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CCCTC-Binding Factor

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Machine learning approaches infer vitamin D signaling: Critical impact of vitamin D receptor binding within topologically associated domains.

The Journal of steroid biochemistry and molecular biology 30044963
The vitamin D-modulated transcriptome of highly responsive human cells, such as THP-1 monocytes, comprises more than 500 genes, half of which are primary targets. Recently, we proposed a chromatin model of vitamin D signaling demonstrating that nearl...
Algorithms CCCTC-Binding Factor Cell Line Chromatin Gene Expression Regulation High-Throughput Nucleotide Sequencing Humans Machine Learning Protein Binding Receptors, Calcitriol RNA, Messenger Signal Transduction THP-1 Cells Vitamin D
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DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure.

Genome biology 32216817
Non-coding variants have been shown to be related to disease by alteration of 3D genome structures. We propose a deep learning method, DeepMILO, to predict the effects of variants on CTCF/cohesin-mediated insulator loops. Application of DeepMILO on v...
CCCTC-Binding Factor Cell Cycle Proteins Cell Line, Tumor Chromatin Chromosomal Proteins, Non-Histone Cohesins Deep Learning Humans Insulator Elements Mutation Neoplasms Whole Genome Sequencing
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Predicting 3D genome folding from DNA sequence with Akita.

Nature methods 33046897
In interphase, the human genome sequence folds in three dimensions into a rich variety of locus-specific contact patterns. Cohesin and CTCF (CCCTC-binding factor) are key regulators; perturbing the levels of either greatly disrupts genome-wide foldin...
CCCTC-Binding Factor Cell Cycle Proteins Chromosomal Proteins, Non-Histone Cohesins DNA-Binding Proteins Gene Expression Regulation Genome, Human Humans Models, Genetic Neural Networks, Computer Sequence Analysis, DNA
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DeepC: predicting 3D genome folding using megabase-scale transfer learning.

Nature methods 33046896
Predicting the impact of noncoding genetic variation requires interpreting it in the context of three-dimensional genome architecture. We have developed deepC, a transfer-learning-based deep neural network that accurately predicts genome folding from...
Base Sequence CCCTC-Binding Factor Chromatin Computer Simulation Genome, Human Genomic Structural Variation Genomics Humans Models, Genetic Neural Networks, Computer
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A sequence-based deep learning approach to predict CTCF-mediated chromatin loop.

Briefings in bioinformatics 33634313
Three-dimensional (3D) architecture of the chromosomes is of crucial importance for transcription regulation and DNA replication. Various high-throughput chromosome conformation capture-based methods have revealed that CTCF-mediated chromatin loops a...
CCCTC-Binding Factor Chromatin Datasets as Topic Gene Expression Regulation Genome, Human Humans K562 Cells MCF-7 Cells Molecular Conformation Neural Networks, Computer Nucleotide Motifs Software
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InsuLock: A Weakly Supervised Learning Approach for Accurate Insulator Prediction, and Variant Impact Quantification.

Genes 35456427
Mapping chromatin insulator loops is crucial to investigating genome evolution, elucidating critical biological functions, and ultimately quantifying variant impact in diseases. However, chromatin conformation profiling assays are usually expensive, ...
CCCTC-Binding Factor Chromatin Genome Neural Networks, Computer Supervised Machine Learning
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CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

Bioinformatics (Oxford, England) 35997565
MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure of cells. Although variou...
Binding Sites CCCTC-Binding Factor Cell Line Chromatin Deep Learning
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DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery.

Genes 38397134
Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facili...
CCCTC-Binding Factor Deep Learning DNA Humans Precursor T-Cell Lymphoblastic Leukemia-Lymphoma Transcription Factors
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Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites.

Nucleic acids research 38950902
CCCTC-binding factor (CTCF) is an insulator protein that binds to a highly conserved DNA motif and facilitates regulation of three-dimensional (3D) nuclear architecture and transcription. CTCF binding sites (CTCF-BSs) reside in non-coding DNA and are...
Binding Sites Breast Neoplasms CCCTC-Binding Factor Chromatin Female Humans Machine Learning Male Mutation Neoplasms Prostatic Neoplasms Protein Binding
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Probabilistic and machine-learning methods for predicting local rates of transcription elongation from nascent RNA sequencing data.

Nucleic acids research 39964478
Rates of transcription elongation vary within and across eukaryotic gene bodies. Here, we introduce new methods for predicting elongation rates from nascent RNA sequencing data. First, we devise a probabilistic model that predicts nucleotide-specific...
Binding Sites CCCTC-Binding Factor DNA Methylation Epigenomics Histone Code Histones Humans Machine Learning Models, Statistical Neural Networks, Computer RNA Splice Sites Sequence Analysis, RNA Transcription Elongation, Genetic
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