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.

增强器是重要的非编码元素，但是传统上很难通过实验来表征。大规模平行测定的发展首次允许表征大量增强子。在这里，我们使用果蝇开发了一个框架STARR-seq可根据表观遗传特征的元特征创建形状匹配过滤器。我们将这些功能与受监督的机器学习算法集成在一起，以预测增强器。我们进一步证明了我们的模型可以转移到预测哺乳动物的增强子中。我们使用体内和体外方法的组合全面验证了该预测，包括小鼠中的转基因测定和人细胞系中基于转导的报告子测定（总共153个增强子）。结果证实我们的模型无需重新参数化即可准确预测不同物种中的增强子。最后，我们检查了预测的增强子与启动子的转录因子结合模式。