ENCODE Regulation TF Clusters Track Settings (2024)

Description

This track shows regions of transcription factor binding derived from a large collectionof 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 productionphase ("ENCODE 3").

Transcription factors (TFs) are proteins that bind to DNA and interact with RNA polymerases toregulate 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 immunoprecipitationfollowed by sequencing, or 'ChIP-seq') can be used to identify regions ofTF 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 theENCODE 3 TF ChIP Peaks tracks (hg19,hg38)

Display Conventions

A gray box encloses each peak cluster of transcription factor occupancy, with thedarkness of the box being proportional to the maximum signal strength observed in any cell typecontributing 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 thecell 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 designateENCODE 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 thecluster and the cell line abbreviation table.

Methods

Peaks of transcription factor occupancy ("optimal peak set") from ENCODE ChIP-seq datasetswere clustered using the UCSC hgBedsToBedExps tool. Scores were assigned to peaks by multiplying the input signal values by a normalizationfactor calculated as the ratio of the maximum score value (1000) to the signal value at onestandard deviation from the mean, with values exceeding 1000 capped at 1000. This has theeffect 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.

Data Access

The raw data for the ENCODE3 TF Clusters track can be accessed from theTable 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 our 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 thePublic SQL server.

Credits

Thanks to the ENCODE Consortium, the ENCODE ChIP-seq production laboratories, and theENCODE 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 theZLab ENCODE Public Hubsfor the most updated data.

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

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

Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS, Hitz BC, Gabdank I, Narayanan AK, Ho M, LeeBT 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 Yet al.Sequence features and chromatin structure around the genomic regions bound by 119 humantranscription 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 etal.Factorbook.org: a Wiki-based database for transcription factor-binding data generated by the ENCODEconsortium.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.