Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

RIP-seq from ENCODE/SUNY Albany

ABSTRACT: This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Scott Tenenbaum mailto:STenenbaum@uamail.albany.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). The RNA binding protein (RBP) associated mRNA sequencing track (RIP-Seq) is produced as part of the Encyclopedia of DNA Elements (ENCODE) Project (http://hgwdev.cse.ucsc.edu/ENCODE/index.html). This track displays transcriptional fragments associated with RBP in cell lines (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType) K562 and GM12878, using Ribonomic profiling via Illumina SBS. In eukaryotic organisms gene regulatory networks require an additional level of coordination that links transcriptional and post-transcriptional processes. Messenger RNAs have traditionally been viewed as passive molecules in the pathway from transcription to translation. However, it is now clear that RNA-binding proteins play a major role in regulating multiple mRNAs in order to facilitate gene expression patterns. These tracks show the associated mRNAs that co-precipitate with the targeted RNA-binding proteins using RIP-Seq profiling. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf RBP-mRNA complexes were purified from cells grown according to the approved ENCODE cell culture protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell). RNA samples were amplified and converted to cDNA with the Nugen (http://www.nugeninc.com/) Ovation© RNA-Seq System and prepped for sequencing with the Illumina (http://www.illumina.com/) mRNA-Seq protocol. Approximately 30 million single end sequencing reads were obtained for each K562 and GM12878. RIP samples were analyzed for signal that was at or above the 60th percentile and statistically enriched compared to the negative control. Sequences were analyzed using TopHat (http://tophat.cbcb.umd.edu/) (Trapnell et al., 2009) with Bowtie (http://bowtie-bio.sourceforge.net/index.shtml) (Langmead et al., 2009). Peaks were called from the top 40% of TopHat normalized reads, with a max gap, min run of (24:48). Unions of overlapping peak regions from total RNA replicates (RIP-Input) are presented with p-value from a one tailed t-test for average signal from replicates versus 0 (no cut-off was used for totals). Replicate overlap for positive RIP treatment peaks (ELAVL1 and PABPC1) are presented with a p-value from one tailed t-test versus signal for same the region in negative control replicates (T7-tag). RIP peaks were from sequences longer than 120 bp and p-value < .05. For both totals (RIP-input) and RIPs, the peak scores are scaled relative p-values between treatment and control.

ORGANISM(S): Homo sapiens

SUBMITTER: UCSC ENCODE DCC

PROVIDER: E-GEOD-35585 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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 was produced as part of the ENCODE Project. This track displays genome-wide maps of histone modifications in different cell lines (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType) 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 (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell). Cells were cross-linked 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 a Fisher Dismembrator (model 500). Sheared chromatin fragments were immunoprecipitated with specific polyclonal antibodies at 4 °C with gentle rotation. Antibody-chromatin complexes were washed and eluted. The cross-linking in the 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. A quantity of 20-50 ng of ChIP DNA was end-repaired, followed by the addition of adenine, ligation to Illumina adapters, and creation of a Solexa library for sequencing. ChIP-seq affinity was directly measured through the 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). One percent false discovery rate thresholds (FDR 1.0%) were computed for each cell type by applying the HotSpot algorithm to an equivalent number of random uniquely mapping 36-mers. ChIP-Seq affinities (Peaks) were identified as signal peaks within FDR 1.0% hypersensitive zones using a peak-finding algorithm. All tracks have a False Discovery Rate of 1% (FDR 1.0%). Data were verified by sequencing biological replicates displaying a correlation coefficient > 0.9.

Project description:This track displays a chromatin state segmentation for each of nine human cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=GM12878,H1-hESC,HepG2,HUVEC,HMEC,HSMM,K562,NHEK,NHLF). A common set of states across the cell types were learned by computationally integrating ChIP-seq data for nine factors plus input (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=CTCF,H3K4me1,H3K4me2,H3K4me3,H3K27ac,H3K9ac,H3K36me3,H4K20me1,H3K27me3,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. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf ChIP-seq data from the Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeBroadChipSeq) track was used to generate this track. Data for nine factors plus input (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=CTCF,H3K4me1,H3K4me2,H3K4me3,H3K27ac,H3K9ac,H3K36me3,H4K20me1,H3K27me3,Input) and nine cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=GM12878,H1-hESC,HepG2,HUVEC,HMEC,HSMM,K562,NHEK,NHLF) 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 (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeBroadChipSeq) track. This track has also been lifted over to GRCh37/hg19 (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHmm). It is anticipated that the HMM methods will be run on the newer GRCh37/hg19 Broad Histone (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHistone) data and will replace the lifted version.

Project description:This track is produced as part of the mouse ENCODE Project. This track shows DNaseI sensitivity measured genome-wide in mouse tissues and cell lines using the Digital DNaseI methodology (see below), and DNaseI hypersensitive sites. DNaseI has long been used to map general chromatin accessibility and DNaseI hypersensitivity is a universal feature of active cis-regulatory sequences. The use of this method has led to the discovery of functional regulatory elements that include enhancers, insulators, promotors, locus control regions and novel elements. For each experiment (tissue/cell type) this track shows DNaseI sensitivity as a continuous function using sequencing tag density (Signal), and discrete loci of DNaseI sensitive zones (HotSpots) and hypersensitive sites (Peaks). 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 (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell/mouse). Fresh tissues were harvested from mice and the nuclei prepared according to the tissue appropriate protocol (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/cell/mouse). Digital DNaseI was performed by DNaseI digestion of intact nuclei, 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 Illumina IIx (and Illumina HiSeq by early 2011) platform (36 bp reads). Uniquely mapping high-quality reads were mapped to the genome using the bowtie aligner. DNaseI sensitivity is directly reflected in raw tag density, 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 sensitive 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. DNaseI hypersensitive sites (DHSs or Peaks) were identified as signal peaks within FDR 1.0% hypersensitive zones using a peak-finding algorithm (I-max).

Dataset Information

RIP-seq from ENCODE/SUNY Albany

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