TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data
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 repre...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58921-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850145097877815296 |
|---|---|
| author | Guorui Zhang Chao Song Mingxue Yin Liyuan Liu Yuexin Zhang Ye Li Jianing Zhang Maozu Guo Chunquan Li |
| author_facet | Guorui Zhang Chao Song Mingxue Yin Liyuan Liu Yuexin Zhang Ye Li Jianing Zhang Maozu Guo Chunquan Li |
| author_sort | Guorui Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-329c6483725d4d868b44049deee435b2 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-329c6483725d4d868b44049deee435b22025-08-20T02:28:10ZengNature PortfolioNature Communications2041-17232025-04-0116112010.1038/s41467-025-58921-0TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic dataGuorui Zhang0Chao Song1Mingxue Yin2Liyuan Liu3Yuexin Zhang4Ye Li5Jianing Zhang6Maozu Guo7Chunquan Li8The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South ChinaThe First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South ChinaThe First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South ChinaThe First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South ChinaThe First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South ChinaThe First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South ChinaThe First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South ChinaSchool of Intelligence Science and Technology, Beijing University of Civil Engineering and ArchitectureThe First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South ChinaAbstract 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.https://doi.org/10.1038/s41467-025-58921-0 |
| spellingShingle | Guorui Zhang Chao Song Mingxue Yin Liyuan Liu Yuexin Zhang Ye Li Jianing Zhang Maozu Guo Chunquan Li TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data Nature Communications |
| title | TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data |
| title_full | TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data |
| title_fullStr | TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data |
| title_full_unstemmed | TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data |
| title_short | TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data |
| title_sort | trapt a multi stage fused deep learning framework for predicting transcriptional regulators based on large scale epigenomic data |
| url | https://doi.org/10.1038/s41467-025-58921-0 |
| work_keys_str_mv | AT guoruizhang traptamultistagefuseddeeplearningframeworkforpredictingtranscriptionalregulatorsbasedonlargescaleepigenomicdata AT chaosong traptamultistagefuseddeeplearningframeworkforpredictingtranscriptionalregulatorsbasedonlargescaleepigenomicdata AT mingxueyin traptamultistagefuseddeeplearningframeworkforpredictingtranscriptionalregulatorsbasedonlargescaleepigenomicdata AT liyuanliu traptamultistagefuseddeeplearningframeworkforpredictingtranscriptionalregulatorsbasedonlargescaleepigenomicdata AT yuexinzhang traptamultistagefuseddeeplearningframeworkforpredictingtranscriptionalregulatorsbasedonlargescaleepigenomicdata AT yeli traptamultistagefuseddeeplearningframeworkforpredictingtranscriptionalregulatorsbasedonlargescaleepigenomicdata AT jianingzhang traptamultistagefuseddeeplearningframeworkforpredictingtranscriptionalregulatorsbasedonlargescaleepigenomicdata AT maozuguo traptamultistagefuseddeeplearningframeworkforpredictingtranscriptionalregulatorsbasedonlargescaleepigenomicdata AT chunquanli traptamultistagefuseddeeplearningframeworkforpredictingtranscriptionalregulatorsbasedonlargescaleepigenomicdata |