BACKGROUND In recent years, with the development of high-throughput genome sequencing technologies, a large amount of genome data has been generated, which has caused widespread concern about data storage and transmission costs. However, how to effectively compression genome sequences data remains an unsolved problem. RESULTS In this paper, we propose a compression method using machine learning techniques (DeepDNA), for compressing human mitochondrial genome data. The experimental results show the effectiveness of our proposed method compared with other on the human mitochondrial genome data. CONCLUSIONS The compression method we proposed can be classified as non-reference based method, but the compression effect is comparable to that of reference based methods. Moreover, our method not only have a well compression results in the population genome with large redundancy, but also in the single genome with small redundancy. The codes of DeepDNA are available at https://github.com/rongjiewang/DeepDNA .

中文翻译：

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使用机器学习技术压缩人类线粒体基因组。

背景技术近年来，随着高通量基因组测序技术的发展，产生了大量的基因组数据，引起了数据存储和传输成本的广泛关注。然而，如何有效地压缩基因组序列数据仍然是一个未解决的问题。结果在本文中，我们提出了一种使用机器学习技术（DeepDNA）的压缩方法，用于压缩人类线粒体基因组数据。实验结果表明，与其他人类线粒体基因组数据相比，我们提出的方法是有效的。结论我们提出的压缩方法可以归类为非基于参考的方法，但压缩效果与基于参考的方法相当。此外，我们的方法不仅在冗余较大的群体基因组中具有良好的压缩结果，而且在冗余较小的单个基因组中也具有良好的压缩结果。DeepDNA的代码可以在https://github.com/rongjiewang/DeepDNA获取。