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ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

Author

Listed:
  • Vianne R. Gao

    (Memorial Sloan Kettering Cancer Center
    Tri-Institutional Program in Computational Biology and Medicine)

  • Rui Yang

    (Memorial Sloan Kettering Cancer Center
    Tri-Institutional Program in Computational Biology and Medicine)

  • Arnav Das

    (University of Washington)

  • Renhe Luo

    (Sloan Kettering Institute)

  • Hanzhi Luo

    (Memorial Sloan Kettering Cancer Center)

  • Dylan R. McNally

    (Cornell University)

  • Ioannis Karagiannidis

    (Weill Cornell Medical College)

  • Martin A. Rivas

    (Weill Cornell Medical College)

  • Zhong-Min Wang

    (Sloan Kettering Institute and Ludwig Center at Memorial Sloan Kettering Cancer Center)

  • Darko Barisic

    (Weill Cornell Medical College)

  • Alireza Karbalayghareh

    (Memorial Sloan Kettering Cancer Center)

  • Wilfred Wong

    (Memorial Sloan Kettering Cancer Center
    Tri-Institutional Program in Computational Biology and Medicine)

  • Yingqian A. Zhan

    (Memorial Sloan Kettering Cancer Center)

  • Christopher R. Chin

    (Weill Cornell Medical College)

  • William S. Noble

    (University of Washington)

  • Jeff A. Bilmes

    (University of Washington)

  • Effie Apostolou

    (Weill Cornell Medicine)

  • Michael G. Kharas

    (Memorial Sloan Kettering Cancer Center)

  • Wendy Béguelin

    (Weill Cornell Medical College)

  • Aaron D. Viny

    (Columbia University Irving Medical Center)

  • Danwei Huangfu

    (Sloan Kettering Institute)

  • Alexander Y. Rudensky

    (Sloan Kettering Institute and Ludwig Center at Memorial Sloan Kettering Cancer Center)

  • Ari M. Melnick

    (Weill Cornell Medical College)

  • Christina S. Leslie

    (Memorial Sloan Kettering Cancer Center)

Abstract

Identifying cell-type-specific 3D chromatin interactions between regulatory elements can help decipher gene regulation and interpret disease-associated non-coding variants. However, achieving this resolution with current 3D genomics technologies is often infeasible given limited input cell numbers. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps, including regulatory interactions, from single-cell ATAC sequencing (scATAC-seq) data alone. ChromaFold uses pseudobulk chromatin accessibility, co-accessibility across metacells, and a CTCF motif track as inputs and employs a lightweight architecture to train on standard GPUs. Trained on paired scATAC-seq and Hi-C data in human samples, ChromaFold accurately predicts the 3D contact map and peak-level interactions across diverse human and mouse test cell types. Compared to leading contact map prediction models that use ATAC-seq and CTCF ChIP-seq, ChromaFold achieves state-of-the-art performance using only scATAC-seq. Finally, fine-tuning ChromaFold on paired scATAC-seq and Hi-C in a complex tissue enables deconvolution of chromatin interactions across cell subpopulations.

Suggested Citation

  • Vianne R. Gao & Rui Yang & Arnav Das & Renhe Luo & Hanzhi Luo & Dylan R. McNally & Ioannis Karagiannidis & Martin A. Rivas & Zhong-Min Wang & Darko Barisic & Alireza Karbalayghareh & Wilfred Wong & Yi, 2024. "ChromaFold predicts the 3D contact map from single-cell chromatin accessibility," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53628-0
    DOI: 10.1038/s41467-024-53628-0
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    References listed on IDEAS

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