Uniform TFBS Track Settings
 
Transcription Factor ChIP-seq Uniform Peaks from ENCODE/Analysis   (ENC TF Binding)

This track is part of a parent called 'ENC TF Binding'. To show other tracks of this parent, go to the ENC TF Binding configuration page.

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Score range: min: (0 to 1000)
Select subtracks by cell line and factor: (help)
 All
Cell Line
GM12878 (Tier 1)
H1-hESC (Tier 1)
K562 (Tier 1)
HeLa-S3 (Tier 2)
HepG2 (Tier 2)
HUVEC (Tier 2)
IMR90 (Tier 2*)
A549 (Tier 2*)
MCF-7 (Tier 2*)
SK-N-SH (Tier 2*)
AG04449
AG04450
AG09309
AG09319
AG10803
AoAF
BE2 C
BJ
Caco-2
Dnd41
ECC-1
Fibrobl
Gliobla
GM06990
GM08714
GM10847
GM12801
GM12864
GM12865
GM12872
GM12873
GM12874
GM12875
GM12891
GM12892
GM15510
GM18505
GM18526
GM18951
GM19099
GM19193
GM19238
GM19239
GM19240
HAc
HA-sp
HBMEC
HCFaa
HCM
HCPEpiC
HCT-116
HEEpiC
HEK293
HEK293-T-REx
HFF
HFF-Myc
HL-60
HMEC
HMF
HPAF
HPF
HRE
HRPEpiC
HSMM
HSMMtube
HVMF
MCF10A-Er-Src
NB4
NH-A
NHDF-Ad
NHDF-neo
NHEK
NHLF
NT2-D1
Osteobl
PANC-1
PBDE
PBDEFetal
PFSK-1
ProgFib
Raji
RPTEC
SAEC
SH-SY5Y
SK-N-MC
SK-N-SH RA
T-47D
U2OS
U87
WERI-Rb-1
WI-38
Cell Line
All 
Factor


























































































