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

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. © 2014 karczewski et al.