Description

These tracks display chromatin immunoprecipitation (ChIP-seq) evidence as part of the four Open Chromatin track sets (see below). ChIP-seq is a method to identify the specific location of proteins that are directly or indirectly bound to genomic DNA. By identifying the binding location of sequence-specific transcription factors, general transcription machinery components, and chromatin factors, ChIP can help in the functional annotation of the open chromatin regions identified by DNaseI HS mapping and FAIRE.

Together with DNaseI HS and FAIRE experiments, these tracks display the locations of active regulatory elements identified as open chromatin in multiple cell types from the Duke, UNC-Chapel Hill, UT-Austin, and EBI ENCODE group. Within this project, open chromatin was identified using two independent and complementary methods: DNaseI hypersensitivity (HS) and Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE), combined with these ChIP-seq assays for select regulatory factors. DNaseI HS and FAIRE provide assay cross-validation with commonly identified regions delineating the highest confidence areas of open chromatin. These ChIP assays provide functional validation and preliminary annotation of a subset of open chromatin sites. Each method employed Illumina (formerly Solexa) sequencing by synthesis as the detection platform. The Tier 1 and Tier 2 cell types were additionally verified by a second platform, high-resolution 1% ENCODE tiled microarrays supplied by NimbleGen.

As a background control experiment, the input genomic DNA sample that was used for ChIP was sequenced. Crosslinked chromatin was sheared and the crosslinks were reversed without carrying out the immunoprecipitation step. This sample was otherwise processed in a manner identical to the ChIP sample as described below. The input track is useful in revealing potential artifacts arising from the sequence alignment process such as copy number differences between the reference genome and the sequenced samples, as well as regions of poor sequence alignability. For cell lines for which there is no input experiment available, the peaks were generated using the control of generic_male or generic_female, as an attempt to create a general background based on input data from several cell types. These files are in "iff" format, which is used when calling peaks with F-seq software, and can be downloaded from the production lab directly from under the section titled "Copy number / karyotype correction."

Other Open Chromatin track sets:

• Data for the DNaseI HS experiments can be found in Duke DNaseI HS.
• Data for the FAIRE experiments can be found in UNC FAIRE.
• A synthesis of all the open chromatin assays for select cell lines can be found in Open Chrom Synth.

Display Conventions and Configuration

This track is a multi-view composite track that contains a single data type with multiple levels of annotation (views). For each view, there are multiple subtracks representing different cell types that display individually on the browser. Instructions for configuring multi-view tracks are here. ChIP data displayed here represents a continuum of signal intensities. The Iyer lab recommends setting the "Data view scaling: auto-scale" option when viewing signal data in full mode to see the full dynamic range of the data. Note that in regions that do not have open chromatin sites, autoscale will rescale the data and inflate the background signal, making the regions appear noisy. Changing back to fixed scale will alleviate this issue. In general, for each experiment in each of the cell types, the UTA TFBS tracks contain the following views:

Peaks

Regions of enriched signal in ChIP experiments. Peaks were called based on signals created using F-Seq, a software program developed at Duke (Boyle et al., 2008b). Significant regions were determined by fitting the data to a gamma distribution to calculate p-values. Contiguous regions where p-values were below a 0.05/0.01 threshold were considered significant. The solid vertical line in the peak represents the point with highest signal.

F-Seq Density Signal

Density graph (wiggle) of signal enrichment calculated using F-Seq for the combined set of sequences from all replicates. F-Seq employs Parzen kernel density estimation to create base pair scores (Boyle et al., 2008b). This method does not look at fixed-length windows but rather weights contributions of nearby sequences in proportion to their distance from that base. It only considers sequences aligned 4 or less times in the genome and uses an alignability background model to try to correct for regions where sequences cannot be aligned. For each cell type, especially important for those with an abnormal karyotype, a model to try to correct for amplifications and deletions that is based on control input data was also used.

Base Overlap Signal

An alternative version of the F-Seq Density Signal track annotation that provides a higher resolution view of the raw sequence data. This track also includes the combined set of sequences from all replicates. For each sequence, the aligned read is extended 5 bp in both directions from its 5' aligned end where DNase cut the DNA. The score at each base pair represents the number of extended fragments that overlap the base pair.

Peaks and signals displayed in this track are the results of pooled replicates. The raw sequence and alignment files for each replicate are available for download.

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.

To perform ChIP, proteins were cross-linked to DNA in vivo using 1% formaldehyde solution (Bhinge et al., 2007; ENCODE Project Consortium, 2007). Cross-linked chromatin was sheared by sonication and immunoprecipitated using a specific antibody against the protein of interest. After reversal of the cross-links, the immunoprecipitated DNA was used to identify the genomic location of transcription factor binding. This was accomplished by sequencing of the ends of the immunoprecipitated DNA (ChIP-seq) using the Illumina (Solexa) sequencing system. ChIP data for Tier 1 and Tier 2 cell lines were verified by comparing multiple independent growths (replicates) and determining the reproducibility of the data. For some cell types, additional verification was performed using the same immunoprecipitated DNA by labeling and hybridizing to NimbleGen Human ENCODE tiling arrays (1% of the genome) along with the input DNA as reference (ChIP-chip). A more detailed protocol is available here.

DNA fragments isolated by ChIP are 100-200 bp in length, with the average length being 134 bp. Sequences from each experiment were aligned to the genome using Burrows-Wheeler Aligner (Li et al., 2010) for the GRCh37 (hg19) assembly.

Sequences from multiple lanes are combined for a single replicate using the bwa samse command, and converted in the sam/bam format using SAMtools.

Only those that aligned to 4 or fewer locations were retained. Other sequences were also filtered based on their alignment to problematic regions (such as satellites and rRNA genes - see supplemental materials). The mappings of these short reads to the genome are available for download.

The resulting digital signal was converted to a continuous wiggle track using F-Seq that employs Parzen kernel density estimation to create base pair scores (Boyle et al., 2008b). Input data has been generated for several cell lines. These are used directly to create a control/background model used for F-Seq when generating signal annotations for these cell lines. These models are meant to correct for sequencing biases, alignment artifacts, and copy number changes in these cell lines. Input data is not being generated directly for other cell lines. Instead, a general background model was derived from the available input data sets. This should provide corrections for sequencing biases and alignment artifacts, but will not correct for cell type specific copy number changes.

Discrete ChIP sites (peaks) were identified from ChIP-seq F-seq density signal. Significant regions were determined by fitting the data to a gamma distribution to calculate p-values. Contiguous regions where p-values were below a 0.05/0.01 threshold were considered significant.

Data from the high-resolution 1% ENCODE tiled microarrays supplied by NimbleGen were normalized using the Tukey biweight normalization, and peaks were called using ChIPOTle (Buck, et al., 2005) at multiple levels of significance. Regions matched on size to these peaks that were devoid of any significant signal were also created as a null model. These data were used for additional verification of Tier 1 and Tier 2 cell lines by ROC analysis. Files labeled Validation view containing this data are available for download.

Release Notes

Release 2 (August 2011) of this track adds 34 new experiments including 17 new cell lines.