ArticlePloS one2018
Detection of cooperatively bound transcription factor pairs using ChIP-seq peak intensities and expectation maximization.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- Expanding the DNA Motif Lexicon of the Transcriptional Regulatory Code.bioRxiv : the preprint server for biology · 2025Article
- Differential Transcriptional Programs Reveal Modular Network Rearrangements Associated with Late-Onset Alzheimer's Disease.International journal of molecular sciences · 2025Article
- Transcription activation inEcoSal Plus · 2024Review
- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- Multiomic profiling of transcription factor binding and function in human brain.Nature neuroscience · 2024Article
- High-throughput data and modeling reveal insights into the mechanisms of cooperative DNA-binding by transcription factor proteins.Nucleic acids research · 2023Article
- Profiling chromatin accessibility responses in human neutrophils with sensitive pathogen detection.Life science alliance · 2021Article
- NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information.Biology direct · 2020Article
- Factors that influence the androgen receptor cistrome in benign and malignant prostate cells.Molecular oncology · 2019Article
- PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition.Nature communications · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcription factors (TFs) often work cooperatively, where the binding of one TF to DNA enhances the binding affinity of a second TF to a nearby location. Such cooperative binding is important for activating gene expression from promoters and enhancers in both prokaryotic and eukaryotic cells. Existing methods to detect cooperative binding of a TF pair rely on analyzing the sequence that is bound. We propose a method that uses, instead, only ChIP-seq peak intensities and an expectation maximization (CPI-EM) algorithm. We validate our method using ChIP-seq data from cells where one of a pair of TFs under consideration has been genetically knocked out. Our algorithm relies on our observation that cooperative TF-TF binding is correlated with weak binding of one of the TFs, which we demonstrate in a variety of cell types, including E. coli, S. cerevisiae and M. musculus cells. We show that this method performs significantly better than a predictor based only on the ChIP-seq peak distance of the TFs under consideration. This suggests that peak intensities contain information that can help detect the cooperative binding of a TF pair. CPI-EM also outperforms an existing sequence-based algorithm in detecting cooperative binding. The CPI-EM algorithm is available at https://github.com/vishakad/cpi-em.
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