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Inflated performance measures in enhancer–promoter interaction-prediction methods

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Fig. 1: The performance of using occurrences of real and shuffled motifs in window regions to predict enhancer–promoter interactions by using TargetFinder datasets.
Fig. 2: Analyses of TargetFinder and JEME.

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All data used in the manuscript are available at

Code availability

All code used in the manuscript are available at


  1. 1.

    Lieberman-Aiden, E. et al. Science 326, 289–293 (2009).

    CAS  Article  Google Scholar 

  2. 2.

    Fullwood, M. J. & Ruan, Y. J. Cell. Biochem. 107, 30–39 (2009).

    CAS  Article  Google Scholar 

  3. 3.

    Whalen, S., Truty, R. M. & Pollard, K. S. Nat. Genet. 48, 488–496 (2016).

    CAS  Article  Google Scholar 

  4. 4.

    Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Nat. Biotechnol. 33, 831–838 (2015).

    CAS  Article  Google Scholar 

  5. 5.

    Quang, D. & Xie, X. Nucleic Acids Res. 44, e107 (2016).

    Article  Google Scholar 

  6. 6.

    Wang, J. et al. Genome Res. 22, 1798–1812 (2012).

    CAS  Article  Google Scholar 

  7. 7.

    Mathelier, A. et al. Nucleic Acids Res. 42, D142–D147 (2014).

    CAS  Article  Google Scholar 

  8. 8.

    Yang, Y., Zhang, R., Singh, S. & Ma, J. Bioinformatics 33, i252–i260 (2017).

    CAS  Article  Google Scholar 

  9. 9.

    Singh, S., Yang, Y., Poczos, B. & Ma, J. Quant. Biol. (2019).

    CAS  Article  Google Scholar 

  10. 10.

    Cao, Q. et al. Nat. Genet. 49, 1428–1436 (2017).

    CAS  Article  Google Scholar 

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We would like to thank Q. Cao and Y.-L. Yip for providing us with JEME and TargetFinder’s training data on JEME’s random targets datasets and JEME’s training data on RIPPLE 5C datasets. We would like to thank all members of the laboratory of M.J.F. for helpful comments. This research was supported by the National Research Foundation (NRF) Singapore through an NRF Fellowship awarded to M.J.F. (NRF-NRFF2012-054) and Yale-NUS start-up funds awarded to M.J.F. This research was supported by the RNA Biology Center at the Cancer Science Institute of Singapore, NUS, as part of funding under the Singapore Ministry of Education Academic Research Fund Tier 3 awarded to D. G. Tenen (MOE2014-T3-1-006). This research is supported by the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative.

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F.C. and M.J.F. conceived the research. F.C. performed the analyses. F.C. and M.J.F. reviewed the data and wrote the manuscript. All authors reviewed and approved of the manuscript.

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Correspondence to Melissa J. Fullwood.

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The authors declare no competing interests.

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Cao, F., Fullwood, M.J. Inflated performance measures in enhancer–promoter interaction-prediction methods. Nat Genet 51, 1196–1198 (2019).

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