IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-21765-5.html
   My bibliography  Save this article

Deep learning-based enhancement of epigenomics data with AtacWorks

Author

Listed:
  • Avantika Lal

    (NVIDIA Corporation)

  • Zachary D. Chiang

    (Harvard University)

  • Nikolai Yakovenko

    (NVIDIA Corporation)

  • Fabiana M. Duarte

    (Harvard University)

  • Johnny Israeli

    (NVIDIA Corporation)

  • Jason D. Buenrostro

    (Harvard University)

Abstract

ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks, a deep learning toolkit to denoise sequencing coverage and identify regulatory peaks at base-pair resolution from low cell count, low-coverage, or low-quality ATAC-seq data. Models trained by AtacWorks can detect peaks from cell types not seen in the training data, and are generalizable across diverse sample preparations and experimental platforms. We demonstrate that AtacWorks enhances the sensitivity of single-cell experiments by producing results on par with those of conventional methods using ~10 times as many cells, and further show that this framework can be adapted to enable cross-modality inference of protein-DNA interactions. Finally, we establish that AtacWorks can enable new biological discoveries by identifying active regulatory regions associated with lineage priming in rare subpopulations of hematopoietic stem cells.

Suggested Citation

  • Avantika Lal & Zachary D. Chiang & Nikolai Yakovenko & Fabiana M. Duarte & Johnny Israeli & Jason D. Buenrostro, 2021. "Deep learning-based enhancement of epigenomics data with AtacWorks," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21765-5
    DOI: 10.1038/s41467-021-21765-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-21765-5
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-21765-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhiyuan Luo & Jiacheng Zhang & Jingyi Fei & Shengdong Ke, 2022. "Deep learning modeling m6A deposition reveals the importance of downstream cis-element sequences," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    2. Eloise Berson & Anjali Sreenivas & Thanaphong Phongpreecha & Amalia Perna & Fiorella C. Grandi & Lei Xue & Neal G. Ravindra & Neelufar Payrovnaziri & Samson Mataraso & Yeasul Kim & Camilo Espinosa & A, 2023. "Whole genome deconvolution unveils Alzheimer’s resilient epigenetic signature," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Zhijian Li & Christoph Kuppe & Susanne Ziegler & Mingbo Cheng & Nazanin Kabgani & Sylvia Menzel & Martin Zenke & Rafael Kramann & Ivan G. Costa, 2021. "Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    4. Samir Rachid Zaim & Mark-Phillip Pebworth & Imran McGrath & Lauren Okada & Morgan Weiss & Julian Reading & Julie L. Czartoski & Troy R. Torgerson & M. Juliana McElrath & Thomas F. Bumol & Peter J. Ske, 2024. "MOCHA’s advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts," Nature Communications, Nature, vol. 15(1), pages 1-24, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21765-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.