022). HADC: A hybrid compression approach for DNA sequences. Journal of Software Engineering and Applications. doi: 10.1109/ACCESS.2022.3212523

Wei, D., & Jiang, M. (2021). A fast image encryption algorithm based on parallel compressive sensing and DNA sequence. Optik, 238, 166748.

Cao, Y., Tan, L., Xu, X., & Li, B. (2024). A universal image compression sensing–encryption algorithm based on DNA-triploid mutation. Mathematics, 12(13), 1990.

Cao, Y., Tan, L., Xu, X., & Li, B. (2024). A universal image compression sensing–encryption algorithm based on DNA-triploid mutation. Mathematics, 12(13), 1990.

Lan, D., Tobler, R., Souilmi, Y., & Llamas, B. (2021). Genozip: A universal extensible genomic data compressor. Bioinformatics, 37(16), 2225–2230.

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Published

2025-03-04

How to Cite

Thammuluri, R., Gottala Surendra Kumar, Bellamgubba Anoch, Ramesh Babu Mallela, Anuj Rapaka, & Veera V. Rama Rao M. (2025). Enhanced Hybrid Adaptive DNA Compression: Accelerating Genomic Data Compression through Parallel Processing. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.943

Issue

Vol. 11 No. 1 (2025): IJCESEN

Section

Research Article

License

Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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