PSU Histone Track Settings
 
Histone Modifications by ChIP-seq from ENCODE/PSU   (All Expression and Regulation tracks)

Maximum display mode:       Reset to defaults   
Select views (Help):
Peaks ▾       Signal ▾      
Select subtracks by cell line and factor:
 All Cell Line CH12  Erythroblast  G1E  G1E-ER4  Megakaryocyte 
Factor
H3K4me1 
H3K4me3 
H3K9me3 
H3K27me3 
H3K36me3 
Input 
List subtracks: only selected/visible    all    ()
  Cell Line↓1 Treatment↓2 Factor↓3 views↓4   Track Name↓5    Restricted Until↓6
 
hide
 Configure
 CH12      H3K4me1  Peaks  CH12 H3K4me1 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-07-31 
 
hide
 Configure
 CH12      H3K4me1  Signal  CH12 H3K4me1 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-07-31 
 
hide
 Configure
 CH12      H3K4me3  Peaks  CH12 H3K4me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-07-28 
 
hide
 Configure
 CH12      H3K4me3  Signal  CH12 H3K4me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-07-28 
 
hide
 Configure
 CH12      H3K9me3  Peaks  CH12 H3K9me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-08-11 
 
hide
 Configure
 CH12      H3K9me3  Signal  CH12 H3K9me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-08-11 
 
hide
 Configure
 CH12      H3K27me3  Peaks  CH12 H3K27me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-06-23 
 
hide
 Configure
 CH12      H3K27me3  Signal  CH12 H3K27me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-06-23 
 
hide
 Configure
 CH12      H3K36me3  Peaks  CH12 H3K36me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-07-24 
 
hide
 Configure
 CH12      H3K36me3  Signal  CH12 H3K36me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-07-24 
 
hide
 Configure
 Erythroblast      H3K4me1  Peaks  Erythroblast H3K4me1 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-08-15 
 
hide
 Configure
 Erythroblast      H3K4me1  Signal  Erythroblast H3K4me1 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-08-15 
 
hide
 Configure
 Erythroblast      H3K4me3  Peaks  Erythroblast H3K4me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-08-15 
 
hide
 Configure
 Erythroblast      H3K4me3  Signal  Erythroblast H3K4me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-08-15 
 
hide
 Configure
 Erythroblast      H3K9me3  Peaks  Erythroblast H3K9me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-08-11 
 
hide
 Configure
 Erythroblast      H3K9me3  Signal  Erythroblast H3K9me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-08-11 
 
hide
 Configure
 Erythroblast      H3K27me3  Peaks  Erythroblast H3K27me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-08-07 
 
hide
 Configure
 Erythroblast      H3K27me3  Signal  Erythroblast H3K27me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-08-07 
 
hide
 Configure
 Erythroblast      H3K36me3  Peaks  Erythroblast H3K36me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-08-15 
 
hide
 Configure
 Erythroblast      H3K36me3  Signal  Erythroblast H3K36me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-08-15 
 
hide
 Configure
 Megakaryocyte      H3K4me1  Peaks  Megakaryocyte H3K4me1 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-07-30 
 
hide
 Configure
 Megakaryocyte      H3K4me1  Signal  Megakaryocyte H3K4me1 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-07-30 
 
hide
 Configure
 Megakaryocyte      H3K4me3  Peaks  Megakaryocyte H3K4me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-07-28 
 
hide
 Configure
 Megakaryocyte      H3K4me3  Signal  Megakaryocyte H3K4me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-07-28 
 
hide
 Configure
 Megakaryocyte      H3K9me3  Peaks  Megakaryocyte H3K9me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-08-14 
 
hide
 Configure
 Megakaryocyte      H3K9me3  Signal  Megakaryocyte H3K9me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-08-14 
 
hide
 Configure
 Megakaryocyte      H3K27me3  Peaks  Megakaryocyte H3K27me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-08-17 
 
hide
 Configure
 Megakaryocyte      H3K27me3  Signal  Megakaryocyte H3K27me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-08-17 
 
hide
 Configure
 Megakaryocyte      H3K36me3  Peaks  Megakaryocyte H3K36me3 Histone Mods by ChIP-seq Peaks from ENCODE/PSU    Schema   2012-08-15 
 
hide
 Configure
 Megakaryocyte      H3K36me3  Signal  Megakaryocyte H3K36me3 Histone Mods by ChIP-seq Signal from ENCODE/PSU    Schema   2012-08-15 
     Restriction Policy
Downloads

