This track displays biosample-specific candidate cis-regulatory elements (cCREs) alongside genome-wide epigenomic signals for the ENCODE Core Collection of 18 ENCODE biosamples. Each biosample contains five subtracks:
Additional epigenomic datasets are available at the ENCODE portal, and further exploration of cCREs and their supporting data is available through the SCREEN web tool, accessible via the track details page.
Click a specific biosample type and organ/tissue combination to view available datasets. Signal subtracks can be further filtered by the signal type.
Each cCRE in the biosample-specific cCREs subtrack is color-coded by its classification type:

Mousing over an item displays the element ID with a linkout to SCREEN, the cCRE class, and the biosample-specific Z-scores for DNase, H3K4me3, H3K27ac, and CTCF. A Z-score above 1.64 is considered "high" signal, while a score of 1.64 or below is considered "low" signal for classification purposes (Moore et al., 2026).
The DNase-seq data were processed using the ENCODE DNase-seq pipeline, the H3K4me3 and H3K27ac ChIP-seq data were processed using the ENCODE histone ChIP-seq pipeline, and the CTCF ChIP-seq data were processed using the ENCODE transcription factor ChIP-seq pipeline.
In addition to the cell type-agnostic classification (described in the cCRE Registry track in this collection), the biochemical activity of each cCRE was evaluated in individual biosamples using the corresponding biosample-specific DNase, H3K4me3, H3K27ac, and CTCF data. Active cCREs in each biosample are displayed in the cCRE subtrack. cCREs with low DNase Z-scores in a given biosample are considered inactive and labeled "Low-DNase."
The Core Collection includes only the four core assays standardized across all 18 biosamples: DNase, H3K4me3, H3K27ac, and CTCF. ATAC-seq signal tracks for select biosamples are available in the ENCODE4 Regulation track.
All data is available from the ENCODE data portal.
The 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 at UCSC in bigWig and bigBed files that can be downloaded from our download server.
The cCREs tracks in this data are found as bigBed files, and the biosignal tracks as bigWig files.
See the Data format link besides the specific data track for a URL to the file on our download
server. Individual
regions or the whole genome annotation can be obtained using our tools bigWigToWig
or bigBedToBed 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 tools can also be used to obtain features confined to a given range, e.g.,
bigWigToBedGraph -chrom=chr1 -start=100000 -end=100500 https://hgdownload.soe.ucsc.edu/gbdb/mm10/encode4/ccre/coreCollection/ENCFF325FZS.bw stdout
or
bigBedToBed -chrom=chr1 -start=100000 -end=100500 https://hgdownload.soe.ucsc.edu/gbdb/mm10/encode4/ccre/coreCollection/ENCFF325FZS_ENCFF396LHC_ENCFF010WPI_ENCFF196GIY.bb stdout
Data were generated by the ENCODE Consortium. We thank the production labs for generating the data: Drs. Bing Ren (UCSD), John Stamatoyannopoulos (UW), Michael Snyder (Stanford), Richard Myers (HAIB). 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.
ENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, Kawli T, Davis CA, Dobin A et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature. 2020 Jul;583(7818):699-710. PMID: 32728249; PMC: PMC7410828
Moore JE, Pratt HE, Fan K, Phalke N, Fisher J, Elhajjajy SI, Andrews G, Gao M, Shedd N, Fu Y et al. An Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional Regulation. Nature. 2026 January 7. PMID: 39763870; PMC: PMC11703161