Complexity-Configurable Learning-based Genome Compression

In this paper, we propose the complexity configurable learning-based genome data compression method, in an effort to achieve a good balance between coding complexity and performance in lossless DNA compression. In particular, we first introduce the concept of Group of Bases (GoB), which serves as the foundation and enables the parallel implementation of the learning-based genome data compression. Subsequently, the Markov model is introduced for modeling the initial content, and the learning-based inference is achieved for the remaining base data. The compression is finally achieved with efficient arithmetic coding, and based upon a set of configurations on compression ratios and inference speed, the proposed method is shown to be more efficient and provide more flexibility in real-world applications.