DNaseI Hypersensitivity by Digital DNaseI from ENCODE/University of Washington
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: ENCODE DCC
PROVIDER: E-GEOD-29692 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Publications
The accessible chromatin landscape of the human genome.
Thurman Robert E RE Rynes Eric E Humbert Richard R Vierstra Jeff J Maurano Matthew T MT Haugen Eric E Sheffield Nathan C NC Stergachis Andrew B AB Wang Hao H Vernot Benjamin B Garg Kavita K John Sam S Sandstrom Richard R Bates Daniel D Boatman Lisa L Canfield Theresa K TK Diegel Morgan M Dunn Douglas D Ebersol Abigail K AK Frum Tristan T Giste Erika E Johnson Audra K AK Johnson Ericka M EM Kutyavin Tanya T Lajoie Bryan B Lee Bum-Kyu BK Lee Kristen K London Darin D Lotakis Dimitra D Neph Shane S Neri Fidencio F Nguyen Eric D ED Qu Hongzhu H Reynolds Alex P AP Roach Vaughn V Safi Alexias A Sanchez Minerva E ME Sanyal Amartya A Shafer Anthony A Simon Jeremy M JM Song Lingyun L Vong Shinny S Weaver Molly M Yan Yongqi Y Zhang Zhancheng Z Zhang Zhuzhu Z Lenhard Boris B Tewari Muneesh M Dorschner Michael O MO Hansen R Scott RS Navas Patrick A PA Stamatoyannopoulos George G Iyer Vishwanath R VR Lieb Jason D JD Sunyaev Shamil R SR Akey Joshua M JM Sabo Peter J PJ Kaul Rajinder R Furey Terrence S TS Dekker Job J Crawford Gregory E GE Stamatoyannopoulos John A JA
Nature 20120901 7414
DNase I hypersensitive sites (DHSs) are markers of regulatory DNA and have underpinned the discovery of all classes of cis-regulatory elements including enhancers, promoters, insulators, silencers and locus control regions. Here we present the first extensive map of human DHSs identified through genome-wide profiling in 125 diverse cell and tissue types. We identify ∼2.9 million DHSs that encompass virtually all known experimentally validated cis-regulatory sequences and expose a vast trove of n ...[more]
Similar Datasets
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Florencia Pauli mailto:fpauli@hudsonalpha.org). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track is produced as part of the ENCODE project. The track reports the percentage of DNA molecules that exhibit cytosine methylation at specific CpG dinucleotides. In general, DNA methylation within a gene's promoter is associated with gene silencing, and DNA methylation within the exons and introns of a gene is associated with gene expression. Proper regulation of DNA methylation is essential during development and aberrant DNA methylation is a hallmark of cancer. DNA methylation status is assayed at more than 500,000 CpG dinucleotides in the genome using Reduced Representation Bisulfite Sequencing (RRBS). Genomic DNA is digested with the methyl-insensitive restriction enzyme MspI, small genomic DNA fragments are purified by gel electrophoresis, and then used to construct an Illumina sequencing library. The library fragments are treated with sodium bisulfite and amplified by PCR to convert every unmethylated cytosine to a thymidine while leaving methylated cytosines intact. The sequenced fragments are aligned to a customized reference genome sequence and for each assayed CpG we report the number of sequencing reads covering that CpG and the percentage of those reads that are methylated. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf DNA methylation at CpG sites was assayed with a modified version of Reduced Representation Bisulfite Sequencing (RRBS; Meissner et al., 2008). RRBS was performed on cell lines grown by many ENCODE production groups. The production group that grew the cells and isolated genomic DNA is indicated in the "obtainedBy" field of the metadata. When a cell type was provided by more than one lab, the data for the cells from only one lab are displayed in the table above. However, the data for every cell type from every lab is available from the Downloads page. RRBS was carried out by the Myers production group at the HudsonAlpha Institute for Biotechnology. Isolation of genomic DNA Genomic DNA is isolated from biological replicates of each cell line using the QIAGEN DNeasy Blood & Tissue Kit according to the instructions provided