The DOI matches its Crossref record; 86 of the 89 references it cites were found in a registry or an index.
Supervised enhancer prediction with epigenetic pattern recognition and targeted validation
Open accessPublished version of a preprint
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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Additional file 8 of Identification and prediction of developmental enhancers in sea urchin embryos
Arenas‐Mena, C., Miljovska, S., Rice, E. J., Gurges, J., Shashikant, T., Wang, Z., Ercan, S., & Danko, C. G. (2021) · Figshare · doi:10.6084/m9.figshare.16835074
Additional file 3 of Cis-regulatory atlas of primary human CD4+ T cells
Stefan, K., & Barski, A. (2024) · Figshare · doi:10.6084/m9.figshare.26588766
Additional file 2 of Cis-regulatory atlas of primary human CD4+ T cells
Stefan, K., & Barski, A. (2024) · Figshare · doi:10.6084/m9.figshare.26588763
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Additional file 4 of Identification and prediction of developmental enhancers in sea urchin embryos
Arenas‐Mena, C., Miljovska, S., Rice, E. J., Gurges, J., Shashikant, T., Wang, Z., Ercan, S., & Danko, C. G. (2021) · Figshare · doi:10.6084/m9.figshare.16835062
Additional file 1 of Cis-regulatory atlas of primary human CD4+ T cells
Stefan, K., & Barski, A. (2024) · Figshare · doi:10.6084/m9.figshare.26588760
Additional file 7 of Identification and prediction of developmental enhancers in sea urchin embryos
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mwHIT: accelerated and accurate histone modification imputation using multi-scale window attention
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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
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