Article

Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome

2018-02-28

Abstract excerpt

<h4>Motivation</h4> Identifying transcription factor binding sites is the first step in pinpointing non-coding mutations that disrupt the regulatory function of transcription factors and promote disease. ChIP-seq is the most common method for identifying binding sites, but performing it on patient samples is hampered by the amount of available biological material and the cost of the experiment. Existing methods f...

Topics

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

Identifiers and source

Literature Corpus work: 5724ef98-9496-5d41-bb7a-4e8c9d494adb
DOI: 10.1101/168419

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptomeDOI 10.1101/168419Read

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

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

Factorbook: an Updated Catalog of Transcription Factor Motifs and Candidate Regulatory Motif SitesSemantic proximity Read

BART: a transcription factor prediction tool with query gene sets or epigenomic profilesSemantic proximity Read

Explain-seq: an end-to-end pipeline from training to interpretation of sequence-based deep learning modelsSemantic proximity Read

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

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.Semantic proximity Read

Annotations capturing cell-type-specific TF binding explain a large fraction of disease heritabilitySemantic proximity Read

SEMplMe: A tool for integrating DNA methylation effects in transcription factor binding affinity predictionsSemantic proximity Read

Advancing Transcription Factor Binding Site Prediction Using DNA Breathing Dynamics and Sequence Transformers via Cross AttentionSemantic proximity Read

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence modelSemantic proximity Read