by the manufacturer. DNA concentrations for each genomic DNA preparation are determined using fluorescent DNA binding dye and a fluorometer (Invitrogen Quant-iT dsDNA High Sensitivity Kit and Qubit Fluorometer). Typically, 1 µg of DNA is used to make an RRBS library; however, we have also had success in making libraries with 200 ng genomic DNA from rare or precious samples. RRBS library construction and sequencing RRBS library construction starts with MspI digestion of genomic DNA , which cuts at every CCGG regardless of methylation status. Klenow exo- DNA Polymerase is then used to fill in the recessed end of the genomic DNA and add an adenosine as a 3prime overhang. Next, a methylated version of the Illumina paired-end adapters is ligated onto the DNA. Adapter ligated genomic DNA fragments between 105 and 185 basepairs are selected using agarose gel electrophoresis and Qiagen Qiaquick Gel Extraction Kit. The selected adapter-ligated fragments are treated with sodium bisulfite using the Zymo Research EZ DNA Methylation Gold Kit, which converts unmethylated cytosines to uracils and leaves methylated cytosines unchanged. Bisulfite treated DNA is amplified in a final PCR reaction which has been optimized to uniformly amplify diverse fragment sizes and sequence contexts in the same reaction. During this final PCR reaction uracils are copied as thymines resulting in a thymine in the PCR products wherever an unmethylated cytosine existed in the genomic DNA. The sample is now ready for sequencing on the Illumina sequencing platform. These libraries were sequenced with an Illumina Genome Analyzer IIx according to the manufacturer's recommendations. Data analysis To analyze the sequence data, a reference genome is created that contains only the 36 base pairs adjacent to every MspI site and every C in those sequences is changed to T. A converted sequence read file is then created by changing each C in the original sequence reads to a T. The converted sequence reads are aligned to the converted reference genome, and only reads that map uniquely to the reference genome are kept. Once reads are aligned the percent methylation is calculated for each CpG using the original sequence reads. The percent methylation and number of reads is reported for each CpG.
2011-03-01 | E-GEOD-27584 | biostudies-arrayexpress
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Richard Sandstrom mailto:sull@u.washington.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track is produced as part of the ENCODE Project. This track displays maps of genome-wide binding of the CTCF transcription factor in different cell lines using ChIP-seq high-throughput sequencing For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols. Cells were crosslinked with 1% formaldehyde, and the reaction was quenched by the addition of glycine. Fixed cells were rinsed with PBS, lysed in nuclei lysis buffer, and the chromatin was sheared to 200-500 bp fragments using Fisher Dismembrator (model 500). Sheared chromatin fragments were immunoprecipitated with specific polyclonal antibodies at 4 degrees C with gentle rotation. Antibody-chromatin complexes were washed and eluted. The cross linking in immunoprecipitated DNA was reversed and treated with RNase-A. Following proteinase K treatment, the DNA fragments were purified by phenol-chloroform-isoamyl alcohol extraction and ethanol precipitation. 20-50 ng of ChIP DNA was end-repaired, adenine ligated to Illumina adapters was added, and then a Solexa library was made for sequencing. ChIP-seq affinity is directly reflected in raw tag density (Raw Signal), which is shown in the track as density of tags mapping within a 150 bp sliding window (at a 20 bp step across the genome). ChIP-seq affinity zones (HotSpots) were identified using the HotSpot algorithm described in Sabo et al. (2004). 1.0% false discovery rate thresholds (FDR 0.01) were computed for each cell type by applying the HotSpot algorithm to an equivalent number of random uniquely mapping 36mers. ChIP-seq affinity (Peaks) were identified as signal peaks within FDR 1.0% hypersensitive zones using a peak-finding algorithm.
