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

文献类型： 外文期刊

第一作者： Li, Zhaohong

作者： Li, Zhaohong;Zhang, Yuanyuan;Qin, Shenghua;Chen, Yun;Chen, Choulin;Bao, Yongzhou;Xu, Lingna;Xie, Long;Deng, Biao;Zuo, Erwei;Liu, Yuwen;Li, Zhaohong;Zhang, Yuanyuan;Qin, Shenghua;Chen, Yun;Chen, Choulin;Bao, Yongzhou;Xu, Lingna;Deng, Biao;Liu, Yuwen;Peng, Bo;Ni, Jian-Quan;Peng, Bo;Ni, Jian-Quan;Zhang, Qian;Liu, Gang;Zhu, Yuqi;Hong, Yi;Sun, Tongjun;Liu, Binghua;Liu, Qian;Wang, Hongyan;Shao, Changwei;Chen, Xi;Wang, Jing;Wang, Guirong;Ma, Xinhao;Mei, Chugang;Yao, Yilong;Tang, Zhonglin;Liu, Yuwen;Li, Jiaying;Wang, Ningli;De, Baojun;Chen, Yuting;Cao, Junwei;Li, Tian;Zhu, Fangjie;Liu, Ranran;Ni, Jian-Quan

作者机构：

期刊名称：NUCLEIC ACIDS RESEARCH （ 影响因子：13.1； 五年影响因子：16.8 ）

ISSN： 0305-1048

年卷期： 2024 年 52 卷 21 期

页码：

收录情况： SCI

摘要： 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 sequence features implicating strong enhancer activity. Leveraging DREAM, we have engineered enhancers that surpass the potency of the strongest enhancer within the Drosophila genome by approximately 3.6-fold. Remarkably, these synthetic enhancers exhibited conserved functionality across species that have diverged more than billion years, indicating that DREAM was able to learn highly conserved enhancer regulatory grammar. Additionally, we designed silencers and cell line-specific enhancers using DREAM, demonstrating its versatility. Overall, our study not only introduces an interpretable approach for enhancer design but also lays out a general framework applicable to the design of other types of cis-regulatory elements. Graphical Abstract

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序