Coherent Functional Modules Improve Transcription Factor Target Identification, Cooperativity Prediction, and Disease Association

Abstract

Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high false positive rate, identifying coherent functional target sets is difficult. We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments. We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known. Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships. As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset. These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease.

A resource for characterizing genome‐wide binding and putative target genes of transcription factors expressed during secondary growth and wood formation in Populus

“…Indeed, in most cases in both animals (Cheng et al, 2012) and plants (Heyndrickx et al, 2014) binding of a single transcription factor is not sufficient to predict gene expression levels, and instead transcription factors tend to co-associate in context-specific patterns to integrate developmental cues into determining gene expression outcomes (Karczewski et al, 2014;Teng et al, 2014). Our analysis of overlapping binding regions and putative target genes between different transcription factors examined two subgroups of putative target genes: putative target genes that were assigned to overlapping versus putative target genes assigned to non-overlapping binding regions of two transcription factors.…”

The Escherichia coli Transcriptome Mostly Consists of Independently Regulated Modules

“…Prior application of ICA to microarray expression data 12 has identified co-expressed, functionally-related gene sets [13][14][15] that often map to metabolic pathways 16,17 . The overall expression levels, or activities, of the gene sets have been leveraged to classify tumor samples 18,19 and connect transcriptional modules to disease states 20 .…”

Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases

“…Pathway- and network-based approaches, on the other hand, have been successful at identifying relevant pathways or modules based on the connectivity between trait-associated genes, but current studies typically rely on protein-protein interaction 10,11 , co-expression 12 or functional association networks 13 lacking fine-grained regulatory and, with few exceptions 14–16 , tissue-specific information. Indeed, a suitable compendium of tissue-specific regulatory circuits was previously not available, as most studies focused on building gene regulatory networks either globally 17–19 or for a single tissue or condition of in terest 20–22 .…”