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Chromatin Immunoprecipitation Sequencing

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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
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...

Domain-adaptive neural networks improve cross-species prediction of transcription factor binding.

Genome research
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...

Base-resolution prediction of transcription factor binding signals by a deep learning framework.

PLoS computational biology
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...

Prediction of the transcription factor binding sites with meta-learning.

Methods (San Diego, Calif.)
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...

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

PLoS computational biology
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...

LanceOtron: a deep learning peak caller for genome sequencing experiments.

Bioinformatics (Oxford, England)
MOTIVATION: Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome wide. Regions where these elements are found appear as peaks in the analog signal of an assay's ...

A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder.

International journal of molecular sciences
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...

Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling.

Briefings in bioinformatics
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...

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
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...

Multi-batch single-cell comparative atlas construction by deep learning disentanglement.

Nature communications
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 ...