Factor
ARID3A   ARID3A
ATF1   ATF1
ATF2   ATF2
ATF3   ATF3
BACH1   BACH1
BATF   BATF
BCL11A   BCL11A
BCL3   BCL3
BCLAF1   BCLAF1
BDP1   BDP1
BHLHE40   BHLHE40
BRCA1   BRCA1
BRF1   BRF1
BRF2   BRF2
CBX3   CBX3
CCNT2   CCNT2
CEBPB   CEBPB
CEBPD   CEBPD
CHD1   CHD1
CHD2   CHD2
CREB1   CREB1
CTBP2   CTBP2
CTCF   CTCF
CTCFL   CTCFL
E2F1   E2F1
E2F4   E2F4
E2F6   E2F6
EBF1   EBF1
EGR1   EGR1
ELF1   ELF1
ELK1   ELK1
ELK4   ELK4
EP300   EP300
ESR1   ESR1
ESRRA   ESRRA
ETS1   ETS1
EZH2   EZH2
FAM48A   FAM48A
FOS   FOS
FOSL1   FOSL1
FOSL2   FOSL2
FOXA1   FOXA1
FOXA2   FOXA2
FOXM1   FOXM1
FOXP2   FOXP2
GABPA   GABPA
GATA1   GATA1
GATA2   GATA2
GATA3   GATA3
GRp20   GRp20
GTF2B   GTF2B
GTF2F1   GTF2F1
GTF3C2   GTF3C2
HDAC1   HDAC1
HDAC2   HDAC2
HDAC6   HDAC6
HDAC8   HDAC8
HMGN3   HMGN3
HNF4A   HNF4A
HNF4G   HNF4G
HSF1   HSF1
IKZF1   IKZF1
IRF1   IRF1
IRF3   IRF3
IRF4   IRF4
JUN   JUN
JUNB   JUNB
JUND   JUND
KAP1   KAP1
KDM5A   KDM5A
KDM5B   KDM5B
MAFF   MAFF
MAFK   MAFK
MAX   MAX
MAZ   MAZ
MBD4   MBD4
MEF2A   MEF2A
MEF2C   MEF2C
MTA3   MTA3
MXI1   MXI1
MYBL2   MYBL2
MYC   MYC
NANOG   NANOG
NFATC1   NFATC1
NFE2   NFE2
NFIC   NFIC
NFYA   NFYA
NFYB   NFYB
NR2C2   NR2C2
NR2F2   NR2F2
NR3C1   NR3C1
NRF1   NRF1
PAX5   PAX5
PBX3   PBX3
PHF8   PHF8
PML   PML
POLR2A   POLR2A
POLR3G   POLR3G
POU2F2   POU2F2
POU5F1   POU5F1
PPARGC1A   PPARGC1A
PRDM1   PRDM1
RAD21   RAD21
RBBP5   RBBP5
RCOR1   RCOR1
RDBP   RDBP
RELA   RELA
REST   REST
RFX5   RFX5
RPC155   RPC155
RUNX3   RUNX3
RXRA   RXRA
SAP30   SAP30
SETDB1   SETDB1
SIN3A   SIN3A
SIN3AK20   SIN3AK20
SIRT6   SIRT6
SIX5   SIX5
SMARCA4   SMARCA4
SMARCB1   SMARCB1
SMARCC1   SMARCC1
SMARCC2   SMARCC2
SMC3   SMC3
SP1   SP1
SP2   SP2
SP4   SP4
SPI1   SPI1
SREBP1   SREBP1
SRF   SRF
STAT1   STAT1
STAT2   STAT2
STAT3   STAT3
STAT5A   STAT5A
SUZ12   SUZ12
TAF1   TAF1
TAF7   TAF7
TAL1   TAL1
TBL1XR1   TBL1XR1
TBP   TBP
TCF12   TCF12
TCF3   TCF3
TCF7L2   TCF7L2
TEAD4   TEAD4
TFAP2A   TFAP2A
TFAP2C   TFAP2C
THAP1   THAP1
TRIM28   TRIM28
UBTF   UBTF
USF1   USF1
USF2   USF2
WRNIP1   WRNIP1
YY1   YY1
ZBTB33   ZBTB33
ZBTB7A   ZBTB7A
ZEB1   ZEB1
ZKSCAN1   ZKSCAN1
ZNF143   ZNF143
ZNF217   ZNF217
ZNF263   ZNF263
ZNF274   ZNF274
ZZZ3   ZZZ3
Factor


























































































Factor
 All
Cell Line
GM12878 (Tier 1)
H1-hESC (Tier 1)
K562 (Tier 1)
HeLa-S3 (Tier 2)
HepG2 (Tier 2)
HUVEC (Tier 2)
IMR90 (Tier 2*)
A549 (Tier 2*)
MCF-7 (Tier 2*)
SK-N-SH (Tier 2*)
AG04449
AG04450
AG09309
AG09319
AG10803
AoAF
BE2 C
BJ
Caco-2
Dnd41
ECC-1
Fibrobl
Gliobla
GM06990
GM08714
GM10847
GM12801
GM12864
GM12865
GM12872
GM12873
GM12874
GM12875
GM12891
GM12892
GM15510
GM18505
GM18526
GM18951
GM19099
GM19193
GM19238
GM19239
GM19240
HAc
HA-sp
HBMEC
HCFaa
HCM
HCPEpiC
HCT-116
HEEpiC
HEK293
HEK293-T-REx
HFF
HFF-Myc
HL-60
HMEC
HMF
HPAF
HPF
HRE
HRPEpiC
HSMM
HSMMtube
HVMF
MCF10A-Er-Src
NB4
NH-A
NHDF-Ad
NHDF-neo
NHEK
NHLF
NT2-D1
Osteobl
PANC-1
PBDE
PBDEFetal
PFSK-1
ProgFib
Raji
RPTEC
SAEC
SH-SY5Y
SK-N-MC
SK-N-SH RA
T-47D
U2OS
U87
WERI-Rb-1
WI-38
Cell Line
All 
List subtracks: only selected/visible    all    ()
  Tier↓1 Cell Line↓2 Factor↓3 Lab↓4   Track Name↓5  
 
