ArticleScientific reports2016
Functional transcription factor target discovery via compendia of binding and expression profiles.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Combinatorial transcription factor binding encodes cis-regulatory wiring of mouse forebrain GABAergic neurogenesis.Developmental cell · 2025Article
- Transcription Factor-Wide Association Studies to Identify Functional SNPs in Alzheimer's Disease.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Article
- Unexpected mutual regulation underlies paralogue functional diversification and promotes epithelial tissue maturation in Tribolium.Communications biology · 2020Article
- Stems cells, big data and compendium-based analyses for identifying cell types, signalling pathways and gene regulatory networks.Biophysical reviews · 2019Review
- Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations.F1000Research · 2018Article
- Regulation of circadian clock transcriptional output by CLOCK:BMAL1.PLoS genetics · 2018Article
- DNA methylation at the mu-1 opioid receptor gene (Pharmacogenomics and personalized medicine · 2017Article
- Target Gene Prediction of Transcription Factor Using a New Neighborhood-regularized Tri-factorization One-class Collaborative Filtering Algorithm.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and BiomedicineArticle
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Authors and funding
3 authors.
Funding
Abstract
Genome-wide experiments to map the DNA-binding locations of transcription-associated factors (TFs) have shown that the number of genes bound by a TF far exceeds the number of possible direct target genes. Distinguishing functional from non-functional binding is therefore a major challenge in the study of transcriptional regulation. We hypothesized that functional targets can be discovered by correlating binding and expression profiles across multiple experimental conditions. To test this hypothesis, we obtained ChIP-seq and RNA-seq data from matching cell types from the human ENCODE resource, considered promoter-proximal and distal cumulative regulatory models to map binding sites to genes, and used a combination of linear and non-linear measures to correlate binding and expression data. We found that a high degree of correlation between a gene's TF-binding and expression profiles was significantly more predictive of the gene being differentially expressed upon knockdown of that TF, compared to using binding sites in the cell type of interest only. Remarkably, TF targets predicted from correlation across a compendium of cell types were also predictive of functional targets in other cell types. Finally, correlation across a time course of ChIP-seq and RNA-seq experiments was also predictive of functional TF targets in that tissue.
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