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Inflated performance measures in enhancer–promoter interaction-prediction methods

Nature Genetics volume 51, pages 1196–1198 (2019)Cite this article

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To the Editor — Assigning enhancers to target genes remains an important question in understanding gene regulation. The availability of genome-organization data from experiments such as high-throughput chromosome-conformation capture (Hi-C)1 and chromatin interaction analysis with paired-end tags (ChIA-PET)2 remains limited. Development of computational methods to predict enhancer–promoter interactions could thus extend the availability of such data to new cell types.

TargetFinder3 is a machine-learning method that predicts enhancer–promoter interactions on the basis of functional-genomic data, such as that from chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP–seq) of histone modifications or transcription factors, and DNase I hypersensitive site sequencing (DNase-seq). This method introduced the interesting idea that the functional-genomic signatures in the intervening regions (windows) between enhancer–promoter pairs are predictive of enhancer–promoter interactions. The success of TargetFinder in predicting enhancer–promoter interactions and recent advances in the prediction of transcription-factor-binding sites from DNA sequences4,5 led us to investigate the possibility of using sequence features in window regions to predict enhancer–promoter interactions.

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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.

Data availability

All data used in the manuscript are available at https://github.com/caofan/correspondence/.

Code availability

All code used in the manuscript are available at https://github.com/caofan/correspondence/.

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Acknowledgements

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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Authors and Affiliations

  1. Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore

    Fan Cao & Melissa J. Fullwood

  2. School of Biological Sciences, Nanyang Technological University, Singapore, Singapore

    Melissa J. Fullwood

  3. Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Melissa J. Fullwood

Authors
  1. Fan Cao
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  2. Melissa J. Fullwood
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Contributions

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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Cao, F., Fullwood, M.J. Inflated performance measures in enhancer–promoter interaction-prediction methods. Nat Genet 51, 1196–1198 (2019). https://doi.org/10.1038/s41588-019-0434-7

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