dense
 1  GM12878  CTCF  UT-A  GM12878 TFBS Uniform Peaks of CTCF from ENCODE/UT-A/Analysis    schema 
 
dense
 1  GM12878  POLR2A  HudsonAlpha  GM12878 TFBS Uniform Peaks of Pol2-4H8 from ENCODE/HudsonAlpha/Analysis    schema 
 
dense
 1  GM12878  SP1  HudsonAlpha  GM12878 TFBS Uniform Peaks of SP1 from ENCODE/HudsonAlpha/Analysis    schema 
 
dense
 1  H1-hESC  CTCF  UT-A  H1-hESC TFBS Uniform Peaks of CTCF from ENCODE/UT-A/Analysis    schema 
 
dense
 1  H1-hESC  NANOG  HudsonAlpha  H1-hESC TFBS Uniform Peaks of NANOG_(SC-33759) from ENCODE/HudsonAlpha/Analysis    schema 
 
dense
 1  H1-hESC  POLR2A  HudsonAlpha  H1-hESC TFBS Uniform Peaks of Pol2-4H8 from ENCODE/HudsonAlpha/Analysis    schema 
 
dense
 1  K562  CTCF  UT-A  K562 TFBS Uniform Peaks of CTCF from ENCODE/UT-A/Analysis    schema 
 
dense
 1  K562  NFYA  Stanford  K562 TFBS Uniform Peaks of NF-YA from ENCODE/Stanford/Analysis    schema 
 
dense
 1  K562  POLR2A  HudsonAlpha  K562 TFBS Uniform Peaks of Pol2-4H8 from ENCODE/HudsonAlpha/Analysis    schema 
 
dense
 2  HeLa-S3  CTCF  UT-A  HeLa-S3 TFBS Uniform Peaks of CTCF from ENCODE/UT-A/Analysis    schema 
 
dense
 2  HeLa-S3  E2F1  USC  HeLa-S3 TFBS Uniform Peaks of HA-E2F1 from ENCODE/USC/Analysis    schema 
 
dense
 2  HeLa-S3  POLR2A  HudsonAlpha  HeLa-S3 TFBS Uniform Peaks of Pol2 from ENCODE/HudsonAlpha/Analysis    schema 
 
dense
 2  HepG2  CTCF  UT-A  HepG2 TFBS Uniform Peaks of CTCF from ENCODE/UT-A/Analysis    schema 
 
dense
 2  HepG2  EP300  Stanford  HepG2 TFBS Uniform Peaks of p300_(SC-584) from ENCODE/Stanford/Analysis    schema 
 
dense
 2  HepG2  POLR2A  HudsonAlpha  HepG2 TFBS Uniform Peaks of Pol2-4H8 from ENCODE/HudsonAlpha/Analysis    schema 
 
dense
 2  HUVEC  CTCF  UT-A  HUVEC TFBS Uniform Peaks of CTCF from ENCODE/UT-A/Analysis    schema 
 
dense
 2  HUVEC  FOS  USC  HUVEC TFBS Uniform Peaks of c-Fos from ENCODE/USC/Analysis    schema 
 
dense
 2  HUVEC  POLR2A  HudsonAlpha  HUVEC TFBS Uniform Peaks of Pol2-4H8 from ENCODE/HudsonAlpha/Analysis    schema 
 
dense
 2*  A549  CTCF  HudsonAlpha  A549 (DEX_100nM) TFBS Uniform Peaks of CTCF_(SC-5916) ENCODE/HudsonAlph/Analysis    schema 
 
