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.
Highlights
Transcriptional networks are fundamental to many aspects of biology and disease
Functional modules improve the identification of transcription factor targets We used a set of functional modules derived using independent component analysis (ICA) [14]
We used ENCODE chromatin immunoprecipitation (ChIP)-Seq experimental data to connect transcription factors to individual modules if the factor bound a significant number of genes in that module (Figure 2A; see Materials and Methods)
Summary
Transcriptional networks are fundamental to many aspects of biology and disease. Gene expression is a carefully controlled process orchestrated by the activities of transcription factors (TFs) which regulate the transcription of each gene. Despite the abundance of data about the genomic binding sites for transcription factors, determining transcription factor targets and when factors are active remains challenging. In any given biological context, such as a local cooperative interaction with another transcription factor such as Stat, only a handful of these genes are actively regulated by NFkB at any one time [4]. This property of TF function gives the illusion that TFs are operating broadly when they perform specific contextdependent functions–in many cases with specific partners. These difficulties conspire to make the regulome challenging to study at a global level
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