1. Supervised enhancer prediction with epigenetic pattern recognition and targeted validation
- Author
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Anne N. Harrington, Emrah Gumusgoz, Kevin Y. Yip, Anurag Sethi, Axel Visel, Elizabeth Lee, Catherine S. Novak, Mengting Gu, Richard E. Sutton, Ingrid Plajzer-Frick, Iros Barozzi, Momoe Kato, Tyler H. Garvin, Len A. Pennacchio, Quan Pham, Koon-Kiu Yan, Brandon J. Mannion, Yoko Fukuda-Yuzawa, Landon L Chan, Veena Afzal, Diane E. Dickel, Mark Gerstein, Jennifer A. Akiyama, Chengfei Yan, and Joel Rozowsky
- Subjects
Automated ,Technology ,Transgene ,1.1 Normal biological development and functioning ,Mice, Transgenic ,Computational biology ,Biology ,Pattern Recognition ,Biochemistry ,Medical and Health Sciences ,Article ,Transgenic ,Epigenesis, Genetic ,Pattern Recognition, Automated ,Cell Line ,Histones ,03 medical and health sciences ,Mice ,Genetic ,Underpinning research ,Genetics ,Animals ,Humans ,Epigenetics ,Enhancer ,Molecular Biology ,Transcription factor ,030304 developmental biology ,0303 health sciences ,Human Genome ,Reproducibility of Results ,Promoter ,Cell Biology ,Human cell ,Biological Sciences ,Pattern recognition (psychology) ,Drosophila ,Generic health relevance ,Biotechnology ,Epigenesis ,Developmental Biology - 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.
- Published
- 2020