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PLoS One. 2018 Jul 17;13(7):e0199771. doi: 10.1371/journal.pone.0199771. eCollection 2018.

Detection of cooperatively bound transcription factor pairs using ChIP-seq peak intensities and expectation maximization.

Author information

1
Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, TIFR, Bengaluru 560065, India.
2
The Institute of Mathematical Sciences/HBNI, Taramani, Chennai 600 113, India.

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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