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Human mitochondrial genome compression using machine learning techniques

Authors
  • Wang, Rongjie1
  • Zang, Tianyi2
  • Wang, Yadong2
  • 1 Peng Cheng Laboratory, ShenZhen, China , ShenZhen (China)
  • 2 School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China , Harbin (China)
Type
Published Article
Journal
Human Genomics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Oct 22, 2019
Volume
13
Issue
Suppl 1
Identifiers
DOI: 10.1186/s40246-019-0225-3
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundIn 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.ResultsIn 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.ConclusionsThe 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.

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