## Description

Rationale for the Mouse ENCODE project
Our 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 we interpret these 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. Thus we will be able to discover which epigenetic features are conserved between mouse and human, and we can examine the extent to which these overlap with the DNA sequences under negative selection. The contribution of DNA that with a function preserved in mammals versus that with a function in only one species will be discovered. The results will have a significant impact on our understanding of the evolution of gene regulation.

Maps of histone modifications
Levels of three histone modifications are being determined. H3K4me1 (monomethylation of lysine 4 of histone H3) is a mark for active chromatin and in the absence of H3K4me3, it is one indicator of an enhancer. H3K4me3 (trimethylation of lysine 4 of histone H3) is highly enriched at active promoters. One repressive (Polycomb) mark, H3K27me3, is associated with some silenced genes. 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 is 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 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 are being 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. Because 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 aligments and sequences are available for download at downloads page.

## Credits

Cell growth, ChIP, and Illumina library construction was done in the laboratories of Ross Hardison, Mitchell J. Weiss and Gerd A. Blobel (PSU and Children's Hospital of Philadelpia). Sequencing on the Illumina is done by the laboratories of Stephan Schuster and Ross Hardison. Data processing and analysis is overseen by James Taylor (Emory University) and Anton Nekrutenko and Hardison(PSU). 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 April; 125(2): 315-326.

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

Blankenberg D, Gordon A, Von Kuster G, Coraor N, Taylor J, Nekrutenko A. Manipulation of FASTQ data with Galaxy. Bioinformatics 2010 June 18; 26(14): 1783-1785.

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 Feb; 39(3): 311-318.

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

Muller 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; 111(2): 197-208.

Rando OJ, Chang HY. Genome-wide views of chromatin structure Annu Rev Biochem 2009 July; 78: 245-271.

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 March; 16: 3145-3157.

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; 104(10): 3136-3147.

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

## 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 on the track configuration page and the download page. The full data release policy for ENCODE is available here.