Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework.
Abstract
Transcription Factors (TFs) bind to DNA and control activity of target genes. Here, we present ChIPanalyser, a user-friendly, versatile and powerful R/Bioconductor package predicting and modelling the binding of TFs to DNA. ChIPanalyser performs similarly to state-of-the-art tools, but is an explainable model and provides biological insights into binding mechanisms of TFs. We focused on investigating the binding mechanisms of three TFs that are known architectural proteins CTCF, BEAF-32 and su(Hw) in three Drosophila cell lines (BG3, Kc167 and S2). While CTCF preferentially binds only to a subset of high affinity sites located mainly in open chromatin, BEAF-32 binds to most of its high affinity binding sites available in open chromatin. In contrast, su(Hw) binds to both open chromatin and also partially closed chromatin. Most importantly, differences in TF binding profiles between cell lines for these TFs are mainly driven by differences in DNA accessibility and not by differences in TF concentrations between cell lines. Finally, we investigated binding of Hox TFs in Drosophila and found that Ubx binds only in open chromatin, while Abd-B and Dfd are capable to bind in both open and partially closed chromatin. Overall, our results show that TFs display different binding mechanisms and that our model is able to recapitulate this diverse repertoire of mechanisms.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 6
- FWCI
- 0.32
- Field percentile
- top 50%
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Identification of mammalian transcription factors that bind to inaccessible chromatin
Romana T Pop, Alessandra Pisante, … Nicolae Radu Zabet·Nucleic Acids ResearchPeer-reviewed·Jul 11, 2023
- Joint sequence & chromatin neural networks characterize the differential abilities of Forkhead transcription factors to engage inaccessible chromatin
Sonny Arora, Jianyu Yang, … Shaun Mahony·bioRxiv·Oct 6, 2023
- Systematic analysis of the genomic features involved in the binding preferences of transcription factors
R. Romero, Christophe Menichelli, … Laurent Bréhélin·bioRxiv·Aug 16, 2022
- An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced TF binding
Divyanshi Srivastava, Begüm Aydın, … Shaun Mahony·Genome biologyPeer-reviewed·Jan 7, 2021
- Mocap: Large-scale inference of transcription factor binding sites from chromatin accessibility
Xi Xian Chen, Bowen Yu, … Richard Bonneau·Nucleic Acids ResearchPeer-reviewed·Mar 6, 2017
- Mitotic chromosome binding predicts transcription factor properties in interphase
Mahé Raccaud, Elias T. Friman, … David M. Suter·Nature CommunicationsPeer-reviewed·Jan 30, 2019