AIMC Topic: Chromatin Immunoprecipitation Sequencing

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Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli.

Nature communications May 7, 2025
The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs us...
Transcription Factors Escherichia coli Binding Sites Chromatin Immunoprecipitation Sequencing Escherichia coli Proteins Neural Networks, Computer Protein Binding DNA, Bacterial
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DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration.

BMC bioinformatics Apr 1, 2025
BACKGROUND: Chromatin loops are critical for the three-dimensional organization of the genome and gene regulation. Accurate identification of chromatin loops is essential for understanding the regulatory mechanisms in disease. However, current mainst...
Software Deep Learning Computational Biology Humans Chromatin Chromatin Immunoprecipitation Sequencing
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Deep Neural Network-Mining of Rice Drought-Responsive TF-TAG Modules by a Combinatorial Analysis of ATAC-Seq and RNA-Seq.

Plant, cell & environment Mar 31, 2025
Drought is a critical risk factor that impacts rice growth and yields. Previous studies have focused on the regulatory roles of individual transcription factors in response to drought stress. However, there is limited understanding of multi-factor st...
Gene Regulatory Networks Transcription Factors Oryza Neural Networks, Computer RNA-Seq Gene Expression Regulation, Plant Droughts Chromatin Immunoprecipitation Sequencing Data Mining Plant Proteins Stress, Physiological
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CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

BMC genomics Mar 18, 2025
BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent yea...
Computational Biology Transcription Factors DNA Convolutional Neural Networks Protein Binding Chromatin Immunoprecipitation Sequencing Binding Sites Software Algorithms Neural Networks, Computer
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ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning.

Nature communications Jan 2, 2025
Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. Here, we develop ChromatinHD, a pair of scale-adaptive models that uses the...
Chromatin Chromatin Immunoprecipitation Sequencing Gene Expression Regulation Nucleic Acid Conformation Regulatory Sequences, Nucleic Acid Transcription Factors DNA Machine Learning Animals Humans Mice Single-Cell Analysis Computational Biology
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TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.

Genome biology Jul 10, 2024
Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence fea...
Humans Machine Learning Binding Sites Protein Binding Nucleotide Motifs Transcription Factors Chromatin Immunoprecipitation Sequencing
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Predicting Transcription Factor Binding Sites with Deep Learning.

International journal of molecular sciences May 3, 2024
Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with diff...
HeLa Cells Binding Sites Chromatin Immunoprecipitation Sequencing Computational Biology Transcription Factors Cell Line Deep Learning Humans Protein Binding
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Multi-batch single-cell comparative atlas construction by deep learning disentanglement.

Nature communications Jul 12, 2023
Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield ...
Deep Learning Ascomycota Chromatin Immunoprecipitation Sequencing Biological Assay Embryonic Development
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How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulati...
Binding Sites Chromatin Immunoprecipitation Sequencing Virulence Nucleotides Humans Transcription Factors Deep Learning DNA
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A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder.

International journal of molecular sciences Mar 1, 2023
Recent advances in single-cell sequencing assays for the transposase-accessibility chromatin (scATAC-seq) technique have provided cell-specific chromatin accessibility landscapes of cis-regulatory elements, providing deeper insights into cellular sta...
Chromatin Immunoprecipitation Sequencing Transcription Factors Deep Learning Gene Expression Regulation Single-Cell Analysis Chromatin
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