This track represents a comprehensive set of human transcription factor binding sites based on ChIP-seq experiments generated by production groups in the ENCODE Consortium from the inception of the project in September 2007, through the March 2012 internal data freeze. The track represents peak calls (regions of enrichment) that were generated by the ENCODE Analysis Working Group (AWG) based on a uniform processing pipeline developed for the ENCODE Integrative Analysis effort and published in a set of coordinated papers in September 2012. Peak calls from that effort, based on datasets from the January 2011 ENCODE data freeze) are available at the ENCODE Analysis Data Hub. This track is an update that includes newer data, and slightly modified methods for the peak calling.
This track contains 695 ChIP-seq datasets representing 177 unique regulatory factors (generic and sequence-specific factors). The datasets span 91 human cell types and some are in various treatment conditions. These datasets were generated by four main production groups : Broad, Stanford/Yale/UC-Davis/Harvard, HudsonAlpha, University of Texas(Austin) and Unversity of Washington.
The display for this track shows site location with the point-source of the peak marked with a colored vertical bar. The display can be filtered to higher valued items, using the 'Minimum signal' configuration item.
This track is a composite annotation track containing multiple subtracks, one for each cell type. The display mode and filtering of each subtrack can be individually controlled. For more information about track configuration, see Configuring Multi-View Tracks. Metadata for a particular subtrack can be found by clicking the down arrow in the list of subtracks. The UCSC Accession listed in the metadata can be used with the File Search tool to retrieve primary data files underlying datasets of interest.
In the subtrack selection list, the ENCODE tier (priority) is listed for each cell type. Tier 1 and Tier 2 represent categories with cell types designated for intensive study by the ENCODE investigators. After the January 2011 data freeze, an additional set of cell types were promoted from Tier 3 to Tier 2 to broaden the list of intensively studied cell types. These cell types are listed as Tier 2* in the subtrack list here (and are described as 'newly promoted to tier 2: not in 2011 analysis' on the ENCODE Common Cell Types page).
All ChIP-seq experiments were performed at least in duplicate, and were scored against an appropriate control designated by the production groups (either input DNA or DNA obtained from a control immunoprecipitation).
For each dataset, mapped reads in the form of BAM files were downloaded from the ENCODE UCSC DCC. These BAM files were generated by the individual ENCODE data production labs (using different mappers and mapping parameters), but all used a standardized version of the GRCh37 (hg19) reference human genome sequence with the following modifications:
In order to standardize the mapping protocol, custom unique-mappability tracks were used to only retain unique mapping reads i.e. reads that map to exactly one location in the genome. Positional and PCR duplicates were also filtered out.
A number of quality metrics for individual replicates listed at http://encodeproject.org/ENCODE/qualityMetrics.html, including measures of library complexity and signal enrichment, were calculated and are available for review (Landt et al. 2012, Kundaje et al., 2013a). Datasets that did not pass the minimum quality control thresholds were flagged. As sequencing has become more economical, minimum standards for the number of reads required for submission of data have been established and upgraded over the course of the ENCODE project. Datasets (replicates) were required to have a minimum of 10M uniquely mapping reads and those that did not were flagged.
