Genetic Sequence compression using Machine Learning and Arithmetic Encoding Decoding Techniques

We live in a period where a significant quantity of genomic data is being produced as a result of the advancement of high-throughput genome sequencing technology, raising concerns about the costs associated with data storage and transmission. The question of properly compressing data from genomic sequences is still a concern among researchers. Previously several researchers have proposed some compression methods for DNA Compression with a deep learning approach. Extending previous research, we propose a new architecture, like modified DeepDNA, and a new methodology, deploying a double-based strategy to compress DNA sequences. We validated the results by experimenting on three sizes of datasets: 100, 243, and 356. The experimental outcomes highlight our improved approach’s superiority over existing approaches for analyzing human mitochondrial genome data.