AIMC Topic: Nucleosomes

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Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.

Cell genomics Feb 27, 2025
Deep learning models have advanced our ability to predict cell-type-specific chromatin patterns from transcription factor (TF) binding motifs, but their application to perturbed contexts remains limited. We applied transfer learning to predict how co...
Nucleosomes SOX9 Transcription Factor Binding Sites Chromatin Animals Deep Learning Mice Transcription Factors Protein Binding Humans
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Developing a method for predicting DNA nucleosomal sequences using deep learning.

Technology and health care : official journal of the European Society for Engineering and Medicine Nov 20, 2024
BackgroundDeep learning excels at processing raw data because it automatically extracts and classifies high-level features. Despite biology's low popularity in data analysis, incorporating computer technology can improve biological research.Objective...
Neural Networks, Computer Algorithms Deep Learning Humans Nucleosomes Sequence Analysis, DNA DNA
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Context dependent prediction in DNA sequence using neural networks.

PeerJ Sep 20, 2022
One way to better understand the structure in DNA is by learning to predict the sequence. Here, we trained a model to predict the missing base at any given position, given its left and right flanking contexts. Our best-performing model was a neural n...
Nucleosomes Base Sequence Genome, Human Mice DNA Neural Networks, Computer Humans Animals
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Prediction of nucleosome dynamic interval based on long-short-term memory network (LSTM).

Journal of bioinformatics and computational biology May 21, 2022
Nucleosome localization is a dynamic process and consists of nucleosome dynamic intervals (NDIs). We preprocessed nucleosome sequence data as time series data (TSD) and developed a long short-term memory network (LSTM) model for training time series ...
Neural Networks, Computer Nucleosomes
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Nucleosome positioning based on DNA sequence embedding and deep learning.

BMC genomics Apr 13, 2022
BACKGROUND: Nucleosome positioning is the precise determination of the location of nucleosomes on DNA sequence. With the continuous advancement of biotechnology and computer technology, biological data is showing explosive growth. It is of practical ...
Nucleosomes Drosophila melanogaster Caenorhabditis elegans Animals Plant Extracts Base Sequence Deep Learning
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Machine learning predicts nucleosome binding modes of transcription factors.

BMC bioinformatics Mar 30, 2021
BACKGROUND: Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding...
Binding Sites Nucleosomes Protein Binding Transcription Factors Machine Learning Amino Acid Sequence
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CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification.

BMC bioinformatics Sep 16, 2020
BACKGROUND: Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization...
Neural Networks, Computer Genomics Humans Nucleosomes
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Deep learning architectures for prediction of nucleosome positioning from sequences data.

BMC bioinformatics Nov 20, 2018
BACKGROUND: Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several b...
Neural Networks, Computer Databases, Nucleic Acid Reproducibility of Results Base Sequence Nucleosomes Animals Deep Learning Saccharomyces cerevisiae ROC Curve Humans
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Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.

BMC bioinformatics Jan 11, 2016
BACKGROUND: Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often ...
Protein Processing, Post-Translational Chromatin Algorithms DNA Chromatin Immunoprecipitation Binding Sites Cell Lineage Humans Nucleosomes Genome, Human Transcription Factors Gene Expression Regulation Machine Learning Histones
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Quantitative spatial analysis of chromatin biomolecular condensates using cryoelectron tomography.

Proceedings of the National Academy of Sciences of the United States of America May 13, 2025
Phase separation is an important mechanism to generate certain biomolecular condensates and organize the cell interior. Condensate formation and function remain incompletely understood due to difficulties in visualizing the condensate interior at hig...
Animals Nucleosomes Electron Microscope Tomography Biomolecular Condensates Cryoelectron Microscopy Chromatin
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