biostudies-literatureLi ZChina National Key R&D ProgramMinistry of Science and TechnologyNational Natural Science Foundation of ChinaChina National Key R&D Program13447-13468https://www.ebi.ac.uk/biostudies/studies/S-EPMC11602155biostudies-literatureUnknown52(21)Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design. Proficient in uncovering subtle and intricate patterns within extensive enhancer screening data, DREAM achieves cutting-edge sequence-based enhancer activity prediction and highlights critical seNucleic acids researchA novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.PMC116021552021YFF120050020221250020201813009883207059520201300797Zhu YCao JMa XLiu QLiu RWang GWang HWang JZhang QQin SShao CWang NLiu YYao YNi JQChen YChen XXu LTang ZLi JDe BBao YZhu FSun TXie LChen CDeng BZuo ELi ZHong YLi TZhang YPeng BMei CLiu BLiu GfalseA novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design. Proficient in uncovering subtle and intricate patterns within extensive enhancer screening data, DREAM achieves cutting-edge sequence-based enhancer activity prediction and highlights critical se2024-01-01T00:00:00Z2024 Nov2026-06-01T10:00:29.998Z2025-04-04T00:43:56.86ZS-EPMC116021553942060110.1093/nar/gkae912