2011-06-29 | E-GEOD-30263 | biostudies-arrayexpress
Project description:This data was produced by the Wold lab at Caltech as part of the ENCODE Project. RNA-Seq is a method for mapping and quantifying the transcriptome of any organism that has a genomic DNA sequence assembly. RNA-Seq is performed by reverse-transcribing an RNA sample into cDNA, followed by high throughput DNA sequencing. The resulting sequence reads are then informatically mapped onto the genome sequence. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf The transcriptome measurements shown on these tracks were performed on polyA selected RNA from total cellular RNA. Data have been produced in two formats: single reads, each of which comes from one end of a randomly primed cDNA molecule; and paired-end reads, which are obtained as pairs from both ends cDNAs resulting from random priming. The resulting sequence reads are then informatically mapped onto the genome sequence (Alignments). Those that don't map to the genome are mapped to known RNA splice junctions (Splice Sites). These mapped reads are then counted to determine their frequency of occurrence at known gene models. Sequence reads that cluster at genome locations that lack an existing transcript model are also identified informatically and they are quantified. RNA-Seq is especially suited for giving information about RNA splicing patterns and for determining unequivocally the presence or absence of lower abundance class RNAs. As performed here, internal RNA standards are used to assist in quantification and to provide internal process controls. This RNA-Seq protocol does not specify the coding strand. As a result, there will be ambiguity at loci where both strands are transcribed. The "randomly primed" reverse transcription is, apparently, not fully random. This is inferred from a sequence bias in the first residues of the read population, and this likely contributes to observed unevenness in sequence coverage across transcripts. These tracks show 1x32 n.t. or 2x75 n.t. sequence reads of cDNA obtained from biological replicate samples (different culture plates) of the ENCODE cell lines. The 32 n.t. sequences were aligned to the human genome (hg18) and UCSC known-gene splice junctions using different sequence alignment programs. The 2x75 n.t. reads were mapped serially, first with the Bowtie program (Langmead et al., 2009) against the genome and UCSC known-gene splice junctions (Splice Sites). Bowtie-unmapped reads were then mapped using BLAT to find evidence of novel splicing, by requiring at least 10 bp on the short-side of the splice.
2010-07-30 | E-GEOD-23316 | biostudies-arrayexpress
Project description:This track is produced as part of the ENCODE Project. It shows high throughput sequencing of RNA samples from tissues or sub cellular compartments from cell lines included in the ENCODE Transcriptome subproject. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf The RNA-Seq data were generated from high quality polyA RNA, and the RNA-Seq libraries were constructed using SOLiD Whole Transcriptome (WT) protocol and reagent kit. Total RNA in good quality was used as starting materials and purified twice through MACs polyT column aimed to enrich polyA and remove any contaminants (e.g., rRNA, tRNA, DNA, protein etc.). A one microgram enriched polyA RNA sample was then fragmented to small pieces, and a gel-based selection method was performed to collect fragmented random polyA at a size-range of 50-150 nt in length. The collected fragmental RNA was then hybridized and ligated to a mix of adapters provided from ABI, followed by reverse transcription to generate corresponding cDNAs. The resulting cDNA library was further amplified by PCR and sequenced by SOLiD platform for single reads at 35 bp length (new version in 50 bp length). Cells were grown according to the approved ENCODE cell culture protocols. Data: The SOLiD-generated RNA-Seq reads were 35 bp in length. An initial filtering process was performed to remove any non-desirable contamination sequences, such as rRNA, tRNA, and repeats etc. A read-split mapping approach was developed to map the 35 bp reads onto the reference genome (GRCh37/hg19) excluding mitochondrion, haplotypes, randoms and chromosome Y. Mapping parameters: Strand specific mapping was done using Applied Biosystems' SOLiD alignment where all the reads were mapped to the genome, and to exon-exon junction database. Seed and extend strategy is adopted where initial seed length of 25 is mapped with maximum of 2 mismatches and then extended to read length, each color space match is awarded a score of +1 and each mismatch is awarded a penalty of -2. After extension each read is trimmed to its maximum score, shortest length. The color space sequences are then converted into base space and checked to ensure that each sequence has a maximum of 2 base pair mismatches. If any sequence has more than 2 mismatches, then that sequence is discarded.
