Ask about this productRelated genes to: CTCF antibody
- Gene:
- CTCF NIH gene
- Name:
- CCCTC-binding factor
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 16q22.1
- Locus Type:
- gene with protein product
- Date approved:
- 2000-10-20
- Date modifiied:
- 2016-02-12
Related products to: CTCF antibody
Related articles to: CTCF antibody
- Chromatin organization underlies essential genome functions, but its nanoscale organization remains challenging to capture and quantify with precision. Atomic force microscopy (AFM) offers direct structural readouts of DNA and chromatin, yet translating these rich images into reproducible biological metrics has been limited by the lack of standardized, scalable analysis tools. Here we present DNAsight, an automated analysis framework that integrates machine learning-based segmentation with modular, base-pair-calibrated quantification of DNA spatial organization, looping, nucleosome spacing, and protein clustering. Applied across diverse chromatin-associated proteins, DNAsight reveals protein-specific organizational signatures, including topology-dependent compaction by integration host factor, condition-dependent changes in loop-like DNA structures in cohesin-CTCF-precocious dissociation of sisters 5A reactions, and promoter-driven multimerization of GAGA factor clusters. The framework further enables direct extraction of nucleosome spacing distributions from raw AFM images, providing a label-free route to investigate chromatin fiber architecture. Together, these advances establish DNAsight as a generalizable and scalable approach for converting AFM measurements into quantitative insights into the physical principles of chromatin organization. - Source: PubMed
Sørensen Emily WintherPangeni SushilMerino Urteaga RaquelMurray Peter JRudnizky SergeiLiao Ting-WeiRashid FahadHwang JiheeYamadi MaryamFeng Xinyu AZähringer JonasGu StephanieDavidson Iain FCaccianini LauraOsorio-Valeriano ManuelFarnung LucasVos Seychelle MPeters Jan-MichaelBerger JamesWu CarlHatzakis Nikos SKirkegaard Julius BHa Taekjip - Chromatin loop calling from chromatin interaction data often exhibits substantial variability across related samples. We present UnionLoops, a computational workflow for chromatin loop calling across multiple related samples. UnionLoops integrates information across datasets to determine positions and dataset-specificity of looping interactions. It constructs a unified candidate loop set, applies consistent filtering and aggregation, and evaluates loop support across samples. We demonstrate that UnionLoops increases sensitivity for detecting shared chromatin loops, reduces spurious sample-specific calls, and improves concordance with independent genomic features, including CTCF and cohesin occupancy. UnionLoops enables improved biological interpretation of chromatin loop organization and dynamics across related conditions. - Source: PubMed
Publication date: 2026/06/20
Liu JiangyuanGibcus Johan HDekker Job - The pig (Sus scrofa) is both an economically important livestock species and a valuable biomedical model . Its genome bears regulatory features shaped by domestication and selection that are often poorly captured by genomic language models (gLMs) trained on human or model organism data. To address these challenges, we developed Porcine MutBERT, a suite of lightweight gLMs with 86 million parameters that employs a probabilistic masking strategy targeting evolutionarily informative single-nucleotide polymorphisms. This design captures population-specific variation while reducing computational cost. We further propose PorcineBench, a benchmark that evaluates gLM performance across porcine functional genomics tasks, including chromatin accessibility (ATAC-seq), CTCF binding, and histone modifications (H3K27ac, H3K4me1, and H3K27me3). Results show that Porcine MutBERT family achieves highly competitive performance on PorcineBench relative to substantially larger models, while providing an explicitly porcine-adapted alternative for downstream functional genomics in pigs. These findings underscore the advantages of species-adapted, efficient architectures in agricultural genomics and demonstrate that compact gLMs can expand accessibility and impact in resource-constrained settings. The code and data are available at https://github.com/ai4nucleome/pigmutbert. - Source: PubMed
Long WeicaiZhou RongWei WenkangZhang XiaoaiWu ShanshanLi KuiZhang YanlinWang Zishuai - Nonunion of fractures is a major challenge in orthopedics and traumatology, especially with increasing high-energy injuries. Adipose-derived mesenchymal stem cells (ASCs) are a readily accessible source with strong osteogenic potential. This study compared the bone regenerative efficacy of undifferentiated ASCs versus their osteogenically pre-differentiated derivatives in a critical-size femoral nonunion model. - Source: PubMed
Publication date: 2026/06/18
Maslennikov SerhiiIsachenko MariiaDanukalo MaksymHancheva OlgaGolovakha MaksymKolesnyk Yurii - Protein-mediated chromatin interactions are fundamental to gene regulation. However, experimental approaches such as Chromatin Interaction Analysis by Paired-End Tag sequencing are limited by data scarcity and high cost, while existing computational models are constrained by limited resolution and challenges in effectively integrating heterogeneous genomic data. To address these issues, we propose GraphChIAr, a regression-based deep learning framework that estimates chromatin interaction strength by augmenting Hi-C contact maps with various ChIP-seq profiles and genomic sequence information. A key advantage of GraphChIAr is its super-resolution capability, enabling accurate estimations of chromatin interactions from conventional resolutions down to ultra-high near-nucleosome resolution (e.g. 200 bp). By introducing genomic shift distance in GraphChIAr, we enabled it to predict remote interactions between distant genomic loci at a genome-wide scale. Cross-referencing results demonstrate high predictive accuracy for key mediating proteins such as CTCF, highlighting the benefits of integrating complementary genomic features. Together, GraphChIAr provides an effective computational tool to augment experimental data and advance the study of 3D genome organization. The source code of GraphChIAr is available at (https://github.com/don194/GraphChIAr). - Source: PubMed
Dong HaoRao Guo-ZhengWang Hao-YuWu Xin-RanXian TongWang Bo-QiangDu Pu-Feng