TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data

Guorui Zhang, Chao Song, Mingxue Yin, Liyuan Liu, Yuexin Zhang, Ye Li, Jianing Zhang, Maozu Guo () and Chunquan Li ()
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Guorui Zhang: University of South China
Chao Song: University of South China
Mingxue Yin: University of South China
Liyuan Liu: University of South China
Yuexin Zhang: University of South China
Ye Li: University of South China
Jianing Zhang: University of South China
Maozu Guo: Beijing University of Civil Engineering and Architecture
Chunquan Li: University of South China

Nature Communications, 2025, vol. 16, issue 1, 1-20

Abstract: Abstract It is challenging to identify regulatory transcriptional regulators (TRs), which control gene expression via regulatory elements and epigenomic signals, in context-specific studies on the onset and progression of diseases. The use of large-scale multi-omics epigenomic data enables the representation of the complex epigenomic patterns of control of the regulatory elements and the regulators. Herein, we propose Transcription Regulator Activity Prediction Tool (TRAPT), a multi-modality deep learning framework, which infers regulator activity by learning and integrating the regulatory potentials of target gene cis-regulatory elements and genome-wide binding sites. The results of experiments on 570 TR-related datasets show that TRAPT outperformed state-of-the-art methods in predicting the TRs, especially in terms of forecasting transcription co-factors and chromatin regulators. Moreover, we successfully identify key TRs associated with diseases, genetic variations, cell-fate decisions, and tissues. Our method provides an innovative perspective on identifying TRs by using epigenomic data.

Date: 2025
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DOI: 10.1038/s41467-025-58921-0

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