2011-02-14 | E-GEOD-27221 | biostudies-arrayexpress
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Yijun Ruan mailto:ruanyj@gis.a-star.edu.sg). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track, produced as part of the ENCODE Project, contains deep sequencing DNase data that will be used to identify sites where regulatory factors bind to the genome (footprints). Footprinting is a technique used to define the DNA sequences that interact with and bind DNA-binding proteins, such as transcription factors, zinc-finger proteins, hormone-receptor complexes, and other chromatin-modulating factors like CTCF. The technique depends upon the strength and tight nature of protein-DNA interactions. In their native chromatin state, DNA sequences that interact directly with DNA-binding proteins are relatively protected from DNA degrading endonucleases, while the exposed/unbound portions are readily degraded by such endonucleases. A massively parallel next-generation sequencing technique to define the DNase hypersensitive sites in the genome was adopted. Sequencing these next-generation-sequencing DNase samples to significantly higher depths of 300-fold or greater produces a base-pair level resolution of the DNase susceptibility maps of the native chromatin state. These base-pair resolution maps represent and are dependent upon the nature and the specificity of interaction of the DNA with the regulatory/modulatory proteins binding at specific loci in the genome; thus they represent the native chromatin state of the genome under investigation. The deep sequencing approach has been used to define the footprint landscape of the genome by identifying DNA motifs that interact with known or novel DNA binding proteins. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols. Digital DNaseI was performed by DNaseI digestion of intact nuclei, followed by isolating DNaseI 'double-hit' fragments as described in Sabo et al. (2006), and direct sequencing of fragment ends (which correspond to in vivo DNaseI cleavage sites) using the Solexa platform (27 bp reads). High-quality reads were mapped to the GRCh37/hg19 human genome using Bowtie 0.12.5 (Eland was used to map to NCBI36/hg18); only unique mappings were kept. DNaseI sensitivity is directly reflected in raw tag density (Signal), which is shown in the track as density of tags mapping within a 150 bp sliding window (at a 20 bp step across the genome). DNaseI hypersensitive zones (HotSpots) were identified using the HotSpot algorithm described in Sabo et al. (2004). False discovery rate thresholds of 1.0% (FDR 0.01) were computed for each cell type by applying the HotSpot algorithm to an equivalent number of random uniquely mapping 36-mers. DNaseI hypersensitive sites (DHSs or Peaks) were identified as signal peaks within 1.0% (FDR 0.01) hypersensitive zones using a peak-finding algorithm. Only DNase Solexa libraries from unique cell types producing the highest quality data, as defined by Percent Tags in Hotspots (PTIH ~40%) were designated for deep sequencing to a depth of over 200 million tags.
2011-04-27 | E-GEOD-28867 | biostudies-arrayexpress
Project description:This track depicts high throughput sequencing of long RNAs (>200 nt) from RNA samples from tissues or subcellular compartments from ENCODE cell lines. The overall goal of the ENCODE project is to identify and characterize all functional elements in the sequence of the human genome. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols. Sample preparation and sequencing: K562 and GM12878 total cell, total RNA: Standard Illumina Pair-end kit with the sole exception that a "tagged" random hexamer was used to prime the 1st strand synthesis: 5'-ACTGTAGGN6-3'. The addition of this tag is what permits us to make strand assignments for the reads. The sequence of the tag is reported in the 5' end of the read. Asymmetric PCR can place the tag on either the 1st or 2nd read depending on which strand it used as a template. Strand assignments are made by looking for the tag at the 5' end of either read 1 or read 2. Read 1 is physically linked to read 2. Therefore, if a tag is present on one end strand assignments are made for both ends. We noted during analysis that the tags are generally 5' truncated. We only "strand" reads that contain ACTGTAGG, CTGTAGG, TGTAGG, GTAGG. Between 63-68% of reads could be stranded in these libraries. It is possible to cull additional stranded reads that contain non-templated TAGG, AGG, GG, or G sequences at their 5' end. The peak in insert size distribution is between 200-250 nucleotides. K562 cytosol, polyA+ RNA: Oligo-dT selected poly-A+ RNA was RiboMinus-treated according to the manufacturer's protocol (Invitrogen). The RNA was treated with tobacco alkaline pyrophosphatase to eliminate any 5' cap structures and hydrolyzed to ~200 bases via alkaline hydrolysis. The 3' end was repaired using calf intestinal alkaline phosphatase, and poly-A polymerase was used to catalyze the addition of Cs to the 3' end. The 5' end was phosphorylated using T4 PNK, and an RNA linker was ligated onto the 5' end. Reverse transcription was carried out using a poly-G oligo with a defined 5' extension. The inserts were then amplified using oligos targeting the 5' linker and poly-G extension. This cloning protocol generated stranded reads that were