CTCF (CCCTC-binding factor) is a multifunctional DNA-binding protein involved in chromatin
organization, transcriptional regulation, and insulation of regulatory elements. This track
displays genome-wide CTCF binding signal, as determined by CTCF ChIP-seq data across all
phases of the ENCODE project. CTCF plays a key role in establishing chromatin loops and
boundary elements that influence gene expression and higher-order genome architecture. CTCF
binding sites often occur at insulators and chromatin loop anchors, which help define
topologically associating domains (TADs) and mediate enhancer-promoter interactions. The data
are processed following the
ENCODE
transcription factor ChIP-seq pipeline. Additional transcription factor binding and
chromatin accessibility datasets are available at the
ENCODE portal.
For each organ, this track provides up to two subtracks averaging CTCF signal:
Tissue and Primary Cell averages only the tissue and primary cell experiments.
All Biosamples averages every experiment for that organ, including the
tissue/primary cell ones plus any from cell lines, in vitro differentiated cells, or
organoids.
Whether one or two subtracks appear for an organ depends on which kinds of biosamples have
been assayed:
Tissue/primary cell only. There are no cell line, in vitro differentiated cell,
or organoid experiments to include, so the two averages would be computed from the same
data and produce identical numbers. Only the Tissue and Primary Cell subtrack
is shown.
Cell line, in vitro differentiated cell, or organoid experiments only.
There are no tissue or primary cell experiments to average, so only the All Biosamples
subtrack is shown. In this case it represents those experiments.
Both kinds of biosamples available. The two averages give different numbers
because one covers only the tissue and primary cell experiments while the other includes everything together.
Both subtracks are shown.
Available Organs and Tissues
Organ/Tissue
Tissue and Primary Cell Subtrack
All Biosamples Subtrack
adipose
–
✓
adrenal gland
–
✓
blood
✓
✓
blood vessel
–
✓
bone
–
✓
bone marrow
–
✓
brain
✓
✓
breast
✓
✓
connective tissue
✓
✓
embryo
–
✓
epithelium
–
✓
esophagus
–
✓
eye
✓
✓
heart
✓
✓
kidney
✓
✓
large intestine
✓
✓
liver
✓
✓
lung
✓
✓
mouth
–
✓
muscle
✓
✓
nerve
–
✓
ovary
–
✓
pancreas
✓
✓
parathyroid gland
–
✓
penis
–
✓
placenta
–
✓
prostate
✓
✓
skin
✓
✓
small intestine
–
✓
spinal cord
–
✓
spleen
–
✓
stomach
–
✓
testis
–
✓
thyroid
–
✓
uterus
✓
✓
vagina
–
✓
Display Conventions and Configuration
By default, this track uses a transparent overlay to visualize data from multiple organs or tissues within
the same vertical space. For each organ or tissue, signals from all associated experiments were
averaged to generate the displayed track. Each organ or tissue is assigned a distinct
color following the
ENCODE color
mapping convention,
selected to be light and saturated to maintain clarity when overlaid. Initially, each layered
track displays an overlay of five representative organs: blood, brain, kidney, liver, and
muscle. Clicking on the track opens a details page where you can view and select organs or
tissues.
Data Access
The ENCODE 4 Regulation data on the UCSC Genome Browser can be explored interactively with the
Table Browser or the
Data Integrator.
For automated download and analysis, the genome annotation is stored in bigWig
files that can be downloaded from
our download server.
The data may also be explored interactively using our
REST API.
The original data files are also available from the
ENCODE portal.
These files may also be locally explored using our tool bigWigToWig,
which can be compiled from the source code or downloaded as a precompiled
binary for your system. Instructions for downloading source code and binaries can be found
here.
The tool can also be used to obtain data confined to a given range, e.g.,
Data were generated by the ENCODE Consortium. We thank the production labs for generating the
data: Drs. Bradley Bernstein (Broad), John Stamatoyannopoulos (UW),
Michael Snyder (Stanford), Richard Myers (HAIB), and Vishwanath Iyer (UTA). The data were
further processed for visualization through a collaborative effort between the
Weng lab and the
Moore lab
at UMass Chan Medical School (funded by NIH grant HG012343). Integration and visualization
were developed by Drs. Mingshi Gao, Jill Moore, and Zhiping Weng at UMass Chan Medical School,
who were part of the ENCODE Data Analysis Center.