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

PloS One
Vishaka DattaSandeep Krishna

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 C...Continue Reading

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Citations

Sep 15, 2019·Molecular Oncology·Ben T CopelandJeremy O Jones

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Datasets Mentioned

BETA
GSE92255

Methods Mentioned

BETA
ChIP-seq

Software Mentioned

get
SPRY
Scipy
CPI
STAP
EM
narrowPeak
STAP ( Sequence To Affinity Program )
fasta
pybedtools

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