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CoGI: Towards Compressing Genomes as an Image.

Xiaojing Xie, Shuigeng Zhou, Jihong Guan

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |December 17, 2015
    PubMed
    Summary

    Compressing Genomes as an Image (CoGI) transforms genomic data into a 2D image for efficient compression. This novel approach significantly improves compression ratios and efficiency compared to existing genome compression tools.

    Area of Science:

    • Genomics
    • Bioinformatics
    • Data Compression

    Background:

    • Genomic sequencing generates massive datasets, posing storage and transfer challenges.
    • Existing genome compression methods often treat sequences as 1D strings, limiting efficiency.

    Purpose of the Study:

    • To introduce a novel genome compression approach, Compressing Genomes as an Image (CoGI).
    • To evaluate CoGI's performance against state-of-the-art compression techniques.

    Main Methods:

    • Transforming genomic sequences into two-dimensional binary images.
    • Applying rectangular partition coding for image compression.
    • Developing entropy-based algorithms for reference genome selection in reference-based compression.

    Main Results:

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    • Reference-based CoGI outperforms GReEn and RLZ-opt in compression ratio and efficiency.
    • Reference-free CoGI achieves comparable compression ratio but superior efficiency to XM.
    • CoGI surpasses Gzip in both compression speed and ratio.

    Conclusions:

    • CoGI offers an effective and practical solution for genome data compression.
    • The image-based approach provides significant advantages over traditional string-based methods.
    • CoGI demonstrates high performance for both reference-based and reference-free compression scenarios.