Lossless DNA Sequence Compression: Exploring Spatial and Sequential Redundancies of DNA Sequence with 2-D CNN Autoencoder

The volume of genomic data being generated is growing due to the technological breakthroughs in genome sequencing and the economic affordability of sequencing. Consequently, the need for compression techniques that are specifically designed for genomic data is rising. Deep learning provides robust methods for reducing the data size and unlocking the full potential of genomic information. The proposed method minimizes storage requirements by applying a 2-D convolutional neural network autoencoder, which learns spatial and sequential redundancies in the sequence to compress the data losslessly. In contrast to conventional compression methods, which handle data as a one-dimensional sequence, the proposed approach uses the spatial structure of the data to produce more compact representations. We evaluated the proposed method with various compression approaches, including state-of-the-art DNA sequence compression algorithms, on two homo sapiens genomes. Experimental results indicate that the proposed method can compress the genomic data effectively, with an improvement of 31.3% compression over the best existing method.