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Supervised enhancer prediction with epigenetic pattern recognition and targeted validation
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Abstract
Enhancers are important non-coding elements, but they have traditionally been hard to characterize experimentally. The development of massively parallel assays allows the characterization of large numbers of enhancers for the first time. Here, we developed a framework using Drosophila STARR-seq to create shape-matching filters based on meta-profiles of epigenetic features. We integrated these features with supervised machine-learning algorithms to predict enhancers. We further demonstrated that our model could be transferred to predict enhancers in mammals. We comprehensively validated the predictions using a combination of in vivo and in vitro approaches, involving transgenic assays in mice and transduction-based reporter assays in human cell lines (153 enhancers in total). The results confirmed that our model can accurately predict enhancers in different species without re-parameterization. Finally, we examined the transcription factor binding patterns at predicted enhancers versus promoters. We demonstrated that these patterns enable the construction of a secondary model that effectively distinguishes enhancers and promoters.
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“While enhancer detection based on chromatin profiling has become standard, this approach may falsely detect regions without appreciable regulatory activity, as well as miss some functional elements [146, 162].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [162]
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Metadata
- Authors
- Sethi, A., Gu, M., Gumusgoz, E., Chan, L., Yan, K.-K., Rozowsky, J., Barozzi, I., Afzal, V., Akiyama, J. A., Plajzer-Frick, I., Yan, C., Novak, C. S., Kato, M., Garvin, T. H., Pham, Q., Harrington, A., Mannion, B. J., Lee, E. A., Fukuda-Yuzawa, Y., Visel, A., Dickel, D. E., Yip, K. Y., Sutton, R., Pennacchio, L. A., & Gerstein, M.
- Affiliations
- Yale University (US)
- Chinese University of Hong Kong (HK)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Gerstein, Mark
- ORCID
- Sethi, Anurag
- Gu, Mengting
- Gumusgoz, Emrah
- Chan, Landon
- Yan, Koon-Kiu
- Rozowsky, Joel
- Barozzi, Iros
- Afzal, Veena
- Akiyama, Jennifer A.
- Yan, Chengfei
- Kato, Momoe
- Garvin, Tyler H.
- Harrington, Anne
- Mannion, Brandon J.
- Lee, Elizabeth A.
- Visel, Axel
- Dickel, Diane E.
- Yip, Kevin Y.
- Sutton, Richard
- Pennacchio, Len A.
- Gerstein, Mark
- Venue
- Nature Methods
- Published
- 2020-07-29
- Volume
- 17
- Issue
- 8
- Pages
- 807-814
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC8073243
- OpenAlex
- W3046699218
- Cited by
- 109
- References
- 89
- Citations by year
- 2026: 14 · 2025: 11 · 2024: 23 · 2023: 18 · 2022: 10 · 2021: 24 · 2020: 6
- FWCI
- 4.65
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (HG009446-01)
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (R24HL123879)
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (R01HG003988, U54HG006997, UM1HG009421)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- enhancer prediction, STARR-seq, epigenetic features, supervised learning, interspecies transmission, transcription factor binding, promoter identification, Drosophila, non-coding elements
- MeSH terms
- Animals, Cell Line, Drosophila, Histones, Humans, Mice, Transgenic, Pattern Recognition, Automated, Reproducibility of Results, Epigenesis, Genetic, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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