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Gene regulation is a complex process involving the role of several genomicelements which work in concert to drive spatio-temporal expression. Theexperimental characterization of gene regulatory elements is a very complex andresource-intensive process. One of the major goals in computational biology isthe \textit{in-silico} annotation of previously uncharacterized elements usingresults from the subset of known, previously annotated, regulatory elements.  The recent results of the ENCODE project (\emph{http://encode.nih.gov})presented in-depth analysis of such functional (regulatory) non-coding elementsfor 1% of the human genome. It is hoped that the results obtained on thissubset can be scaled to the rest of the genome. This is an extremely importanteffort which will enable faster dissection of other functional elements in keybiological processes such as disease progression and organ development(\cite{Kleinjan2005},\cite{Lieb2006}. The computational annotation of thesehitherto uncharacterized regions would require an identification of featuresthat have good predictive value.  In this work, we study transcriptional regulation as a problem inheterogeneous data integration, across sequence, expression and interactomelevel attributes. Using the example of the \textit{Gata2} gene and its recentlydiscovered urogenital enhancers \cite{Khandekar2004} as a case study, weexamine the predictive value of various high throughput functional genomicassays (from projects like ENCODE and SymAtlas) in characterizing theseenhancers and their regulatory role. Observing results from the application ofmodern statistical learning methodologies for each of these data modalities, wepropose a set of features that are most discriminatory to find these enhancers.

Understanding Transcriptional Regulation Using De-novo Sequence Motif Discovery, Network Inference and Interactome Data