Schema for TF Clusters - Transcription Factor ChIP-seq Clusters (340 factors, 129 cell types) from ENCODE 3
  Database: hg38    Primary Table: encRegTfbsClustered    Row Count: 10,565,630   Data last updated: 2019-05-17
Format description: BED5+ with a count, list of sources, and list of source scores for combined data
fieldexampleSQL type description
bin 585smallint(5) unsigned Indexing field to speed chromosome range queries.
chrom chr1varchar(255) Reference sequence chromosome or scaffold
chromStart 9917int(10) unsigned Start position in chromosome
chromEnd 10247int(10) unsigned End position in chromosome
name NUFIP1varchar(255) Name of item
score 680int(10) unsigned Display score (0-1000)
sourceCount 1int(10) unsigned Number of sources
sourceIds 1063longblob Source ids
sourceScores 680longblob Source scores

Connected Tables and Joining Fields
        hg38.encRegTfbsClusteredInputs.factor (via (via (via (via encRegTfbsClustered.sourceIds)

Sample Rows

Note: all start coordinates in our database are 0-based, not 1-based. See explanation here.

TF Clusters (encRegTfbsClustered) Track Description


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 ( hg19, hg38)

Display Conventions

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.


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.

The integrative view presented here was developed by Jim Kent at UCSC.


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

Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS, Hitz BC, Gabdank I, Narayanan AK, Ho M, Lee BT et al. ENCODE data at the ENCODE portal. Nucleic Acids Res. 2016 Jan 4;44(D1):D726-32. PMID: 26527727; PMC: PMC4702836

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. PMID: 22955619

Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW, Greven MC, Pierce BG, Dong X, Kundaje A, Cheng Y et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. 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 al. a Wiki-based database for transcription factor-binding data generated by the ENCODE consortium. Nucleic Acids Res. 2013 Jan;41(Database issue):D171-6. PMID: 23203885; PMC: PMC3531197

Data Use Policy

Users may freely download, analyze and publish results based on any ENCODE data without restrictions. Researchers using unpublished ENCODE data are encouraged to contact the data producers to discuss possible coordinated publications; however, this is optional.

Users of ENCODE datasets are requested to cite the ENCODE Consortium and ENCODE production laboratory(s) that generated the datasets used, as described in Citing ENCODE.