Dataset Information

RNA Subcellular Localization by Paired End diTag Sequencing from ENCODE/GIS

ABSTRACT: 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 is produced as part of the ENCODE Transcriptome Project. It shows the starts and ends of full length mRNA transcripts determined by GIS paired-end ditag (PET) sequencing using RNA extracts (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=rnaExtract) from different sub-cellular localizations (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=localization) in different cell lines (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType). The RNA-PET information provided in this track is composed of two different PET length versions based on how the PETs were extracted. The cloning-based PET (18 bp and 16 bp) is an earlier version and detailed information can be found from reference (Ng et al. 2006). The cloning-free PET (25 bp and 25 bp) is a recently modified version which uses Type II enzyme EcoP15I to generate a longer length of PET (unpublished), which results in a significant enhancement in both library construction and mapping efficiency. Both versions of PET templates were sequenced by Illumina platform at 2 x 36 bp Paired End sequencing. See the Methods and References sections below for more details. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf

ORGANISM(S): Homo sapiens

PROVIDER: GSE33600 | GEO | 2011/11/10

REPOSITORIES: GEO

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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 data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Piero Carninci mailto:carninci@riken.jp). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track shows 5' cap analysis gene expression (CAGE) tags and clusters in RNA extracts (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=rnaExtract) from different sub-cellular localizations (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=localization) in multiple cell lines (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType). A CAGE cluster is a region of overlapping tags with an assigned value that represents the expression level. The data in this track were produced as part of the ENCODE Transcriptome Project. Release 2 has three new downloads only files per experiment (Clusters, TSS Gencode 7 and TSS HMM) and four new cell lines (A459, AG04450, BJ and SK-N-SH_RA). Release 1 on hg19 contained the original data on hg18 (http://hgwdev.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeRikenCage) that was remapped and indicated in this release as Generation 0 since that data had no replicates. If there is both old and new generation data available for a particular experiment, only the new generation data is displayed and the older data is available for download. The new data for this track was done with a different process and has standard replicate numbers. The replicate labeling in the genome browser view is a counter indicating the total number of replicates submitted. The producing lab has replicate numbers that correspond to their internal bio-replicate numbering. Where these two numbering systems conflict, both are listed in the long label of the specific track. 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). RNA molecules longer than 200 nt were isolated from each subcellular compartment and then were fractionated into polyA+ and polyA- fractions as described in these protocols (http://hgwdev.cse.ucsc.edu/ENCODE/protocols/general/rnaExtracts.txt). The CAGE tags were sequenced from the 5' ends of cap-trapped cDNAs produced using RIKEN CAGE technology (Kodzius et al. 2006; Valen et al. 2009). To create the tag, a linker was attached to the 5' end of polyA+ or polyA- reverse-transcribed cDNAs which were selected by cap trapping (Carninci et al. 1996). The first 27 bp of the cDNA were cleaved using class II restriction enzymes. A linker was then attached to the 3' end of the cDNA. After PCR amplification, the tags were sequenced (36 bp single reads) using Illumina's Genome analyzer. Tags were mapped to the human genome (hg19) using the program delve (T. Lassmann manuscript in preparation). Delve is a new probabilistic aligner focused on giving the best possible alignment of reads to a genome rather than focusing on speed. It calculates the mapping accuracy (probability of each alignment being true or not) for each alignment. There is no set limit on the number of errors allowed and therefore the mapping rate is commonly 100%. However, for analysis it is recommended to discard alignments with low mapping qualities. Exceptions to the above protocol are the polyA- RNA samples from K562 cytosol, K562 nucleus, and prostate whole cell which were sequenced using ABI SOLiD (http://www.appliedbiosystems.com/absite/us/en/home/applications-technologies/solid-next-generation-sequencing.html) technology. These reads were mapped using Bowtie using default parameters. Clusters were defined as regions of overlapping CAGE reads. The expression level was computed as the number of reads making up the cluster, divided by the total number of reads sequenced, times 1 million.

