A novel lossless encoding algorithm for data compression-genomics data as an exemplar.

ABSTRACT: Data compression is a challenging and increasingly important problem. As the amount of data generated daily continues to increase, efficient transmission and storage have never been more critical. In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. The proposed algorithm follows a divide-and-conquer approach by scanning the whole genome, classifying subsequences based on similarities in their content, and binning similar subsequences together. The data is then compressed into each bin independently. This approach is different than the currently known approaches: entropy, dictionary, predictive, or transform-based methods. Proof-of-concept performance was evaluated using a benchmark dataset with seventeen genomes ra

SUBMITTER: Al-Okaily A 

PROVIDER: S-EPMC11799261 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature