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Gene regulation in higher eukaryotes involves a complex interplay between thegene proximal promoter and distal genomic elements (such as enhancers) whichwork in concert to drive spatio-temporal expression. The experimentalcharacterization 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, annotated, regulatory elements.  The computational annotation of these hitherto uncharacterized regions wouldrequire an identification of features that have good predictive value forregulatory behavior.  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 in characterizing these enhancers and their regulatory role. Observingresults from the application of modern statistical learning methodologies foreach of these data modalities, we propose a set of attributes that are mostdiscriminatory in the localization and behavior of these enhancers.

Understanding Distal Transcriptional Regulation from Sequence Motif, Network Inference and Interactome Perspectives