Description

Rationale for the Mouse ENCODE project
Knowledge of the function of genomic DNA sequences comes from three basic approaches. Genetics uses changes in behavior or structure of a cell or organism in response to changes in DNA sequence to infer function of the altered sequence. Biochemical approaches monitor states of histone modification, binding of specific transcription factors, accessibility to DNases and other epigenetic features along genomic DNA. In general, these are associated with gene activity, but the precise relationships remain to be established. The third approach is evolutionary, using comparisons among homologous DNA sequences to find segments that are evolving more slowly or more rapidly than expected given the local rate of neutral change. These are inferred to be under negative or positive selection, respectively, and interpreted as DNA sequences needed for a preserved (negative selection) or adaptive (positive selection) function.

The ENCODE project aims to discover all the DNA sequences associated with various epigenetic features, with the reasonable expectation that these will also be functional (best tested by genetic methods). However, it is not clear how to relate these results with those from evolutionary analyses. The mouse ENCODE project aims to make this connection explicitly and with a moderate breadth. Assays identical to those being used in the ENCODE project are performed in cell types in mouse that are similar or homologous to those studied in the human project. The comparison will be used to discover which epigenetic features are conserved between mouse and human, and examine the extent to which these overlap with the DNA sequences under negative selection. The contribution of functional DNA preserved in mammals versus function in only one species will be discovered. The results will have a significant impact on the understanding of the evolution of gene regulation.

Maps of histone modifications
Levels of five histone modifications were determined. Methylation of lysine 4 of histone H3 is associated with active chromatin, with monomethylation (H3K4me1) associated with enhancers and trimethylation (H3K4me3) associated with active promoters. Trimethylation of lysine 36 (H3K36me3) is associated with elongating RNA polymerase II. Two marks associated with repressed chromatin were also determined, trimethylation of lysine 27 of histone H3 (H3K27me3) which is deposited by Polycomb repressive complex 2, and trimethylation of lysine 9 of histone H3 (H3K9me3). Maps of genomic DNA in chromatin with these histone modifications are generated by ChIP-seq. This consists of two basic steps: chromatin immunoprecipitation (ChIP) is used to highly enrich genomic DNA for the segments bound by specific proteins (the antigens recognized by the antibodies) followed by massively parallel short read sequencing to tag the enriched DNA segments. Sequencing was done on the Illumina GAIIx and HiSeq. The sequence tags are mapped back to the mouse genome ((Langmead et al., 2009)), and a graph of the enrichment for histone modifications are displayed as the "Signal" track (essentially the counts of mapped reads per interval) and the deduced probable binding sites from the MACS program ((Zhang et al., 2008)) are shown in the "Peaks" track. Each experiment is associated with an input signal, which represents the control condition where immunoprecipitation with non-specific immunoglobulin was performed in the same cell type. The sequence reads, quality scores, and alignment coordinates from these experiments are available for download.

Display Conventions and Configuration

This track is a multi-view composite track that contains multiple data types (views). For each view, there are multiple subtracks that display individually on the browser. Instructions for configuring multi-view tracks are here. This track contains the following views:

Peaks
Regions of signal enrichment based on processed data (usually normalized data from pooled replicates). Intensity is represented in grayscale; the darker shading shows higher intensity (a solid vertical line in the peak region represents the point with the highest signal). ENCODE Peaks tables contain fields for statistical significance, including FDR (qValue).
Signal
Density graph (wiggle) of signal enrichment based on processed data.

Metadata for a particular subtrack can be found by clicking the down arrow in the list of subtracks.

Methods

Cells were grown according to the approved ENCODE cell culture protocols.

The chromatin immunoprecipitation followed published methods (Welch et al., 2004). Information on antibodies used is available via the hyperlinks in the "Select subtracks" menu. Samples passing initial quality thresholds (enrichment and depletion for positive and negative controls - if available - by quantitative PCR of ChIP material) are processed for library construction for Illumina sequencing, using the ChIP-seq Sample Preparation Kit purchased from Illumina. Starting with a 10 ng sample of ChIP DNA, DNA fragments were repaired to generate blunt ends and a single A nucleotide was added to each end. Double-stranded Illumina adaptors were ligated to the fragments. Ligation products were amplified by 18 cycles of PCR, and the DNA between 250-350 bp was gel purified. Completed libraries were quantified with Quant-iT dsDNA HS Assay Kit. The DNA library was sequenced on the Illumina Genome Analyzer II sequencing system, and more recently on the HiSeq. Cluster generation, linearization, blocking and sequencing primer reagents were provided in the Illumina Cluster Amplification kits. All samples were determined as biological replicates except time course samples. The data displayed are from the pooled reads for all replicates, but individual replicates are available by download.

