ArticleMolecular therapy. Nucleic acids2021
A computational framework for identifying the transcription factors involved in enhancer-promoter loop formation.
Article in Molecular therapy. Nucleic acids, 2021. 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.
- RNA-independent cis-autoregulatory circuits within bidirectional gene pairs control metabolism.Science advances · 2026Article
- Identification of co-localised transcription factors based on paired motifs analysis.IET systems biology · 2024Article
- QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking.BMC chemistry · 2024Article
- Comparative three-dimensional genome architectures of adipose tissues provide insight into human-specific regulation of metabolic homeostasis.The Journal of biological chemistry · 2023Article
- Prediction of CTCF loop anchor based on machine learning.Frontiers in genetics · 2023Article
- Predicting enhancer-promoter interaction based on epigenomic signals.Frontiers in genetics · 2023Article
- Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM.Computational and mathematical methods in medicine · 2022Article
- Identification of Nine mRNA Signatures for Sepsis Using Random Forest.Computational and mathematical methods in medicine · 2022Article
- Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design.Briefings in bioinformatics · 2021Article
- Application of Multilayer Network Models in Bioinformatics.Frontiers in genetics · 2021Review
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Authors and funding
5 authors.
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Abstract
The pairwise interaction between transcription factors (TFs) plays an important role in enhancer-promoter loop formation. Although thousands of TFs in the human genome have been found, only a few TF pairs have been demonstrated to be related to loop formation. It is still a challenge to determine which TF pairs could be involved in the enhancer-promoter regulation network. This work describes a computational framework to identify TF pairs in enhancer-promoter regulation. By integrating different levels of data derived from Promoter Capture Hi-C, chromatin immunoprecipitation sequencing (ChIP-seq) of histone marks, RNA-seq, protein-protein interaction (PPI), and TF motif, we identified 361 significant TF pairs and constructed a TF interaction network. From the network, we found several hub-TFs, which may have important roles in the regulation of long-range interactions. Our studies extended TF pairs identified in other experimental and computational approaches. These findings will help the further study of long-range interactions between enhancers and promoters.
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