dense
 2*  A549  NR3C1  HudsonAlpha  A549 (DEX_100nM) TFBS Uniform Peaks of GR from ENCODE/HudsonAlpha/Analysis    schema 
 
dense
 2*  IMR90  CEBPB  Stanford  IMR90 TFBS Uniform Peaks of CEBPB from ENCODE/Stanford/Analysis    schema 
 
dense
 2*  IMR90  CTCF  Stanford  IMR90 TFBS Uniform Peaks of CTCF_(SC-15914) from ENCODE/Stanford/Analysis    schema 
 
dense
 2*  IMR90  POLR2A  Stanford  IMR90 TFBS Uniform Peaks of Pol2 from ENCODE/Stanford/Analysis    schema 
 
dense
 2*  MCF-7  CTCF  UT-A  MCF-7 (serum_stimulated) TFBS Uniform Peaks of CTCF from ENCODE/UT-A/Analysis    schema 
 
dense
 2*  MCF-7  MYC  UT-A  MCF-7 (serum_stimulated) TFBS Uniform Peaks of c-Myc from ENCODE/UT-A/Analysis    schema 
 
dense
 2*  MCF-7  POLR2A  UT-A  MCF-7 (serum_stimulated) TFBS Uniform Peaks of Pol2 from ENCODE/UT-A/Analysis    schema 
    
Downloads
Data version: ENCODE March 2012 Freeze

Description

This track represents a comprehensive set of human transcription factor binding sites based on ChIP-seq experiments generated by production groups in the ENCODE Consortium from the inception of the project in September 2007, through the March 2012 internal data freeze. The track represents peak calls (regions of enrichment) that were generated by the ENCODE Analysis Working Group (AWG) based on a uniform processing pipeline developed for the ENCODE Integrative Analysis effort and published in a set of coordinated papers in September 2012. Peak calls from that effort, based on datasets from the January 2011 ENCODE data freeze) are available at the ENCODE Analysis Data Hub. This track is an update that includes newer data, and slightly modified methods for the peak calling.

This track contains 690 ChIP-seq datasets representing 161 unique regulatory factors (generic and sequence-specific factors). The datasets span 91 human cell types and some are in various treatment conditions. These datasets were generated by the five ENCODE TFBS ChIP-seq production groups: Broad, Stanford/Yale/UC-Davis/Harvard, HudsonAlpha Institute, University of Texas-Austin and University of Washington, and University of Chicago. The University of Chicago ChIP-seq were performed with an alternative epitope-tagged ChIP-seq methodology. The primary and lab-processed data (along with methods descriptions, credits and references) on which this track is based are available in the following ENCODE tracks: HAIB TFBS, SYDH TFBS, UChicago TFBS, UTA TFBS, UW CTCF Binding. These tracks are accessible from the ENC TF Binding Super-track.

Display and File Conventions and Configuration

The display for this track shows site location with the point-source of the peak marked with a colored vertical bar and the level of enrichment at the site indicated by the darkness of the item. The display can be filtered to higher valued items, using the Score range: configuration item. The score values were computed at UCSC based on signal values assigned by the ENCODE uniform analysis pipeline. The input signal values were multiplied by a normalization factor calculated as the ratio of the maximum score value (1000) to the signal value at 1 standard deviation from the mean, with values exceeding 1000 capped at 1000. This has the effect of distributing scores up to mean + 1std across the score range, but assigning all above to the maximum score.

This track is a composite annotation track containing multiple subtracks, one for each cell type. The display mode and filtering of each subtrack can be individually controlled. For more information about track configuration, see Configuring Multi-View Tracks. Metadata for a particular subtrack can be found by clicking the down arrow in the list of subtracks. The UCSC Accession listed in the metadata can be used with the File Search tool to retrieve primary data files underlying datasets of interest, by selecting UCSC Accession from the "ENCODE terms" drop down menu option.

