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 in only one species will be discovered. The results will have a significant impact on the understanding of the evolution of gene regulation.
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DNaseI hypersensitive sites were isolated using methods called DNase-seq or DNase-chip (Song and Crawford, 2010). Briefly, cells were lysed with NP40, and intact nuclei were digested with optimal levels of DNaseI enzyme. DNaseI-digested ends were captured from three different DNase concentrations, and material was sequenced using Illumina sequencing.
The read length for sequences from DNase-seq is 20 bases long due to a MmeI cutting step of the approximately 50 kb DNA fragments extracted after DNaseI digestion. Sequences from each experiment were mapped to the mouse genome (mm9 assembly) using the program Bowtie (Langmead et al., 2009). Reads mapping to more than one location were not removed. For such reads, only the best mapping result was used ("--best" option). Sequences from multiple lanes were combined for a single replicate and converted to the sam/bam format using SAMtools. Using F-seq, the resulting digital signal was converted to a continuous wiggle track that employs a Parzen kernel density estimation to create base pair scores (Boyle et al., 2008).
Discrete DNaseI HS sites (peaks) were identified from the DNase-seq F-seq density signal. Significant regions were determined by fitting the data to a gamma distribution to calculate p-values.
Cell growth and DNaseI digestion were done by Christine Dorman in the Hardison lab, and DNase-seq libraries were constructed in the laboratory of Greg Crawford (Duke). Sequencing was done by the laboratory of Greg Crawford (Duke). Data processing and analysis was done by Chris Morrissey (PSU) and Yoichiro Shibata (Duke) with advice from Terry Furey (University of North Carolina). Some analyses used 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
Boyle AP, Guinney J, Crawford GE, Furey TS. F-Seq: a feature density estimator for high-throughput sequence tags. Bioinformatics. 2008 Nov 1;24(21):2537-8.
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.
Song L, Crawford GE. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb Protoc. 2010 Feb;2010(2):pdb.prot5384.
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.
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