Since every ENCODE dataset is represented by at least two biological replicate experiments, a novel measure of consistency and reproducibility of peak calling results between replicates, known as the Irreproducible Discovery Rate (IDR), was used to determine an optimal number of reproducible peaks (Li et al., 2011, Kundaje et al., 2013b). Code and detailed step-by-step instructions to call peaks using in the IDR method are available. In brief, the SPP peak caller (Kharchenko et al. 2008) was used with a relaxed peak calling threshold (FDR = 0.9) to obtain a large number of peaks (maximum of 300K) that span true signal as well as noise (false identifications). The IDR method analyzes a pair of replicates, and considers peaks that are present in both replicates to belong to one of two populations : a reproducible signal group or an irreproducible noise group. Peaks from the reproducible group are expected to show relatively higher ranks (ranked based on signal scores) and stronger rank-consistency across the replicates, relative to peaks in the irreproducible groups. Based on these assumptions, a two-component probabilistic copula-mixture model is used to fit the bivariate peak rank distributions from the pairs of replicates. The method adaptively learns the degree of peak-rank consistency in the signal component and the proportion of peaks belonging to each component. The model can then be used to infer an IDR score for every peak that is found in both replicates. The IDR score of a peak represents the expected probability that the peak belongs to the noise component, and is based on its ranks in the two replicates. Hence, low IDR scores represent high-confidence peaks. An IDR score threshold of 0.02 (2%) was used to obtain an optimal peak rank threshold on the replicate peak sets (cross-replicate threshold). If a dataset had more than two replicates, all pairs of replicates were analyzed using the IDR method. The maximum peak rank threshold across all pairwise analyses was used as the final cross-replicate peak rank threshold. Reads from replicate datasets were then pooled and SPP was once again used to call peaks on the pooled data with a relaxed FDR of 0.9. Pooled-data peaks were once again ranked by signal-score. The cross-replicate rank threshold learned from the replicates was used to threshold the ranked set of pooled-data peaks.
Any thresholds based on reproducibility of peak calling between biological replicates are bounded by the quality and enrichment of the worst replicate. Valuable signal is lost in cases for which a dataset has one replicate that is significantly worse in data quality than another replicate. A rescue pipeline was used for such cases. In order to balance data quality between a set of replicates. Mapped reads were pooled across all replicates of a dataset, and then randomly sampled (without replacement) to generate two pseudo-replicates with equal numbers of reads. This sampling strategy tends to transfer signal from stronger replicates to the weaker replicates, thereby balancing cross-replicate data quality and sequencing depth. These pseudo-replicates were then processed using the IDR method in order to learn a rescue threshold. For datasets with comparable replicates (based on independent measures of data quality), the rescue threshold and cross-replicate thresholds were found to be very similar. However, for datasets with replicates of differing data quality, the rescue thresholds were often higher than the cross-replicate thresholds, and were able to capture true peaks that showed statistically significant and visually compelling ChIP-seq signal in one replicate but not in the other. Ultimately, for each dataset, the best of the cross-replicate and rescue thresholds were used to obtain a final consolidated optimal set of peaks.
All peak sets were then screened against a specially curated empirical blacklist (http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeMapability/wgEncodeDacMapabilityConsensusExcludable.bed.gz) of regions in the human genome and peaks overlapping the blacklisted regions were discarded (Kundaje et al.,2013b). Briefly, these artifact regions typically show the following characteristics:
The January 2011 uniform processing was performed as part of the ENCODE Integrative Analysis reported in coordinated publications in September 2012. The results from this effort are available from the ENCODE Analysis Hub at the EBI.
The processed data for this track were generated by Anshul Kundaje on behalf of the ENCODE Analysis Working Group. Credits for the primary data underlying this track are included in track description pages listed in the Description section above.
Contact: Anshul Kundaje
ENCODE Project Consortium. A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 2011 Apr;9(4):e1001046.
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 2012 Sep 6;489(7414):57-74.
Kharchenko PV, Tolstorukov MY, Park PJ. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol. 2008 Dec;26(12):1351-9.
Kundaje A, Jung L, Kharchenko PV, Sidow A, Batzoglou S, Park PJ. Assessment of ChIP-seq data quality using strand cross-correlation analysis (submitted), 2012a.
Kundaje A, Li Q, Brown JB, Rozowsky J, Harmanci A, Wilder SP, Batzoglou S, Dunham I, Gerstein M, Birney E, et al. Reproducibility measures for automatic threshold selection and quality control in ChIP-seq datasets (submitted), 2012b.
Li QH, Brown JB, Huang HY, Bickel PJ. Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat. 2011; 5(3):1752-1779.
While primary ENCODE data is subject to a restriction period as described in the ENCODE data release policy, this restriction does not apply to the integrative analysis results. The data in this track are freely available.