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DNACoder: a CNN-LSTM attention-based network for genomic sequence data compression

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Abstract

Genomic sequencing has become increasingly prevalent, generating massive amounts of data and facing a significant challenge in long-term storage and transmission. A solution that reduces the storage and transfer requirements without compromising data integrity is needed. The effectiveness of neural networks has already been endorsed in tasks like image and speech compression. Adapting them to recognize the intricate patterns in genomic sequences could help to find more redundancies and reduce storage requirements. The proposed method, called DNACoder, leverages deep learning techniques to achieve significant compression ratios while preserving the essential information in genomic data and offers a high-performance compression for genomic sequences in any data format. The results of the experiments clearly demonstrate the effectiveness of the method and its potential applications in genomic data storage. Our proposed method improves compression by 21.1% on bits per base compared to existing compressors on the benchmarked dataset. By using a deep learning prediction model that is structured as a convolutional layer followed by an attention-based long short-term memory network, we propose a novel lossless and reference-free compression approach (DNACoder), which can also be utilized as a reference-based compressor. The experimental outcome on the tested data illustrates that the advocated compression algorithm’s CNN-LSTM model makes generalizations effectively for genomic sequence data and outperforms the state-of-the-art methods.

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Data supporting this study is included within the article and/or supporting materials.

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Acknowledgements

The first author is thankful to the Cochin University of Science and Technology for the financial support through the University-SRF fellowship (File Ref.No.Ac.C3/2021 dated 27/02/2023).

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  1. Artificial Intelligence & Computer Vision Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi, Kerala, 682022, India

    K. S. Sheena & Madhu S. Nair

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  1. K. S. Sheena
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Correspondence to K. S. Sheena.

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Sheena, K.S., Nair, M.S. DNACoder: a CNN-LSTM attention-based network for genomic sequence data compression. Neural Comput & Applic 36, 18363–18376 (2024). https://doi.org/10.1007/s00521-024-10130-4

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