title: Discovering transcription factor regulatory targets using gene expression and binding data.
abstract: BACKGROUND
  Identifying the target genes regulated by transcription factors (TFs) is the most basic step in understanding gene regulation. Recent advances in high-throughput sequencing technology, together with chromatin immunoprecipitation (ChIP), enable mapping TF binding sites genome wide, but it is not possible to infer function from binding alone. This is especially true in mammalian systems, where regulation often occurs through long-range enhancers in gene-rich neighborhoods, rather than proximal promoters, preventing straightforward assignment of a binding site to a target gene.
  
  
  RESULTS
  We present EMBER (Expectation Maximization of Binding and Expression pRofiles), a method that integrates high-throughput binding data (e.g. ChIP-chip or ChIP-seq) with gene expression data (e.g. DNA microarray) via an unsupervised machine learning algorithm for inferring the gene targets of sets of TF binding sites. Genes selected are those that match overrepresented expression patterns, which can be used to provide information about multiple TF regulatory modes. We apply the method to genome-wide human breast cancer data and demonstrate that EMBER confirms a role for the TFs estrogen receptor alpha, retinoic acid receptors alpha and gamma in breast cancer development, whereas the conventional approach of assigning regulatory targets based on proximity does not. Additionally, we compare several predicted target genes from EMBER to interactions inferred previously, examine combinatorial effects of TFs on gene regulation and illustrate the ability of EMBER to discover multiple modes of regulation.
  
  
  BACKGROUND
  All code used for this work is available at http://dinner-group.uchicago.edu/downloads.html.
authors.affiliation: Department of Chemistry, The University of Chicago, Chicago, IL 60637, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Maienschein-Cline
authors.forename: Jie
authors.initials: J
authors.lastname: Zhou
authors.forename: Kevin P
authors.initials: KP
authors.lastname: White
authors.forename: Roger
authors.initials: R
authors.lastname: Sciammas
authors.forename: Aaron R
authors.initials: AR
authors.lastname: Dinner
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D047628:Estrogen Receptor alpha / Q000378:metabolism; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D011401:Promoter Regions, Genetic; D011485:Protein Binding; D018168:Receptors, Retinoic Acid / Q000378:metabolism; D012045:Regulatory Sequences, Nucleic Acid; D000072482:Retinoic Acid Receptor alpha; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D047628:Estrogen Receptor alpha; C000607071:RARA protein, human; D018168:Receptors, Retinoic Acid; D000072482:Retinoic Acid Receptor alpha; D014157:Transcription Factors
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