Hello Brenden,
The Txn Factor ChIP-seq browser track contains clusters of enriched
sites from ChIP-seq performed in cell types from
all 3 tiers of ENCODE. You can see the complete list of cell types
included in this track on the track description page:
http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeRegTfbsClusteredV2
The table you pulled from the Table Browser includes information for
each cluster indicating which cell types contributed to the cluster, and
what the signal strength for that cell type was in the peak contributing
to the cluster, but not the specific coordinates for the peaks on a
cell-specific basis. For that, you would need the individual
per-cell-type datasets. The uniform ChIP-seq peaks that were used to
generate this version of the Txn Factor ChIP track are available at the
ENCODE Analysis Track Hub, for downloads, and access with the Table
Browser (and Genome Browser). For a limited number of datasets, you can
readily extract the peak coordinates for your region of interest using
the Table Browser after loading the track hub. You can do this directly
from the browser Track Connect page, or indirectly from the ENCODE
downloads page (click the TFBS link in the Uniform Peaks section
header):
http://encodeproject.org/ENCODE/downloads.html
Unfortunately the ENCODE data is so voluminous that often downloading
the files and processing at your site is necessary to extract data for
your specific needs. To extract all ChIP-seq peaks in your region of
interest from the 400+ datasets represented here, you would need to
download the TFBS Peaks (SPP) via the download link, and filter the
resulting narrowPeak BED files for your regions of interest.
Fortunately the Peak files are the most compact of the ENCODE files, so
should be relatively easy to download.
Note that the datasets represented here are based on data submitted for
the ENCODE January 2011 data freeze, with analysis reported by the
ENCODE Analysis Working Group in coordinated publications in September
2012:
http://encodeproject.org/ENCODE/analysis.html.
Cheers,
Kate
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Kate Rosenbloom
UCSC Genome Bioinformatics
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