ArticleGenome biology2022
Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Multiscale modeling primer: Focus on chromatin and epigenetics.The Journal of biological chemistry · 2026Review
- ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks.Cell genomics · 2026Article
- Modeling strategies forBioinformatics advances · 2026Article
- Analysis of chromatin structure reveals the connection between sQTLs and the splicing of distant genes.Scientific reports · 2025Article
- Unmeasured human transcription factor ChIP-seq data shape functional genomics and demand strategic prioritization.Briefings in functional genomics · 2025Article
- ChIP-DIP maps binding of hundreds of proteins to DNA simultaneously and identifies diverse gene regulatory elements.Nature genetics · 2024Article
- Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data.NAR genomics and bioinformatics · 2024Article
- Multiomic profiling of transcription factor binding and function in human brain.Nature neuroscience · 2024Article
- Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet.Genome biology · 2024Article
- Interpretable predictive models of genome-wide aryl hydrocarbon receptor-DNA binding reveal tissue-specific binding determinants.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
- Identification of mammalian transcription factors that bind to inaccessible chromatin.Nucleic acids research · 2023Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- High-throughput time series expression profiling of Plasmopara halstedii infecting Helianthus annuus reveals conserved sequence motifs upstream of co-expressed genes.BMC genomics · 2023Article
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
- xcore: an R package for inference of gene expression regulators.BMC bioinformatics · 2023Article
- Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome.Genome biology · 2022Article
- Biologically relevant transfer learning improves transcription factor binding prediction.Genome biology · 2021Article
- ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination.Nucleic acids research · 2021Article
- F-Seq2: improving the feature density based peak caller with dynamic statistics.NAR genomics and bioinformatics · 2021Article
- Gene regulatory network inference as relaxed graph matching.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Existing methods for computational prediction of transcription factor (TF) binding sites evaluate genomic regions with similarity to known TF sequence preferences. Most TF binding sites, however, do not resemble known TF sequence motifs, and many TFs are not sequence-specific. We developed Virtual ChIP-seq, which predicts binding of individual TFs in new cell types, integrating learned associations with gene expression and binding, TF binding sites from other cell types, and chromatin accessibility data in the new cell type. This approach outperforms methods that predict TF binding solely based on sequence preference, predicting binding for 36 TFs (MCC>0.3).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.