DeCTCF: Decoding CTCF binding sequences by leveraging predicted epigenomic features
- 1School of Physical Science and Technology, Inner Mongolia University, Hohhot, China.
- 2Inner Mongolia Key Laboratory of Biophysics and Bioinformatics, Inner Mongolia University, Hohhot, China.
- 3CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, P. R. China.
Abstract
CTCF is a key architectural protein with diverse roles in genome organization and gene regulation, yet how it achieves these roles in different contexts remains unclear. Pretrained sequence-based models such as Sei provide predicted epigenomic features that can be used in downstream analyses of regulatory elements. Here, we developed DeCTCF, an integrative computational framework that uses pretrained Sei predictions to analyze 236,552 CTCF binding sequences by integrating CTCF ChIP-seq data from 118 human cell lines. By leveraging predicted epigenomic features from the Sei model, we grouped these CTCF binding sites into 20 clusters. These clusters can be annotated into distinct functional modules, including a major module associated with 3D chromatin architecture and three lineage-associated modules. The lineage-associated modules reveal associations between candidate co-factors and CTCF’s context-dependent functions. For example, several clusters enriched in the three stem cell lines included in our dataset also showed enrichment of ZIC-family and were associated with gene sets related to pluripotency and neurodevelopment. We further observed associations between cluster-level CTCF ChIP-seq signal profiles and chromatin-loop annotations: single-peak profiles were reproducibly associated with higher loop interaction scores, whereas double- and triple-peak profiles showed distinct loop-pairing preferences. Overall, our study offers a systematic map of CTCF’s modular organization by leveraging predicted epigenomic features and reveals context-associated regulatory patterns that underlie its regulatory diversity. Author summary: In human cells, transcription factors regulate gene expression through diverse mechanisms. Given that CTCF is a ubiquitous factor with diverse roles in chromatin organization and gene regulation, it remains unclear how it achieves functional diversity despite binding to a conserved DNA motif. To address this, we developed a computational framework, DeCTCF. It leverages epigenomic features predicted by the pretrained Sei model to analyse over 230,000 CTCF binding sequences across 118 human cell lines and systematically classifies these sites into 20 clusters. These clusters were further grouped into four functional modules, including three cell-type-associated regulatory modules and one chromatin organization-associated module. We find that the context-associated regulatory functions of CTCF are associated with lineage-specific transcription factors and distinct epigenomic environments. In contrast, its role in chromatin organization is associated with CTCF ChIP-seq signal profiles and interactions with co-factors, which together may contribute to the patterns of chromatin looping. Together, our results provide a generalizable framework for decoding the functional diversity of regulatory elements from sequence and epigenomic features.
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BibTeX
@article{chai2026dectcf,
title = {{DeCTCF: Decoding CTCF binding sequences by leveraging predicted epigenomic features}},
author = {Lu Chai and Jie Gao and Tinghe Guo and Teer Ba and Zihan Li and Junjie Liu and Yong Wang and Lirong Zhang},
journal = {PLOS Computational Biology},
year = {2026},
month = oct,
doi = {10.1371/journal.pcbi.1014848},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014848},
note = {CC BY},
}
RIS
TY - JOUR
TI - DeCTCF: Decoding CTCF binding sequences by leveraging predicted epigenomic features
AU - Lu Chai
AU - Jie Gao
AU - Tinghe Guo
AU - Teer Ba
AU - Zihan Li
AU - Junjie Liu
AU - Yong Wang
AU - Lirong Zhang
PY - 2026
DA - 2026/10/05
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014848
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014848
AB - CTCF is a key architectural protein with diverse roles in genome organization and gene regulation, yet how it achieves these roles in different contexts remains unclear. Pretrained sequence-based models such as Sei provide predicted epigenomic features that can be used in downstream analyses of regulatory elements. Here, we developed DeCTCF, an integrative computational framework that uses pretrained Sei predictions to analyze 236,552 CTCF binding sequences by integrating CTCF ChIP-seq data from 118 human cell lines. By leveraging predicted epigenomic features from the Sei model, we grouped these CTCF binding sites into 20 clusters. These clusters can be annotated into distinct functional modules, including a major module associated with 3D chromatin architecture and three lineage-associated modules. The lineage-associated modules reveal associations between candidate co-factors and CTCF’s context-dependent functions. For example, several clusters enriched in the three stem cell lines included in our dataset also showed enrichment of ZIC-family and were associated with gene sets related to pluripotency and neurodevelopment. We further observed associations between cluster-level CTCF ChIP-seq signal profiles and chromatin-loop annotations: single-peak profiles were reproducibly associated with higher loop interaction scores, whereas double- and triple-peak profiles showed distinct loop-pairing preferences. Overall, our study offers a systematic map of CTCF’s modular organization by leveraging predicted epigenomic features and reveals context-associated regulatory patterns that underlie its regulatory diversity. Author summary: In human cells, transcription factors regulate gene expression through diverse mechanisms. Given that CTCF is a ubiquitous factor with diverse roles in chromatin organization and gene regulation, it remains unclear how it achieves functional diversity despite binding to a conserved DNA motif. To address this, we developed a computational framework, DeCTCF. It leverages epigenomic features predicted by the pretrained Sei model to analyse over 230,000 CTCF binding sequences across 118 human cell lines and systematically classifies these sites into 20 clusters. These clusters were further grouped into four functional modules, including three cell-type-associated regulatory modules and one chromatin organization-associated module. We find that the context-associated regulatory functions of CTCF are associated with lineage-specific transcription factors and distinct epigenomic environments. In contrast, its role in chromatin organization is associated with CTCF ChIP-seq signal profiles and interactions with co-factors, which together may contribute to the patterns of chromatin looping. Together, our results provide a generalizable framework for decoding the functional diversity of regulatory elements from sequence and epigenomic features.
SN - 1553-7358
SN - 1553-734X
ER -