This track shows DNaseI sensitivity measured across ENCODE regions using the Quantitative Chromatin Profiling (QCP) method (Dorschner et al. (2004)). DNaseI has long been used to map general chromatin accessibility and the DNaseI "hyperaccessibility" or "hypersensitivity" that is a universal feature of active cis-regulatory sequences. The use of this method has lead to the discovery of functional regulatory elements that include enhancers, insulators, promotors, locus control regions and novel elements. QCP provides a quantitative high-throughout method for the mapping DNaseI sensitivity as a continuous function of genome position. The moving baseline of mean DNaseI sensitivity is computed using a locally-weighted least squares (LOWESS)-based algorithm.
DNaseI-treated and untreated chromatin samples from the following cell lines/phenotypes were studied:
Key for Source entry in table:
Cell Line Description Source CD4 CD4+ lymphoid Primary CaCo2 intestinal cancer ATCC CaLU3 lung cancer ATCC EryAdult CD34-derived primary adult erythroblasts Primary EryFetal CD34-derived primary fetal erythroblasts Primary GM06990 EBV-transformed lymphoblastoid Coriell HMEC mammary epithelium Cambrex HRE renal epithelial Cambrex HeLa cervical cancer ATCC HepG2 hepatic ATCC Huh7 hepatic JCRB K562 erythroid ATCC NHBE bronchial epithelial Cambrex PANC pancreatic ATCC SAEC small airway epithelial Cambrex SKnSH neural ATCC
DNaseI sensitivity is expressed in standard units, where each increment of 1 unit corresponds to an increase of 1 standard deviation from the baseline. The displayed values are calculated as copies in DNaseI-untreated / copies in DNaseI-treated. Thus, increasing values represent increasing sensitivity. Major DNaseI hypersensitive sites are readily identified as peaks in the signal that exceed 2 standard deviations (corresponding to the ~95% confidence bound on outliers). This is reflected in the default viewing parameters, which apply a lower y-axis threshold of 2 (i.e., showing only sites that exceed the 95% confidence bound).
The subtracks within this composite annotation track correspond to data from different tissues, and may be configured in a variety of ways to highlight different aspects of the displayed data. Four tissue types are present throughout all ENCODE regions: GM06990, CaCo2, HeLa, and SKnSH. Several Relevant tissues were also studied for several ENCODE regions that contain tissue-specific genes. These include the alpha- and beta-globin loci (ENm008 and ENm009); the apolipoprotein A1/C3 loci (ENm003); and the Th2 cytokine locus (ENm002). Color differences among the subtracks are arbitrary; they provide a visual cue for distinguishing the different cell lines/phenotypes.
The graphical configuration options are shown at the top of the track description page, followed by a list of subtracks. To display only selected subtracks, uncheck the boxes next to the tracks you wish to hide. For more information about the graphical configuration options, click the Graph configuration help link.
QCP was performed as described in Dorschner et al. Data were obtained from a tiling path across ENCODE that comprises 102,008 distinct amplicons (mean length = 243 +/- 13). The amplicon tiling path is available through UniSTS. The tiling path covers approximately 86% of ENCODE regions, including many repetitive regions. The Dorschner et al. article describes the methods of chromatin preparation, DNaseI digestion, and DNA purification utilized. DNaseI-treated and -untreated control samples were prepared from each tissue. For each tissue, 6-10 biological replicates (defined as replicate cultures grown from seed and harvested on different days) were pooled together to create a master sample. The relative number of intact copies of the genomic DNA sequence was quantified over the entire tiling path real-time PCR for both DNaseI-treated and -untreated samples. Four to eight technical replicates were performed for each measurement from each amplicon in each tissue. Data shown are the means of these technical replicates. The results were analyzed as described in Dorschner et al. to compute the moving baseline of mean DNaseI sensitivity and to identify outliers that correspond with DNaseI hypersensitive sites. The standard deviation of trimmed mean measurements was used to convert data to standard units.
Data generation, analysis, and validation were performed by the following members of the ENCODE group at the University of Washington (UW) in Seattle.
UW Medical Genetics: Patrick Navas, Man Yu, Hua Cao, Brent Johnson, Ericka Johnson, Tristan Frum, and George Stamatoyannopoulos.
UW Genome Sciences: Michael O. Dorschner, Richard Humbert, Peter J. Sabo, Scott Kuehn, Robert Thurman, Anthony Shafer, Jeff Goldy, Molly Weaver, Andrew Haydock, Kristin Lee, Fidencio Neri, Richard Sandstrom, Shane Neff, Brendan Henry, Michael Hawrylycz, Janelle Kawamoto, Paul Tittel, Jim Wallace, William S. Noble, and John A. Stamatoyannopoulos.
Dorschner MO, Hawrylycz M, Humbert R, Wallace JC, Shafer A, Kawamoto J, Mack J, Hall R, Goldy J, Sabo PJ et al. High-throughput localization of functional elements by quantitative chromatin profiling. Nat Methods. 2004 Dec;1(3):219-25.