Another good way to explore histone and TF chip-seq data is to use data from the Ensembl regulatory build: http://www.ensembl.org/info/docs/funcgen/index.html

Navigate to a gene of interest, such as BRCA2: http://www.ensembl.org/Homo_sapiens/Location/View?g=ENSG00000139618;r=13:32889611-32973805

The click on "Configure this page" on the left hand, which will open a pop-up, in which you should select "Regulatory evidence" from under the regulation option. This will display a matrix of TFs and histone marks arranged by cell type. You can then select multiple cells by clicking-and-dragging across the matrix, or select rows/columns by clicking on the header and "select all"-ing. Once you have chosen your set of regulatory features of interest, click the check mark at the top right to have these tracks be displayed in the main Ensembl "Region in detail" view.

One of the good things about this dataset is that the tracks are all processed using a common pipeline and therefore are (more) directly comparable than sites that combine data based on variable mappers/peak callers.

IMHO, we don't have a standard approach for regulatory genome analysis. AFAIK, at the moment it is custom-designed for the disorder, gene or tissue of interest.

I would recommend this excellent review article for a background reading.

Also if you are specifically interested in Encode data, this how-to style article written by the authors with free ENCODE / UCSC Genome Browser tutorials at OpenHelix will be very useful.

I think this is a direction that the Pritchard lab at U Chicago is taking. What you are really talking about is the intersection of eQTL data, disease association (genetic, medical or other), and DNA motif analysis. Yes, this is cell type specific, but data for a similar or any cell will be of value because it'll show something about the gene's behavior. I would take a look at http://eqtl.uchicago.edu/Home.html and things like CENTIPEDE (inferred transcription factor binding sites) and the eQTL data, whose shared sites on the genome really begin to describe where you want to take your research.

And, a +1 for Khader as the ENCODE et al projects will give a lot of useful ChIP data about regulatory regions.

I would start with regulation tracks in the UCSC genome browser (make sure you use hg18) or ensemble, you can find data from a lot of ChIP-seq experiments there.