In the subtrack selection list, the ENCODE tier (priority) is listed for each cell type. Tier 1 and Tier 2 represent categories with cell types designated for intensive study by the ENCODE investigators. After the January 2011 data freeze, an additional set of cell types were promoted from Tier 3 to Tier 2 to broaden the list of intensively studied cell types. These cell types are listed as Tier 2* in the subtrack list here (and are described as 'newly promoted to tier 2: not in 2011 analysis' on the ENCODE Common Cell Types page).

Download files for this track are in ENCODE NarrowPeak format.

Methods

All ChIP-seq experiments were performed at least in duplicate, and were scored against an appropriate control designated by the production groups (either input DNA or DNA obtained from a control immunoprecipitation).

Short Read Mapping

For each dataset, mapped reads in the form of BAM files were downloaded from the ENCODE UCSC DCC. These BAM files were generated by the ENCODE data production labs (using different mappers and mapping parameters), but all used a standardized version of the GRCh37 (hg19) reference human genome sequence with the following modifications:

  • Mitochondrial sequence was included.
  • Alternate sequences were excluded.
  • Random contigs were excluded.
  • The female version of the genome was represented by the autosomes and chrX, whereas the male genome was represented by the autosomes, chrX, and chrY with the PAR regions masked.

In order to standardize the mapping protocol, custom unique-mappability tracks were used to only retain unique mapping reads, i.e. reads that map to exactly one location in the genome. Positional and PCR duplicates were also filtered out.

Quality Control

A number of quality metrics for individual replicates listed on the ENCODE portal Quality Metrics page, including measures of library complexity and signal enrichment, were calculated and are available for review (Landt et al., 2012; Kundaje et al., 2013a). The Integrated Quality Flag from this quality assessment was used to assign the quality metadata term for each dataset (e.g., Good vs. Caution). Datasets that did not pass the minimum quality control thresholds are not included in this track.

Peak Calling

Since every ENCODE dataset is represented by at least two biological replicate experiments, a novel measure of consistency and reproducibility of peak calling results between replicates, known as the Irreproducible Discovery Rate (IDR), was used to determine an optimal number of reproducible peaks (Li et al., 2011; Kundaje et al., 2013b). Code and detailed step-by-step instructions to call peaks using the IDR method are available. In brief, the SPP peak caller (Kharchenko et al., 2008) was used with a relaxed peak calling threshold (FDR = 0.9) to obtain a large number of peaks (maximum of 300K) that span true signal as well as noise (false identifications). The IDR method analyzes a pair of replicates, and considers peaks that are present in both replicates to belong to one of two populations : a reproducible signal group or an irreproducible noise group. Peaks from the reproducible group are expected to show relatively higher ranks (ranked based on signal scores) and stronger rank-consistency across the replicates, relative to peaks in the irreproducible groups. Based on these assumptions, a two-component probabilistic copula-mixture model is used to fit the bivariate peak rank distributions from the pairs of replicates. The method adaptively learns the degree of peak-rank consistency in the signal component and the proportion of peaks belonging to each component. The model can then be used to infer an IDR score for every peak that is found in both replicates. The IDR score of a peak represents the expected probability that the peak belongs to the noise component, and is based on its ranks in the two replicates. Hence, low IDR scores represent high-confidence peaks. An IDR score threshold of 0.02 (2%) was used to obtain an optimal peak rank threshold on the replicate peak sets (cross-replicate threshold). If a dataset had more than two replicates, all pairs of replicates were analyzed using the IDR method. The maximum peak rank threshold across all pairwise analyses was used as the final cross-replicate peak rank threshold. Reads from replicate datasets were then pooled and SPP was once again used to call peaks on the pooled data with a relaxed FDR of 0.9. Pooled-data peaks were once again ranked by signal-score. The cross-replicate rank threshold learned from the replicates was used to threshold the ranked set of pooled-data peaks.

