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MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell

Author

Listed:
  • Ruohan Wang

    (McGill University
    Research Institute of the McGill University Health Centre
    McGill University)

  • Yumin Zheng

    (Research Institute of the McGill University Health Centre
    McGill University
    McGill University)

  • Zijian Zhang

    (Baylor College of Medicine)

  • Kailu Song

    (Research Institute of the McGill University Health Centre
    McGill University
    McGill University)

  • Erxi Wu

    (Baylor College of Medicine
    Texas A&M University
    The University of Texas at Austin
    Baylor Scott & White Health)

  • Xiaopeng Zhu

    (MyCellome LLC.)

  • Tao P. Wu

    (Baylor College of Medicine)

  • Jun Ding

    (McGill University
    Research Institute of the McGill University Health Centre
    McGill University
    McGill University)

Abstract

Transposable elements (TEs) are crucial for genetic diversity and gene regulation. Current single-cell quantification methods often align multi-mapping reads to either ‘best-mapped’ or ‘random-mapped’ locations and categorize them at the subfamily levels, overlooking the biological necessity for accurate, locus-specific TE quantification. Moreover, these existing methods are primarily designed for and focused on transcriptomics data, which restricts their adaptability to single-cell data of other modalities. To address these challenges, here we introduce MATES, a deep-learning approach that accurately allocates multi-mapping reads to specific loci of TEs, utilizing context from adjacent read alignments flanking the TE locus. When applied to diverse single-cell omics datasets, MATES shows improved performance over existing methods, enhancing the accuracy of TE quantification and aiding in the identification of marker TEs for identified cell populations. This development facilitates the exploration of single-cell heterogeneity and gene regulation through the lens of TEs, offering an effective transposon quantification tool for the single-cell genomics community.

Suggested Citation

  • Ruohan Wang & Yumin Zheng & Zijian Zhang & Kailu Song & Erxi Wu & Xiaopeng Zhu & Tao P. Wu & Jun Ding, 2024. "MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53114-7
    DOI: 10.1038/s41467-024-53114-7
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