The resulting 36-nucleotide sequence reads (fastq files) were moved to a data library in Galaxy, and the tools implemented in Galaxy were used for further processing via workflows (Blankenberg et al., 2010). The reads were mapped to the mouse genome (mm9 assembly) using the program bowtie (Langmead et al., 2009), and the files of mapped reads for the ChIP sample and from the "input" control (no antibody) were processed by MACs (Zhang et al., 2008) to call peaks for occupancy by transcription factors, using the parameters mfold=15, bandwidth=125. Since, the signal for some histone modifications is not expected to be tightly localized (compared to a transcription factor), peak calling programs may not be appropriate. Thus in addition, we provide wiggle tracks with tag counts for every 10 bp segment. Per-replicate alignments and sequences are available for download at downloads page.

Release Notes

This is Release 2 (August 2012). It contains a total of 30 ChIP-seq experiments on Histone Modifications with the addition of 1 new experiment.

Previous versions of files are available for download from the FTP site.

Credits

Cell growth, ChIP, and Illumina library construction were done primarily by Weisheng Wu, and sequencing on the Illumina platform was done largely by Cheryl Keller in the laboratory of Ross Hardison (PSU). Data processing and analysis were overseen by James Taylor (Emory University), using tools provided in the Galaxy platform (Anton Nekrutenko, PSU, and James Taylor, Emory) enabled by the Penn State Cyberstar computer (supported by the National Science Foundation). Generation of these data was supported by National Institutes of Health grants R01DK065806 and RC2HG005573.

Contact: Ross Hardison

References

Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, Fry B, Meissner A, Wernig M, Plath K et al. A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell. 2006 Apr 21;125(2):315-26.

Blankenberg D, Gordon A, Von Kuster G, Coraor N, Taylor J, Nekrutenko A, Galaxy Team. Manipulation of FASTQ data with Galaxy. Bioinformatics. 2010 Jul 15;26(14):1783-5.

ENCODE Project Consortium, Birney E, Stamatoyannopoulos JA, Dutta A, Guigó R, Gingeras TR, Margulies EH, Weng Z, Snyder M, Dermitzakis ET et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 2007 Jun 14;447(7146):799-816.

Heintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins RD, Barrera LO, Van Calcar S, Qu C, Ching KA et al. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat Genet. 2007 Mar;39(3):311-8.

Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25.

Müller J, Hart CM, Francis NJ, Vargas ML, Sengupta A, Wild B, Miller EL, O'Connor MB, Kingston RE, Simon JA. Histone methyltransferase activity of a Drosophila Polycomb group repressor complex. Cell. 2002 Oct 18;111(2):197-208.

Rando OJ, Chang HY. Genome-wide views of chromatin structure. Annu Rev Biochem. 2009;78:245-71.

Weiss MJ, Yu C, Orkin SH. Erythroid-cell-specific properties of transcription factor GATA-1 revealed by phenotypic rescue of a gene-targeted cell line. Mol Cell Biol. 1997 Mar;17(3):1642-51.

Welch JJ, Watts JA, Vakoc CR, Yao Y, Wang H, Hardison RC, Blobel GA, Chodosh LA, Weiss MJ. Global regulation of erythroid gene expression by transcription factor GATA-1. Blood. 2004 Nov 15;104(10):3136-47.

Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9(9):R137.

Publications

Wu W, Cheng Y, Keller CA, Ernst J, Kumar SA, Mishra T, Morrissey C, Dorman CM, Chen KB, Drautz D et al. Dynamics of the epigenetic landscape during erythroid differentiation after GATA1 restoration. Genome Res. 2011 Oct;21(10):1659-71.

Data Release Policy

Data users may freely use ENCODE data, but may not, without prior consent, submit publications that use an unpublished ENCODE dataset until nine months following the release of the dataset. This date is listed in the Restricted Until column, above. The full data release policy for ENCODE is available here.