CREATE: cell-type-specific cis-regulatory element identification via discrete embedding

Xuejian Cui, Qijin Yin, Zijing Gao, Zhen Li, Xiaoyang Chen, Hairong Lv, Shengquan Chen, Qiao Liu, Wanwen Zeng () and Rui Jiang ()
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Xuejian Cui: Tsinghua University
Qijin Yin: Tsinghua University
Zijing Gao: Tsinghua University
Zhen Li: Tsinghua University
Xiaoyang Chen: Tsinghua University
Hairong Lv: Tsinghua University
Shengquan Chen: Nankai University
Qiao Liu: Stanford University
Wanwen Zeng: Stanford University
Rui Jiang: Tsinghua University

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Cis-regulatory elements (CREs), including enhancers, silencers, promoters and insulators, play pivotal roles in orchestrating gene regulatory mechanisms that drive complex biological traits. However, current approaches for CRE identification are predominantly sequence-based and typically focus on individual CRE types, limiting insights into their cell-type-specific functions and regulatory dynamics. Here, we present CREATE, a multimodal deep learning framework based on Vector Quantized Variational AutoEncoder, tailored for comprehensive CRE identification and characterization. CREATE integrates genomic sequences, chromatin accessibility, and chromatin interaction data to generate discrete CRE embeddings, enabling accurate multi-class classification and robust characterization of CREs. CREATE excels in identifying cell-type-specific CREs, and provides quantitative and interpretable insights into CRE-specific features, uncovering the underlying regulatory codes. By facilitating large-scale prediction of CREs in specific cell types, CREATE enhances the recognition of disease- or phenotype-associated biological variabilities of CREs, thus advancing our understanding of gene regulatory landscapes and their roles in health and disease.

Date: 2025
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DOI: 10.1038/s41467-025-59780-5

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