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Transcription Factor Binding Sites by ChIP-seq from ENCODE/PSU   (All Expression and Regulation tracks)

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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 with 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 Occupancy by Transcription Factors
Maps of occupancy of genomic DNA by transcription factors (TFs) are determined 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 TF binding 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. 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 38 ChIP-seq experiments on Transcription Factor Binding Sites with the addition of 14 new experiments.

One data set added an additional replicate: Megakaryocyte/GATA1_(SC-265) (UCSC Accession: wgEncodeEM002351). Files that have been reanalyzed have a version number appended to the name, e.g.V2.

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

Credits

Cell growth, ChIP, and Illumina library construction were done by researchers in the laboratories of Ross Hardison (PSU), Gerd Blobel and Mitch Weiss (Children's Hospital of Philadelphia); major contributors include Yong Cheng, Weisheng Wu, Deepti Jain, Cheryl Keller, Swathi Ashok Kumar, Tejaswini Mishra, Marta Byrska-Bishop, Stephan Kadauke, Maheshi Udugama, and Rena Zheng. Sequencing on the Illumina platform was done largely by Cheryl Keller in the laboratory of Ross Hardison (PSU). Data processing and analysis had major input from Chris Morrissey, Dan Blankenberg, and Belinda Giardine, as overseen by James Taylor (Emory University) and 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

Aplan PD, Nakahara K, Orkin SH, Kirsch IR. The SCL gene product: a positive regulator of erythroid differentiation. EMBO J. 1992 Nov;11(11):4073-81.

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.

Green AR, DeLuca E, Begley CG. Antisense SCL suppresses self-renewal and enhances spontaneous erythroid differentiation of the human leukaemic cell line K562. EMBO J. 1991 Dec;10(13):4153-8.

Huang S, Brandt SJ. mSin3A regulates murine erythroleukemia cell differentiation through association with the TAL1 (or SCL) transcription factor. Mol Cell Biol. 2000 Mar;20(6):2248-59.

Huang S, Qiu Y, Shi Y, Xu Z, Brandt SJ. P/CAF-mediated acetylation regulates the function of the basic helix-loop-helix transcription factor TAL1/SCL. EMBO J. 2000 Dec 15;19(24):6792-803.

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.

Shivdasani RA, Mayer EL, Orkin SH. Absence of blood formation in mice lacking the T-cell leukaemia oncoprotein tal-1/SCL. Nature. 1995 Feb 2;373(6513):432-4.

Tripic T, Deng W, Cheng Y, Zhang Y, Vakoc CR, Gregory GD, Hardison RC, Blobel GA. SCL and associated proteins distinguish active from repressive GATA transcription factor complexes. Blood. 2009 Mar 5;113(10):2191-201.

Wadman IA, Osada H, Grütz GG, Agulnick AD, Westphal H, Forster A, Rabbitts TH. The LIM-only protein Lmo2 is a bridging molecule assembling an erythroid, DNA-binding complex which includes the TAL1, E47, GATA-1 and Ldb1/NLI proteins. EMBO J. 1997 Jun 2;16(11):3145-57.

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.

Wold B, Myers RM. Sequence census methods for functional genomics. Nat Methods. 2008 Jan;5(1):19-21.

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