Detection of cooperatively bound transcription factor pairs using ChIP-seq peak intensities and expectation maximization.
Title | Detection of cooperatively bound transcription factor pairs using ChIP-seq peak intensities and expectation maximization. |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Datta V, Siddharthan R, Krishna S |
Journal | PLoS One |
Volume | 13 |
Issue | 7 |
Pagination | e0199771 |
Date Published | 2018 |
ISSN | 1932-6203 |
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. |
DOI | 10.1371/journal.pone.0199771 |
Alternate Journal | PLoS ONE |
PubMed ID | 30016330 |