This track shows regions of transcription factor binding derived from a large collection
of ChIP-seq experiments performed by the ENCODE project, together with DNA binding motifs
identified within these regions by the ENCODE
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 TFBS ChIP-seq data were processed using the computational pipeline developed
by the ENCODE Analysis Working Group to generate uniform peaks of TF binding.
Peaks for 161 transcription factors in
91 cell types are combined here into clusters to produce a summary display showing
occupancy regions for each factor and motif sites within the regions when identified.
Additional views of the underlying ChIP-seq data and documentation on the methods used
to generate it are available from the
ENCODE Uniform TFBS track.
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 line
contributing to the cluster. The HGNC gene name for the transcription factor is shown
to the left of each cluster. Within a cluster, a green highlight indicates
the highest scoring site of a Factorbook-identified canonical motif for
the corresponding factor. (NOTE: motif highlights are shown
only in browser windows of size 50,000 bp or less, and their display can be suppressed by unchecking
the highlight motifs box on the track configuration page).
Arrows on the highlight designate the matching strand of the motif.
The cell lines where signal was detected for the factor are identified by single-letter
abbreviations shown to the right of the cluster.
The darkness of each letter is proportional to the signal strength observed in the cell line.
Abbreviations starting with capital letters designate
ENCODE cell types
identified for intensive study - Tier 1 and Tier 2 -
while those starting with lowercase letters designate Tier 3 cell lines.
Click on a peak cluster to see more information about the TF/cell assays contributing to the
cluster, the cell line abbreviation table, and details about the highest scoring canonical
motif in the cluster.
Peaks of transcription factor occupancy from uniform processing of ENCODE ChIP-seq data
by the ENCODE Analysis Working Group were filtered to exclude datasets that did not pass the
integrated quality metric
(see "Quality Control" section of Uniform TFBS)
and then 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 Factorbook motif discovery and annotation pipeline uses
the MEME-ChIP and FIMO tools from the MEME software suite in conjunction with machine learning methods and
manual curation to merge discovered motifs with known motifs reported in
Motif identifications reported in Wang et al. 2012 (below) were supplemented in this track
with more recent data (derived from newer ENCODE datasets - Jan 2011 through Mar 2012 freezes),
provided by the Factorbook team. Motif identifications from all datasets were merged, with
the most significant value (qvalue) reported being picked when motifs were duplicated in
multiple cell lines. The scores for the selected best-scoring motif sites were then transformed
Release 4 (February 2014) of this track adds display of the Factorbook motifs.
Release 3 (August 2013) added 124 datasets (690 total, vs. 486 in Release 2),
representing all ENCODE TF ChIP-seq passing quality assessment through
the ENCODE March 2012 data freeze.
The peaks used to generate these clusters were called with less stringent thresholds than
used during the January 2011 uniform processing shown in Release 2 of this track.
The contributing datasets are displayed as individual
tracks in the ENCODE Uniform TFBS track, which is available along with the primary data tracks
in the ENC TF Binding Supertrack page.
The clustering for V3/V4 is based on the transcription factor target, and so differs from V2 where clustering was based on antibody.
For the V3/V4 releases, a new track table format, 'factorSource' was used to represent
the primary clusters table and downloads file, wgEncodeRegTfbsClusteredV3.
This format consists of standard BED5
fields (see File Formats)
followed by an experiment count field (expCount) and finally two fields containing comma-separated lists.
The first list field (expNums) contains numeric identifiers for experiments,
keyed to the wgEncodeRegTfbsClusteredInputsV3 table,
which includes such information as the experiment's underlying Uniform TFBS table name,
factor targeted, antibody used, cell type, treatment (if any), and laboratory source.
The second list field (expScores) contains the scores for the corresponding experiments.
For convenience, the
file downloads directory
for this track also contains a BED file,
wgEncodeRegTfbsClusteredWithCellsV3, that lists each cluster with the cluster score followed by a comma-separated list of cell types.
This track shows ChIP-seq data from the Myers Lab at the HudsonAlpha Institute for Biotechnology and by the labs of
at Yale University,
at the University of Southern California,
Kevin Struhl at Harvard,
at the University of Chicago, and
at the University of Texas, Austin.
These data were processed into uniform peak calls by the ENCODE Analysis Working Group pipeline
The clustering of the uniform peaks was performed by UCSC.
The Factorbook motif identifications and localizations (and valuable assistance with
interpretation) were provided by Jie Wang, Bong Hyun Kim and Jiali Zhuang of the
Zlab (Weng Lab) at UMass Medical School.
Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana E, Rozowsky J,
Alexander R et al.
Architecture of the human regulatory network derived from ENCODE data.
Nature. 2012 Sep 6;489(7414):91-100.
Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW, Greven MC, Pierce BG, Dong X, Kundaje A, Cheng Y
Sequence features and chromatin structure around the genomic regions bound by 119 human
Genome Res. 2012 Sep;22(9):1798-812.
PMID: 22955990; PMC: PMC3431495
Wang J, Zhuang J, Iyer S, Lin XY, Greven MC, Kim BH, Moore J, Pierce BG, Dong X, Virgil D et
Factorbook.org: a Wiki-based database for transcription factor-binding data generated by the ENCODE
Nucleic Acids Res. 2013 Jan;41(Database issue):D171-6.
PMID: 23203885; PMC: PMC3531197
Data Release Policy
While primary ENCODE data was subject to a restriction period as described in the
ENCODE data release policy, this restriction does not apply to the integrative
analysis results, and all primary data underlying this track have passed the restriction date.
The data in this track are freely available.