title: 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.
authors: Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, California, United States of America ; Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America.
authors: Konrad J
authors: KJ
authors: Karczewski
authors: Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America.
authors: Michael
authors: M
authors: Snyder
authors: Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America ; Department of Bioengineering, Stanford University School of Medicine, Stanford, California, United States of America.
authors: Russ B
authors: RB
authors: Altman
authors: Department of Biomedical Informatics, Department of Systems Biology, and Department of Medicine, Columbia University, New York, New York, United States of America.
authors: Nicholas P
authors: NP
authors: Tatonetti
mesh_terms: D002453:Cell Cycle / Q000235:genetics; D019295:Computational Biology; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation / Q000235:genetics*; D006801:Humans; D058977:Molecular Sequence Annotation; D060066:Protein Interaction Maps / Q000235:genetics*; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism*
chemical_list: D014157:Transcription Factors