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 is produced as part of the ENCODE Transcriptome Project. It shows the starts and ends of full length mRNA transcripts determined by GIS paired-end ditag (PET) sequencing using RNA extracts (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=rnaExtract) from different sub-cellular localizations (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=localization) in different cell lines (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType). The RNA-PET information provided in this track is composed of two different PET length versions based on how the PETs were extracted. The cloning-based PET (18 bp and 16 bp) is an earlier version and detailed information can be found from reference (Ng et al. 2006). The cloning-free PET (25 bp and 25 bp) is a recently modified version which uses Type II enzyme EcoP15I to generate a longer length of PET (unpublished), which results in a significant enhancement in both library construction and mapping efficiency. Both versions of PET templates were sequenced by Illumina platform at 2 x 36 bp Paired End sequencing. See the Methods and References sections below for more details. 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). Two different GIS RNA-PET protocols were used to generate the full length transcriptome PETs: one is based on a cloning-free RNA-PET library construction and sequencing strategy (unpublished), and the other is a cloning-based library construction (Ng et al. 2005) and recent Illumina paired end sequencing. Cloning-free RNA-PET (50 bp reads, 25 bp and 25 bp tag for each of the 5' and 3' ends)--Method: The cloning-free RNA-PET libraries were generated from polyA mRNA samples and constructed using a recently modified GIS protocol (unpublished). Total RNA in good quality was used as starting material and purified through MACs polyT column to obtain full length polyA mRNAs. Approximately 5 micrograms of enriched polyA mRNA were used for reverse transcription to convert polyA mRNA to full length cDNA. The obtained full length cDNA was modified and ligated with specific linker sequences, followed by circularization through ligation to generate circular cDNA molecules. The 25 bp tag from each end of the full length cDNA was extracted by type II enzyme EcoP15I digestion. The resulting PETs were ligated with sequencing adaptors at the both ends, amplified by PCR, and further purified as complex templates for paired end (PE) sequencing using Illumina platforms. Data: The sequenced RNA-PETs are unified in 25/25 bp length from each end of a cDNA. After filtering out redundant and noise tags, the unique PETs will proceed to analysis pipeline. Initially, the orientation of each tag will be screened out by the barcode built in the sequencing-template, then paired into a given orientation-PET. The orientation-determined RNA-PET is mapped onto reference genome allowing up to two mismatches. Majority of PETs are mapped on the known transcripts, or splice variants. A small portion of misaligned PETs, defined as discordant PETs, are mapped either too far from each tag, have wrong orientations, or mapped in different chromosomes, indicating exist some transcription variations which could be caused by genome structure variations: such as fusion, deletion, insertion, inversion, tandem repeat and translocation; or RNA trans-splicing etc. Cloning-based RNA-PET (34 bp reads, 18 bp and 16 bp tag for each of the 5' and 3' ends)--Method: The cloning-based RNA-PET (GIS-PET) libraries were generated from polyA RNA samples and constructed using the protocol described by Ng et al. (2005). Total RNA in good quality was used as starting material and further purified through MACs polyT column to enrich polyA mRNA and remove any contaminants (e.g., rRNA, tRNA, DNA, protein etc). Approximately 10 micrograms of polyA mRNA were then used for reverse transcription to convert polyA mRNA into full length cDNA. The obtained full length cDNA was modified with specific linker sequences, then, ligated to a GIS-developed (pGIS4) vector to form a complex full length cDNA library, which was cloned into E. coli. The plasmid DNA was then isolated from the library, followed by MmeI (a type II enzyme) digestion to generate a final length of 18 bp/16 bp ditags from each end of the full length cDNA. The single ditag (or called PET) was then ligated to form a diPET structure (a concatemer with two unrelated PET linked by a linker sequence) to facilitate Illuminaa Paired End sequencing. Data: The cloning-based RNA-PETs are unified in 18 bp and 16 bp length, respectively extracted from 5' and 3' end of each cDNA. The redundant reads were filtered out initially and unique ones were included for analysis. PET sequences were then mapped to (GRCh37, hg19, excluding mitochondirion, haplotypes, randoms and chromosome Y) reference genome using the following specific criteria (Ruan et al. 2007): A minimal continuous 16 bp match must exist for the 5' signature; the 3' signature must have a minimal continuous 14 bp match. Both 5' and 3' signatures must be present on the same chromosome. Their 5' to 3' orientation must be correct (5' signature followed by 3' signature). The maximal genomic span of a PET genomic alignment must be less than one million bp. PETs mapping to 2-10 locations are also included and may represent duplicated genes or pseudogenes in the genome. A majority of PETs mapped on the known transcripts or splice variants. A small portion of misaligned PETs, defined as discordant PETs, were mapped either too far from each other, mapped in the wrong orientation, or mapped to different chromosomes, indicating that some transcription variations exist which could be caused by genome structure variations: such as fusion, deletion, insertion, inversion, tandem repeat and translocation; or RNA trans-splicing etc. Clusters: To cluster the PETs the following procedure was applied: the mapping location of the 5' and 3' tag of a given PET was extended by 100 bp in both directions creating 5' and 3' search windows. If the 5' and 3' tags of a second PET mapped within the 5' and 3' search window of the first PET then the two PETs were clustered and the search windows were adjusted so that they contained the tag extensions of the second PET. PETs which subsequently mapped with their 5' and 3' tags within the adjusted 5' and 3' search window, respectively, were also assigned to this cluster and search window readjusted. This iterative process continued till no new PET was found to fall within the search window, at which stage all the found PETs are classified as belonging to a single cluster. This process is repeated till all PETs are assigned to a cluster. Verification: To assess overall PET quality and mapping specificity, the top ten most abundant PET clusters that mapped to well-characterized known genes were examined. Over 99% of the PETs represented full-length transcripts, and the majority fell within 10 bp of the known 5' and 3' boundaries of these transcripts. The PET mapping was further verified by confirming the existence of physical cDNA clones represented by the ditags. PCR primers were designed based on the PET sequences and amplified the corresponding cDNA inserts either from full length cDNA library (cloning-based PET) or from total RNA isolate (cloning-free PET) for sequencing confirmation.

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.

Dataset Information

RNA Subcellular Localization by Paired End diTag Sequencing from ENCODE/GIS

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