Human mitochondrial genome compression using machine learning techniques.

ABSTRACT: 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 on

SUBMITTER: Wang R 

PROVIDER: S-EPMC6805717 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature