Article

Deep neural networks identify context-specific determinants of transcription factor binding affinity

2020-02-27

Abstract excerpt

Transcription factors (TFs) bind DNA by recognizing highly specific DNA sequence motifs, typically of length 6-12bp. A TF motif can occur tens of thousands of times in the human genome, but only a small fraction of those sites are actually bound. Despite the availability of genome-wide TF binding maps for hundreds of TFs, predicting whether a given motif occurrence is bound and identifying the influential context...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: e3063ad7-41ee-5d4e-9399-e6c05f20b3c4
DOI: 10.1101/2020.02.26.965343

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Deep neural networks identify context-specific determinants of transcription factor binding affinityDOI 10.1101/2020.02.26.965343Read

Base-resolution prediction of transcription factor binding signals by a deep learning frameworkSemantic proximity Read

ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variantsSemantic proximity Read

Using attentive gated neural networks to quantify the impact of non-coding variants on transcription factor binding affinitySemantic proximity Read

DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin AccessibilitySemantic proximity Read

Integrating Protein and DNA Embeddings for Improving Genome-Wide Transcription Factor Binding Site PredictionSemantic proximity Read

DNA sequence quantitatively encodes CTCF-binding affinity at genome scaleSemantic proximity Read

Learning interpretable representation for context-specific transcription regulatory networks using a foundation modelSemantic proximity Read

Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning FrameworkSemantic proximity Read

Large-scale prediction of transcription factor binding across human cell types informs regulatory genomics and reveals promiscuous occupancy associated with chromatin contactsSemantic proximity Read

maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networksSemantic proximity Read

Prediction of Transcription Factor DNA Binding Affinity with High-Throughput <i>K</i> <sub>d</sub> Measurements and Deep LearningSemantic proximity Read

A Bag-Of-Motif Model Captures Cell States at Distal Regulatory SequencesSemantic proximity Read