AIMC Topic: Chromatin Immunoprecipitation Sequencing

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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...
Deep Learning Sequence Analysis, DNA Algorithms Chromatin Immunoprecipitation Software Chromatin Immunoprecipitation Sequencing Binding Sites DNA-Binding Proteins
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LanceOtron: a deep learning peak caller for genome sequencing experiments.

Bioinformatics (Oxford, England) Sep 15, 2022
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 ...
Humans Software Base Sequence Chromatin Immunoprecipitation Sequencing Sequence Analysis, DNA High-Throughput Nucleotide Sequencing Deep Learning
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TempoMAGE: a deep learning framework that exploits the causal dependency between time-series data to predict histone marks in open chromatin regions at time-points with missing ChIP-seq datasets.

Bioinformatics (Oxford, England) Dec 7, 2021
MOTIVATION: Identifying histone tail modifications using ChIP-seq is commonly used in time-series experiments in development and disease. These assays, however, cover specific time-points leaving intermediate or early stages with missing information....
Chromatin Histone Code Regulatory Sequences, Nucleic Acid Chromatin Immunoprecipitation Sequencing Deep Learning
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Locating transcription factor binding sites by fully convolutional neural network.

Briefings in bioinformatics Sep 2, 2021
Transcription factors (TFs) play an important role in regulating gene expression, thus identification of the regions bound by them has become a fundamental step for molecular and cellular biology. In recent years, an increasing number of deep learnin...
Response Elements Chromatin Immunoprecipitation Sequencing Amino Acid Motifs Transcription Factors Sequence Analysis, Protein A549 Cells Neural Networks, Computer Humans MCF-7 Cells Sequence Analysis, DNA
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A self-attention model for inferring cooperativity between regulatory features.

Nucleic acids research Jul 21, 2021
Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information fr...
Promoter Regions, Genetic Regulatory Elements, Transcriptional Nucleotide Motifs Transcription Factors Cell Line Genomics Humans Chromatin Immunoprecipitation Sequencing Deep Learning Arabidopsis
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Identification of Gene Regulatory Networks from Single-Cell Expression Data.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Single-cell RNAseq is an emerging technology that allows the quantification of gene expression in individual cells. In plants, single-cell sequencing technology has been applied to generate root cell expression maps under many experimental conditions...
Programming Languages RNA-Seq Software Chromatin Immunoprecipitation Sequencing Chromatin Computational Biology Single-Cell Analysis Sequence Analysis, DNA Machine Learning Gene Regulatory Networks
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