Anas Al-okaily ^1* Abdelghani Tbakhi ²

• ¹ King Hussein Cancer Center, Amman, Jordan
• ² McMaster University, Hamilton, Ontario, Canada

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 ranging in size from kilobytes to gigabytes.The results showed a considerable improvement in the compression of each genome, preserving several megabytes compared to state-of-the-art tools. Moreover, the algorithm can be applied to the compression of other data types include mainly text, numbers, images, audio, and video which are being generated daily and unprecedentedly in massive volumes.The importance of data compression, a fundamental problem in computer science, information theory, and coding theory, continues to increase as global data quantities expand rapidly. The primary goal of compression is to reduce the size of data for subsequent storage or transmission. There are two common types of compression algorithms: lossless and lossy. Lossless algorithms guarantee exact restoration of the original data, whereas lossy algorithms do not. Such losses are caused, for instance, by the exclusion of unnecessary information, such as metadata in video or audio that will not be observed by users.