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.
- Supplemental Table 1: Transcription factor, expression module enrichment analysis
- Supplemental Table 2: Transcription factor, expression module correlations
- Supplemental Table 3: Shared modules between transcription factors
- Supplemental Table 4: Transcription factor, disease associations
Please Cite:
Karczewski KJ, Snyder M, Altman RB, Tatonetti NP (2014) Coherent Functional Modules Improve Transcription Factor Target Identification, Cooperativity Prediction, and Disease Association. PLoS Genet 10(2): e1004122. doi:10.1371/journal.pgen.1004122