ArticleHuman genomics2019
Human mitochondrial genome compression using machine learning techniques.
Article in Human genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Review
- Mitochondrial Proteins as Exosomal Cargo: New Breast Cancer Biomarkers & Crucial Players in Carcinogenesis?Molecules (Basel, Switzerland) · 2025Article
- Optimized model architectures for deep learning on genomic data.Communications biology · 2024Article
- Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity.Frontiers in genetics · 2023Article
- Efficient DNA sequence compression with neural networks.GigaScience · 2020Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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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