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A tiling-deletion-based genetic screen for cis-regulatory element identification in mammalian cells

Nature Methods volume 14, pages 629635 (2017) | Download Citation

Abstract

Millions of cis-regulatory elements are predicted to be present in the human genome, but direct evidence for their biological function is scarce. Here we report a high-throughput method, cis-regulatory element scan by tiling-deletion and sequencing (CREST-seq), for the unbiased discovery and functional assessment of cis-regulatory sequences in the genome. We used it to interrogate the 2-Mb POU5F1 locus in human embryonic stem cells, and identified 45 cis-regulatory elements. A majority of these elements have active chromatin marks, DNase hypersensitivity, and occupancy by multiple transcription factors, which confirms the utility of chromatin signatures in cis-element mapping. Notably, 17 of them are previously annotated promoters of functionally unrelated genes, and like typical enhancers, they form extensive spatial contacts with the POU5F1 promoter. These results point to the commonality of enhancer-like promoters in the human genome.

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Acknowledgements

We thank D. Gorkin and J. Yan for feedback on previous versions of the manuscript. We thank Z. Ye and S. Kuan for technical assistance. This work was supported by the US National Institutes of Health (NIH) (grants U54 HG006997, U01 DK105541, R01HG008135, 1UM1HG009402 and 2P50 GM085764 to B.R.), the Ludwig Institute for Cancer Research (to B.R.) and the Human Frontier Science Program (HFSP) (Long Term Postdoctoral Fellowship to Y.D.).

Author information

Author notes

    • Yarui Diao
    • , Rongxin Fang
    •  & Bin Li

    These authors contributed equally to this work.

Affiliations

  1. Ludwig Institute for Cancer Research, La Jolla, California, USA.

    • Yarui Diao
    • , Rongxin Fang
    • , Bin Li
    • , Juntao Yu
    • , Yunjiang Qiu
    • , Hui Huang
    • , Tristin Liu
    •  & Bing Ren
  2. Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, USA.

    • Rongxin Fang
    •  & Yunjiang Qiu
  3. Department of Pharmacology, University of California, San Diego, La Jolla, California, USA.

    • Zhipeng Meng
    • , Kimberly C Lin
    •  & Kun-Liang Guan
  4. Moores Cancer Center, University of California, San Diego, La Jolla, California, USA.

    • Zhipeng Meng
    • , Kimberly C Lin
    • , Kun-Liang Guan
    •  & Bing Ren
  5. School of Life Sciences, University of Science and Technology of China, Hefei, China.

    • Juntao Yu
  6. Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, California, USA.

    • Hui Huang
    •  & Ryan J Marina
  7. Biological Science, KAIST, Daejeon, South Korea.

    • Inkyung Jung
  8. Institute for Human Genetics and Department of Neurology, University of California, San Francisco, San Francisco, California, USA.

    • Yin Shen
  9. Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, University of California, San Diego, La Jolla, California, USA.

    • Bing Ren

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Contributions

Y.D. and B.R. conceived the idea for CREST-seq; R.F., Y.D. and B.L. conducted integrative data analysis with help from Y.Q., H.H. and I.J.; B.L. and Y.D. designed paired sgRNA libraries; Y.D., Z.M., J.Y., K.C.L., T.L., H.H., R.J.M. and Y.S. performed the experiment; Z.M., K.C.L. and K.-L.G. packaged the lentiviral library; and Y.D., R.F., B.L. and B.R. wrote the paper.

Competing interests

B.R. is a cofounder of Arima Genomics, Inc.

Corresponding author

Correspondence to Bing Ren.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–15 and Supplementary Notes 1–7

  2. 2.

    Supplementary Protocol

    Supplementary Protocol

Excel files

  1. 1.

    Supplementary Table 1

    Oligo sequence of CREST-seq design

  2. 2.

    Supplementary Table 2

    CREST-seq oligo read count

  3. 3.

    Supplementary Table 3

    Statistical enrichment of each sgRNA pair in the cis samples compared with the control samples

  4. 4.

    Supplementary Table 4

    Statistical significance of rank bias for each 50-bp genomic bin

  5. 5.

    Supplementary Table 5

    Genomic coordinates of 45 predicted CREST-seq peaks

  6. 6.

    Supplementary Table 6

    Quantification and statistics of FACS eGFP levels in the mutant clones

  7. 7.

    Supplementary Table 7

    A full list of genomic features used in machine learning and PCA analysis

  8. 8.

    Supplementary Table 8

    List of DNA and CRISPR RNA oligo sequences used in this study

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DOI

https://doi.org/10.1038/nmeth.4264

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