read from the 5' ends of the inserts. The library was sequenced on a Solexa platform for a total of 36 cycles; however, the reads underwent post-processing, resulting in trimming of their 3' ends. Consequently, the mapped read lengths are variable. Analysis: K562 and GM12878 total cell, total RNA: Tags were removed from the 5' ends of the reads in accordance to their lengths and strand assignments made. Subsequently, the reads were trimmed from their 3' ends to a final length of 50 nucleotides and were mapped using NexAlign, a program developed by Timo Lassman, RIKEN. We allowed up to 2 mismatches across the entire length and only report reads that mapped to a single/unique locus in the assembled hg18 genome. K562 cytosol, polyA+ RNA: Reads were mapped to the human (hg18, March 2006) assembly using Nexalign, with only uniquely mapping (one loci), exactly matching (no mis-matches) aligned reads reported in the processed files, as follows: 1) Collect the read sequences from Illumina non-filtered output files. 2) Filter out all reads that contain undefined nucleotides ('N'). 3) Perform iterative alignment/C-tail chopping algorithm (below). On each alignment step, the reads are aligned to the genome with 100% identity. All reads that align to a single locus are withdrawn from the alignment pool and only the reads that could not be aligned continue to the next step. a) Align to the hg18 genome using Nexalign 1.3.3 (© Timo Lassmann) without chopping off any nucleotides. b) Chop off any C-blocks (until the first non-C) at the ends of the reads. c) Align to the genome -> remove and save those that align. d) Chop off any non-Cs until the next C. e) Chop off C-block until the next non-C. f) Align to the genome -> remove and save those that align. g) Repeat steps d, e, and f until the reads align to the genome, or chopping results in the reduction of the reads' lengths to below 16 (default), or there are no non-Cs left.
2011-10-12 | E-GEOD-32931 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the following subset Series: GSE30567: ENCODE Cold Spring Harbor Labs Long RNA-seq (hg19) GSE32931: ENCODE Cold Spring Harbor Labs Long RNA-seq (hg18) For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Refer to individual Series
2010-12-28 | E-GEOD-26284 | biostudies-arrayexpress
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Richard Sandstrom mailto:sull@u.washington.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track, produced as part of the ENCODE Project, contains deep sequencing DNase data that will be used to identify sites where regulatory factors bind to the genome (footprints). Footprinting is a technique used to define the DNA sequences that interact with and bind DNA-binding proteins, such as transcription factors, zinc-finger proteins, hormone-receptor complexes, and other chromatin-modulating factors like CTCF. The technique depends upon the strength and tight nature of protein-DNA interactions. In their native chromatin state, DNA sequences that interact directly with DNA-binding proteins are relatively protected from DNA degrading endonucleases, while the exposed/unbound portions are readily degraded by such endonucleases. A massively parallel next-generation sequencing technique to define the DNase hypersensitive sites in the genome was adopted. Sequencing these next-generation-sequencing DNase samples to significantly higher depths of 300-fold or greater produces a base-pair level resolution of the DNase susceptibility maps of the native chromatin state. These base-pair resolution maps represent and are dependent upon the nature and the specificity of interaction of the DNA with the regulatory/modulatory proteins binding at specific loci in the genome; thus they represent the native chromatin state of the genome under investigation. The deep sequencing approach has been used to define the footprint landscape of the genome by identifying DNA motifs that interact with known or novel DNA binding proteins. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols. Digital DNaseI was performed by DNaseI digestion of intact nuclei, followed by isolating DNaseI 'double-hit' fragments as described in Sabo et al. (2006), and direct sequencing of fragment ends (which correspond to in vivo DNaseI cleavage sites) using the Solexa platform (27 bp reads). High-quality reads were mapped to the GRCh37/hg19 human genome using Bowtie 0.12.5 (Eland was used to map to NCBI36/hg18); only unique mappings were kept. DNaseI sensitivity is directly reflected in raw tag density (Signal), which is shown in the track as density of tags mapping within a 150 bp sliding window (at a 20 bp step across the genome). DNaseI hypersensitive zones (HotSpots) were identified using the HotSpot algorithm described in Sabo et al. (2004). False discovery rate thresholds of 1.0% (FDR 0.01) were computed for each cell type by applying the HotSpot algorithm to an equivalent number of random uniquely mapping 36-mers. DNaseI hypersensitive sites (DHSs or Peaks) were identified as signal peaks within 1.0% (FDR 0.01) hypersensitive zones using a peak-finding algorithm. Only DNase Solexa libraries from unique cell types producing the highest quality data, as defined by Percent Tags in Hotspots (PTIH ~40%) were designated for deep sequencing to a depth of over 200 million tags.
