This track displays a chromatin state segmentation for each of nine human cell types. A common set of states across the cell types were learned by computationally integrating ChIP-seq data for nine factors plus input using a Hidden Markov Model (HMM). In total, fifteen states were used to segment the genome, and these states were then grouped and colored to highlight predicted functional elements.
This track is a composite track that contains multiple subtracks. Each subtrack represents data for a different cell type and displays individually on the browser. Instructions for configuring tracks with multiple subtracks are here. The fifteen states of the HMM, their associated segment color, and the candidate annotations are as follows:
ChIP-seq data from the Broad Histone track was used to generate this track. Data for nine factors plus input and nine cell types was binarized separately at a 200 base pair resolution based on a Poisson background model. The chromatin states were learned from this binarized data using a multivariate Hidden Markov Model (HMM) that explicitly models the combinatorial patterns of observed modifications (Ernst and Kellis, 2010). To learn a common set of states across the nine cell types, first the genomes were concatenated across the cell types. For each of the nine cell types, each 200 base pair interval was then assigned to its most likely state under the model. Detailed information about the model parameters and state enrichments can be found in (Ernst et al, accepted).
This is release 1 (Jun 2011) of this track, and it is based on the NCBI36/hg18 release of the Broad Histone track. This track has also been lifted over to GRCh37/hg19. It is anticipated that the HMM methods will be run on the newer GRCh37/hg19 Broad Histone data and will replace the lifted version.
The ChIP-seq data were generated at the Broad Institute and in the Bradley E. Bernstein lab at the Massachusetts General Hospital/Harvard Medical School, and the chromatin state segmentation was produced in Manolis Kellis's Computational Biology group at the Massachusetts Institute of Technology. Contact: Jason Ernst.
Data generation and analysis was supported by funds from the NHGRI (ENCODE), the Burroughs Wellcome Fund, Howard Hughes Medical Institute, NSF, Sloan Foundation, Massachusetts General Hospital and the Broad Institute.
Ernst J, Kellis M. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat Biotechnol. 2010 Aug;28(8):817-25.
Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB, Zhang X, Wang L, Issner R, Coyne M et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature. 2011 May 5;473(7345):43-9.
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.
There is no restriction on the use of segmentation data.