AIMC Topic: Chromatin Immunoprecipitation

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LS-GKM: a new gkm-SVM for large-scale datasets.

Bioinformatics (Oxford, England) Mar 15, 2016
UNLABELLED: gkm-SVM is a sequence-based method for predicting and detecting the regulatory vocabulary encoded in functional DNA elements, and is a commonly used tool for studying gene regulatory mechanisms. Here we introduce new software, LS-GKM, whi...
Support Vector Machine DNA Chromatin Immunoprecipitation Humans Software Gene Regulatory Networks
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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 ...
Genome, Human Transcription Factors Humans Histones Protein Processing, Post-Translational Binding Sites Cell Lineage Machine Learning DNA Chromatin Algorithms Gene Expression Regulation Chromatin Immunoprecipitation Nucleosomes
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Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling.

Briefings in bioinformatics Mar 19, 2023
Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair res...
DNA-Binding Proteins Binding Sites Chromatin Immunoprecipitation Chromatin Immunoprecipitation Sequencing Software Algorithms Deep Learning Sequence Analysis, DNA
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Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.

Briefings in bioinformatics Jan 17, 2022
Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) me...
Transcription Factors Base Sequence Binding Sites Chromatin Immunoprecipitation Algorithms Deep Learning
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Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning.

Nucleic acids research Nov 8, 2021
The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and conditions. We propose Epitome, a deep neural network that learns similarities of chromatin accessibility ...
Software Protein Binding Epigenesis, Genetic Transcription Factors Humans Neural Networks, Computer Machine Learning Chromatin Immunoprecipitation Eukaryotic Cells Cell Communication Genome, Human Histones Binding Sites Cell Lineage Atlases as Topic Chromatin
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High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method.

Briefings in bioinformatics Nov 5, 2021
Transcription factors (TFs) are essential proteins in regulating the spatiotemporal expression of genes. It is crucial to infer the potential transcription factor binding sites (TFBSs) with high resolution to promote biology and realize precision med...
Chromatin Immunoprecipitation Deep Learning Protein Binding Computational Biology Transcription Factors Binding Sites
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Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.

Nucleic acids research Jan 11, 2021
Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise ...
Linkage Disequilibrium Genetic Diseases, Inborn Chromatin Immunoprecipitation Histone Code Organ Specificity Molecular Sequence Annotation Binding Sites Polymorphism, Single Nucleotide Epigenomics Models, Genetic Transcription Factors Causality Epigenome Datasets as Topic Humans Deep Learning Genome-Wide Association Study
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Uncovering tissue-specific binding features from differential deep learning.

Nucleic acids research Mar 18, 2020
Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expres...
Myeloid Ecotropic Viral Integration Site 1 Protein Gene Expression Regulation, Developmental Chromatin Immunoprecipitation Branchial Region RNA Organ Specificity High-Throughput Nucleotide Sequencing Mice Animals Protein Isoforms Embryo, Mammalian Computational Biology Binding Sites Models, Genetic Homeodomain Proteins Deep Learning Poisson Distribution Protein Binding
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AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

Nucleic acids research Jun 4, 2019
ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'targ...
Protein Binding Computational Biology Transcription Factors Sequence Analysis, DNA High-Throughput Nucleotide Sequencing Binding Sites Chromatin Immunoprecipitation Humans Machine Learning Algorithms Reproducibility of Results
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Denoising genome-wide histone ChIP-seq with convolutional neural networks.

Bioinformatics (Oxford, England) Jul 15, 2017
MOTIVATION: Chromatin immune-precipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic elements. However, the quality of histone ChI...
High-Throughput Nucleotide Sequencing Chromatin Immunoprecipitation Histone Code Humans Mice Neural Networks, Computer Epigenomics Software Sequence Analysis, DNA Animals Cell Line
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