AIMC Topic: Chromatin Immunoprecipitation Sequencing

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TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile.

PLoS computational biology Sep 12, 2022
Determining transcriptional factor binding sites (TFBSs) is critical for understanding the molecular mechanisms regulating gene expression in different biological conditions. Biological assays designed to directly mapping TFBSs require large sample s...
Transcription Factors Binding Sites Chromatin Immunoprecipitation Sequencing Deep Learning Protein Binding
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Prediction of the transcription factor binding sites with meta-learning.

Methods (San Diego, Calif.) Apr 21, 2022
With the accumulation of ChIP-seq data, convolution neural network (CNN)-based methods have been proposed for predicting transcription factor binding sites (TFBSs). However, biological experimental data are noisy, and are often treated as ground trut...
Transcription Factors Binding Sites Chromatin Immunoprecipitation Sequencing Neural Networks, Computer Protein Binding
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Base-resolution prediction of transcription factor binding signals by a deep learning framework.

PLoS computational biology Mar 9, 2022
Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framewo...
Deep Learning Protein Binding Binding Sites Transcription Factors Chromatin Immunoprecipitation Sequencing
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Domain-adaptive neural networks improve cross-species prediction of transcription factor binding.

Genome research Jan 18, 2022
The intrinsic DNA sequence preferences and cell type-specific cooperative partners of transcription factors (TFs) are typically highly conserved. Hence, despite the rapid evolutionary turnover of individual TF binding sites, predictive sequence model...
Neural Networks, Computer Protein Binding Binding Sites Chromatin Immunoprecipitation Sequencing Computational Biology Transcription Factors
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CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data.

PLoS computational biology Dec 13, 2021
Cis-Regulatory elements (cis-REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis-REs in a given cell type (including from...
Computational Biology Monocytes DNA Deep Learning Single-Cell Analysis Islets of Langerhans Regulatory Sequences, Nucleic Acid Chromatin Immunoprecipitation Sequencing Humans Cells, Cultured
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Multi-Scale Capsule Network for Predicting DNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analysis of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) have been introduced to motif discovery ...
Chromatin Immunoprecipitation Sequencing Binding Sites Sequence Analysis, Protein Transcription Factors DNA-Binding Proteins Deep Learning Protein Binding Computational Biology Algorithms
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Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
The rapid development of high-throughput sequencing technology provides unique opportunities for studying of transcription factor binding sites, but also brings new computational challenges. Recently, a series of discriminative motif discovery (DMD) ...
Humans Algorithms Computational Biology Machine Learning Binding Sites Transcription Factors Chromatin Immunoprecipitation Sequencing DNA
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DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data.

Nature communications Oct 6, 2021
Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. Existing feature selection methods perform inconsistently across datasets, occasionally even...
Gene Expression Genes, Mitochondrial Chromatin Immunoprecipitation Sequencing Sequence Analysis, RNA Cluster Analysis Machine Learning RNA-Seq Software Humans Arthritis, Rheumatoid Research Design Algorithms Single-Cell Analysis
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Biologically relevant transfer learning improves transcription factor binding prediction.

Genome biology Sep 27, 2021
BACKGROUND: Deep learning has proven to be a powerful technique for transcription factor (TF) binding prediction but requires large training datasets. Transfer learning can reduce the amount of data required for deep learning, while improving overall...
Transcription Factors Genome Machine Learning Chromatin Immunoprecipitation Sequencing
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Deep learning-based enhancement of epigenomics data with AtacWorks.

Nature communications Mar 8, 2021
ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks...
Mice Chromatin Immunoprecipitation Sequencing Humans Animals Regulatory Sequences, Nucleic Acid Epigenomics Leukocytes Brain Deep Learning Chromatin
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