A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.

ABSTRACT: 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 se

SUBMITTER: Li Z 

PROVIDER: S-EPMC11602155 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature