Abstract
Integrated analysis of multiple genome-wide transcription factor (TF)-binding profiles will be vital to advance our understanding of the global impact of TF binding. However, existing methods for measuring similarity in large numbers of chromatin immunoprecipitation assays with sequencing (ChIP-seq), such as correlation, mutual information or enrichment analysis, are limited in their ability to display functionally relevant TF relationships. In this study, we propose the use of graphical models to determine conditional independence between TFs and showed that network visualization provides a promising alternative to distinguish ‘direct’ versus ‘indirect’ TF interactions. We applied four algorithms to measure ‘direct’ dependence to a compendium of 367 mouse haematopoietic TF ChIP-seq samples and obtained a consensus network known as a ‘TF association network’ where edges in the network corresponded to likely causal pairwise relationships between TFs. The ‘TF association network’ illustrates the role of TFs in developmental pathways, is reminiscent of combinatorial TF regulation, corresponds to known protein–protein interactions and indicates substantial TF-binding reorganization in leukemic cell types. With the rapid increase in TF ChIP-Seq data sets, the approach presented here will be a powerful tool to study transcriptional programmes across a wide range of biological systems.
Highlights
Transcription factors (TFs) are an important class of proteins that bind to cis-regulatory elements and control the transcription of nearby genes
Various bioinformatics approaches have been used to uncover protein–protein interactions from a collection of ChIP-seq data to gain a global understanding of combinatorial TF binding and interaction networks in the regulation of cell fate decisions [33, 34]
Our group showed that the integration of disparate public TF ChIP-seq experiments of blood-related samples can form a coherent picture of constrained sequence-specific TF pair interaction with the DNA [35]
Summary
Transcription factors (TFs) are an important class of proteins that bind to cis-regulatory elements and control the transcription of nearby genes They have long been recognized as important regulators of haematopoietic cell-type identity and, have been extensively studied to gain a better understanding of their role in the differentiation of normal blood stem cells [1,2,3,4,5,6,7]. The binding of distinct TFs in the same cell type is known to be highly correlated and, correlation or enrichment analysis alone is Felicia Ng is a postdoctoral researcher at the Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute and Cambridge Institute for Medical Research. Submitted: 8 December 2015; Received (in revised form): 20 September 2016
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