MOTIVATION: 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.
AVAILABILITY: All code used for this work is available at 
http://dinner-group.uchicago.edu/downloads.html.