{ "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.", "abstract_shown": true, "affiliations": [ "School of Physical Science and Technology, Inner Mongolia University, Hohhot, China.", "Inner Mongolia Key Laboratory of Biophysics and Bioinformatics, Inner Mongolia University, Hohhot, China.", "CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, P. R. China." ], "article_type": "Research Article", "arxiv": null, "author_total": 8, "authors": [ { "affs": [ 0 ], "name": "Lu Chai" }, { "affs": [ 0 ], "name": "Jie Gao" }, { "affs": [ 0 ], "name": "Tinghe Guo" }, { "affs": [ 0 ], "name": "Teer Ba" }, { "affs": [ 0 ], "name": "Zihan Li" }, { "affs": [ 0, 1 ], "name": "Junjie Liu" }, { "affs": [ 0, 2, 1 ], "name": "Yong Wang" }, { "affs": [ 0, 1 ], "name": "Lirong Zhang", "orcid": "0000-0001-5366-4837" } ], "bibtex": "https://research.draconitic.com/paper/cq26yef8nec40.bib", "cited_by": 0, "doi": "10.1371/journal.pcbi.1014848", "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014848", "id": "cq26yef8nec40", "issns": [ "1553-7358", "1553-734X" ], "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014848", "language": "eng", "licence": "CC-BY", "licence_label": "CC BY", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1312|Genomics and Chromatin Dynamics" ], [ "plos", "/Biology and life sciences/Cell biology/Cell physiology/Cell binding" ], [ "plos", "/Biology and life sciences/Cell biology/Cellular types/Animal cells/Stem cells/Stem cell lines" ], [ "plos", "/Biology and life sciences/Cell biology/Chromosome biology/Chromatin" ], [ "plos", "/Biology and life sciences/Computational biology/Epigenomics" ], [ "plos", "/Biology and life sciences/Computational biology/Genome analysis/Gene ontologies" ], [ "plos", "/Biology and life sciences/Computational biology/Genome analysis/Genome annotation" ], [ "plos", "/Biology and life sciences/Genetics/Epigenetics/Chromatin" ], [ "plos", "/Biology and life sciences/Genetics/Gene expression/Chromatin" ], [ "plos", "/Biology and life sciences/Genetics/Genomics" ], [ "plos", "/Biology and life sciences/Genetics/Genomics/Epigenomics" ], [ "plos", "/Biology and life sciences/Genetics/Genomics/Genome analysis/Gene ontologies" ], [ "plos", "/Biology and life sciences/Genetics/Genomics/Genome analysis/Genome annotation" ], [ "plos", "/Research and analysis methods/Biological cultures/Cell lines/Stem cell lines" ], [ "plos", "/Research and analysis methods/Database and informatics methods/Bioinformatics/Sequence analysis/Sequence motif analysis" ] ], "openalex": "W7172532262", "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014848&type=printable", "pmcid": "PMC13652320", "pmid": "42832579", "published": "2026-10-05", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/cq26yef8nec40.ris", "sources": [ { "first_seen": "2026-10-30T12:17:37Z", "native_id": "10.1371/journal.pcbi.1014848", "source": "plos", "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014848" }, { "first_seen": "2026-10-30T12:18:10Z", "native_id": "MED:42832579", "source": "europepmc", "url": "https://europepmc.org/article/MED/42832579" } ], "subjects": [ { "field": "biology", "origin": "plos:computational biology", "subfield": "bioinformatics" }, { "field": "biology", "origin": "plos:genetics", "subfield": "genetics-genomics" } ], "title": "DeCTCF: Decoding CTCF binding sequences by leveraging predicted epigenomic features", "title_text": "DeCTCF: Decoding CTCF binding sequences by leveraging predicted epigenomic features", "type": "peer-reviewed", "updated_at": "2026-10-30T12:18:10Z", "url": "https://research.draconitic.com/paper/cq26yef8nec40", "venue": "PLOS Computational Biology", "versions": [] }