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    Area of Science:

    • Genomics
    • Bioinformatics
    • Artificial Intelligence

    Background:

    • Genomics research generates vast datasets via next-generation sequencing (NGS).
    • Existing algorithms struggle with the scale and speed requirements of big data in genomics.
    • Deep learning shows promise for enhancing the efficiency of genomics data processing.

    Purpose of the Study:

    • To review the emerging trend of applying deep learning to next-generation sequencing (NGS) data analysis.
    • To analyze internet community interest in NGS and deep learning.
    • To provide a taxonomic overview of deep learning software solutions for specific NGS applications.

    Main Methods:

    • Analysis of internet search trends related to NGS and deep learning.
    • Taxonomic classification of existing deep learning-based software for NGS.
    • Review of current literature and software solutions.

    Main Results:

    • Significant internet community interest exists for both NGS and deep learning.
    • A range of deep learning algorithms are being applied across various NGS application fields.
    • Key software solutions utilizing deep learning for NGS tasks have been identified and categorized.

    Conclusions:

    • Deep learning offers a viable approach to address the computational challenges in big data genomics.
    • The identified software solutions represent the current state-of-the-art in deep learning for NGS.
    • Future advancements will likely involve cloud computing integration for scalable deep learning-based NGS solutions.