Any thresholds based on reproducibility of peak calling between biological replicates are bounded by the quality and enrichment of the worst replicate. Valuable signal is lost in cases for which a dataset has one replicate that is significantly worse in data quality than another replicate. A rescue pipeline was used for such cases in order to balance data quality between a set of replicates. Mapped reads were pooled across all replicates of a dataset, and then randomly sampled (without replacement) to generate two pseudo-replicates with equal numbers of reads. This sampling strategy tends to transfer signal from stronger replicates to the weaker replicates, thereby balancing cross-replicate data quality and sequencing depth. These pseudo-replicates were then processed using the IDR method in order to learn a rescue threshold. For datasets with comparable replicates (based on independent measures of data quality), the rescue threshold and cross-replicate thresholds were found to be very similar. However, for datasets with replicates of differing data quality, the rescue thresholds were often higher than the cross-replicate thresholds, and were able to capture true peaks that showed statistically significant and visually compelling ChIP-seq signal in one replicate but not in the other. Ultimately, for each dataset, the best of the cross-replicate and rescue thresholds were used to obtain a final consolidated optimal set of peaks.

All peak sets were then screened against a specially curated empirical blacklist of regions in the human genome (wgEncodeDacMapabilityConsensusExcludable.bed.gz) and peaks overlapping the blacklisted regions were discarded (Kundaje et al., 2013b). Briefly, these artifact regions typically show the following characteristics:

  • Unstructured and extreme artifactual high signal in sequenced input-DNA and control datasets, as well as open chromatin datasets irrespective of cell type identity.
  • An extreme ratio of multi-mapping to unique mapping reads from sequencing experiments.
  • Overlap with pathological repeat regions such as centromeric, telomeric and satellite repeats that often have few unique mappable locations interspersed in repeats.

Differences from the January 2011 freeze pipeline

The January 2011 uniform processing was performed as part of the ENCODE Integrative Analysis reported in coordinated publications in September 2012. The results from this effort are available from the ENCODE Analysis Hub at the EBI.

  • For the March 2012 freeze, only the SPP peak caller was used. SPP and PeakSeq were used for the January 2011 freeze.
  • For March 2012, In the read mapping phase, an extra step was performed to remove all positional duplicates. This was done to avoid low library complexity issues. In January 2011, remove positional duplicates were retained.
  • For March 2012, an IDR threshold of 2% was used for comparing and thresholding the true replicates and the pooled pseudo-replicates. In January 2011, the IDR threshold was set to 1% for the true replicates and 0.25% for the pooled pseudo-replicates. These thresholds were determined to be too stringent.

Credits

The processed data for this track were generated by Anshul Kundaje on behalf of the ENCODE Analysis Working Group. Credits for the primary data underlying this track are included in track description pages listed in the Description section above.

Contact: Anshul Kundaje

References

ENCODE Project Consortium. A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 2011 Apr;9(4):e1001046. PMID: 21526222; PMCID: PMC3079585

ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012 Sep 6;489(7414):57-74. PMID: 22955616; PMCID: PMC3439153

Kharchenko PV, Tolstorukov MY, Park PJ. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol. 2008 Dec;26(12):1351-9. PMID: 19029915; PMCID: PMC2597701

Kundaje A, Jung L, Kharchenko PV, Sidow A, Batzoglou S, Park PJ. Assessment of ChIP-seq data quality using strand cross-correlation analysis. (submitted), 2012a.

Kundaje A, Li Q, Brown JB, Rozowsky J, Harmanci A, Wilder SP, Batzoglou S, Dunham I, Gerstein M, Birney E, et al. Reproducibility measures for automatic threshold selection and quality control in ChIP-seq datasets. (submitted), 2012b.

Li QH, Brown JB, Huang HY, Bickel PJ. Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat. 2011; 5(3):1752-1779.

Data Release Policy

While primary ENCODE data is subject to a restriction period as described in the ENCODE data release policy, this restriction does not apply to the integrative analysis results. The data in this track are freely available.