This track shows regions of transcription factor binding derived from a large collection
of ChIP-seq experiments performed by the ENCODE project between February 2011 and November 2018,
spanning the first production phase of ENCODE ("ENCODE 2") through the second full production
phase ("ENCODE 3").
Transcription factors (TFs) are proteins that bind to DNA and interact with RNA polymerases to
regulate gene expression. Some TFs contain a DNA binding domain and can bind directly to
specific short DNA sequences ('motifs');
others bind to DNA indirectly through interactions with TFs containing a DNA binding domain.
High-throughput antibody capture and sequencing methods (e.g. chromatin immunoprecipitation
followed by sequencing, or 'ChIP-seq') can be used to identify regions of
TF binding genome-wide. These regions are commonly called ChIP-seq peaks.
ENCODE TF ChIP-seq data were processed using the
ENCODE Transcription Factor ChIP-seq Processing Pipeline to generate peaks of TF binding.
Peaks from 1264 experiments (1256 in hg38) representing 338 transcription factors
(340 in hg38) in 130 cell types (129 in hg38) are combined here into clusters to produce a
summary display showing occupancy regions for each factor.
The underlying ChIP-seq peak data are available from the
ENCODE 3 TF ChIP Peaks tracks (
A gray box encloses each peak cluster of transcription factor occupancy, with the
darkness of the box being proportional to the maximum signal strength observed in any cell type
contributing to the cluster. The HGNC gene name for the transcription factor is shown
to the left of each cluster.
To the right of the cluster a configurable label can optionally display information about the
cell types contributing to the cluster and how many cell types were assayed for the factor
(count where detected / count where assayed).
For brevity in the display, each cell type is abbreviated to a single letter.
The darkness of the letter is proportional to the signal strength observed in the cell line.
Abbreviations starting with capital letters designate
ENCODE cell types initially identified for intensive study,
while those starting with lowercase letters designate cell lines added later in the project.
Click on a peak cluster to see more information about the TF/cell assays contributing to the
cluster and the cell line abbreviation table.
Peaks of transcription factor occupancy ("optimal peak set") from ENCODE ChIP-seq datasets
were clustered using the UCSC hgBedsToBedExps tool.
Scores were assigned to peaks by multiplying the input signal values by a normalization
factor calculated as the ratio of the maximum score value (1000) to the signal value at one
standard deviation from the mean, with values exceeding 1000 capped at 1000. This has the
effect of distributing scores up to mean plus one 1 standard deviation across the score range,
but assigning all above to the maximum score.
The cluster score is the highest score for any peak contributing to the cluster.
The raw data for the ENCODE3 TF Clusters track can be accessed from the
Table Browser or combined with other datasets through the
Data Integrator. This data is stored internally as a BED5+3 MySQL table with additional
metadata tables. For automated analysis and download, the
encRegTfbsClusteredWithCells.hg38.bed.gz track data file can be downloaded from
downloads server, which has 5 fields of BED data followed by a comma-separated list of cell types.
The data can also be queried using the
JSON API or the
Public SQL server.
Thanks to the ENCODE Consortium, the ENCODE ChIP-seq production laboratories, and the
ENCODE Data Coordination Center for generating and processing the TF ChIP-seq datasets used here.
The ENCODE accession numbers of the constituent datasets are available from the peak details page.
Special thanks to Henry Pratt, Jill Moore, Michael Purcaro, and Zhiping Weng, PI, at the
ENCODE Data Analysis Center
(ZLab at UMass Medical Center) for providing the peak datasets, metadata,
and guidance developing this track. Please check the
ZLab ENCODE Public Hubs
for the most updated data.
The integrative view presented here was developed by Jim Kent at UCSC.
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