2010-12-29 | E-GEOD-26328 | biostudies-arrayexpress
Project description:The basic body plan and major physiological axes have been highly conserved during mammalian evolution, yet only a small fraction of the human genome sequence appears to be subject to evolutionary constraint. To quantify cis- versus trans-acting contributions to mammalian regulatory evolution, we performed genomic DNase I footprinting of the mouse genome across 25 cell and tissue types, collectively defining ~8.6 million transcription factor (TF) occupancy sites at nucleotide resolution. Here we show that mouse TF footprints conjointly encode a regulatory lexicon that is ~95% similar with that derived from human TF footprints. However, only ~20% of mouse TF footprints have human orthologues. Despite substantial turnover of the cis-regulatory landscape, nearly half of all pairwise regulatory interactions connecting mouse TF genes have been maintained in orthologous human cell types through evolutionary innovation of TF recognition sequences. Furthermore, the higher-level organization of mouse TF-to-TF connections into cellular network architectures is nearly identical with human. Our results indicate that evolutionary selection on mammalian gene regulation is targeted chiefly at the level of trans-regulatory circuitry, enabling and potentiating cis-regulatory plasticity. We performed genomic DNase I footprinting of the mouse genome across 25 cell and tissue types, collectively defining ~8.6 million transcription factor (TF) occupancy sites at nucleotide resolution.
2014-11-19 | E-GEOD-51341 | biostudies-arrayexpress
Project description:Distal cell-type-specific regulatory elements may be located at very large distances from the genes that they control and are often hidden within intergenic regions or in introns of other genes. The development of methods that enable mapping of regions of open chromatin genome wide has greatly advanced the identification and characterisation of these elements. Here we use DNase I hypersensitivity mapping followed by deep sequencing (DNase-seq) to generate a map of open chromatin in primary human tracheal epithelial (HTE) cells and use bioinformatic approaches to characterise the distribution of these sites within the genome and with respect to gene promoters, intronic and intergenic regions. Genes with THE-selective open chromatin at their promoters were associated with multiple pathways of epithelial function and differentiation. The data predict novel cell-type-specific regulatory elements for genes involved in HTE cell function, such as structural proteins and ion channels, and the transcription factors that may interact with them to control gene expression. Moreover, the map of open chromatin can identify the location of potentially critical regulatory elements in genome-wide association studies (GWAS) in which the strongest association is with single nucleotide polymorphisms in non-coding regions of the genome. We demonstrate its relevance to a recent GWAS that identifies modifiers of cystic fibrosis lung disease severity. Since HTE cells have many functional similarities with bronchial epithelial cells and other differentiated cells in the respiratory epithelium, these data are of direct relevance to elucidating the molecular basis of normal lung function and lung disease. To identify cis-regulatory elements for genes expressed in human tracheal epithelial cells we generated genome-wide maps of open chromatin by DNase-seq. HTE cells were isolated from these trachea and grown as described previously (Davis P.B. et al.1990).
2013-11-08 | E-GEOD-52179 | biostudies-arrayexpress