RNA-seq from ENCODE/Stanford/Yale/USC/Harvard
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: UCSC ENCODE DCC
PROVIDER: E-GEOD-35587 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:The tracks show enrichment of RNA sequence tags mapped to the mouse genome generated by high throughput sequencing (RNA-Seq). Double stranded cDNA was synthesized from enriched RNA that was obtained after depletion of ribosomal RNA. Pieces of cDNA, 300-350 nucleotides in length, were PCR amplified, adapter ligated, and sequenced on an Illumina HiSeq sequencer. 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). Total RNA was extracted using RNeasy Mini Kit (74104, Aiagen), following the manufacturer's protocol. Ribosomal RNA was removed from total RNA using the Ribo-ZeroTM Gold Kits (MRZG126, Epicentre). Double-stranded cDNA synthesis was performed on the rRNA depleted RNA using random primers and the SuperScript double-stranded cDNA synthesis kit (11917-010, Life Tech). After first strand cDNA synthesis, NucAway Spin Column (Ambion cat. 100070-30) was used to remove dNTPs. In the second strand cDNA synthesis reaction, dTTP in the dNTP mix was substituted with dUTP. After end repair and addition of 'A' base to 3' end, illumina paired-end adapter was ligated to Double-stranded cDNA library. After gel size selection of adapter ligated cDNA (300-350), Uracil-N-Glycosylase (UNG: Applied Biosystems) was used to digest the second strand cDNA (Parkhomchuk et al. , 2009). PCR amplified adapter ligated cDNA was sequenced using Illumina HiSeq. Sequence reads of 2x101 nt long with 0-2 mismatches were mapped to the mouse genome (version mm9) using the BWA aligner, version 0.5.7. The signal height corresponds to the number of overlapping fragments at each nucleotide position in the genome.
2012-07-24 | E-GEOD-39619 | biostudies-arrayexpress
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Gerstein Lab mailto:datasubmission@gersteinlab.org). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). The tracks show enrichment of RNA sequence tags generated by high throughput sequencing (RNA-seq) and mapped to the human genome. Double stranded cDNA was synthesized from polyadenylated RNA (polyA+) . PCR amplified, adapter ligated cDNA, 150-300nt long, was sequenced on an Illumina GA sequencer. Where designated, cell lines received specific treatments prior to RNA isolation. As indicated, K562 cells were treated with either interferon-a or interferon-g for 30 minutes or 6 hours. These experiments were carried out in conjunction with ChIP-Seq experiments on the transcription factors STAT1 and STAT2 in order to examine the effects that inducers of a specific transcriptional response might have on gene expression and on transcription factor binding site discovery. K562 cells were treated with a-amanitin in order to examine the effects of RNA polymerase II inhibition on RNA polymerase III-mediated transcription. This track shows expression data generated as confirmation of the SYDH TFBS (http://genome-preview.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeSydhTfbs) tracks currently available on genome-preview. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2012-04-25 | GSE35587 | GEO
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Philip Cayting mailto:pcayting@stanford.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). The tracks show enrichment of RNA sequence tags mapped to the mouse genome generated by high throughput sequencing (RNA-Seq). Double stranded cDNA was synthesized from enriched RNA that was obtained after depletion of ribosomal RNA. Pieces of cDNA, 300-350 nucleotides in length, were PCR amplified, adapter ligated, and sequenced on an Illumina HiSeq sequencer. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2012-07-25 | GSE39619 | GEO
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 data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Carrie Davis mailto:davisc@cshl.edu (experimental), Roderic Guigo mailto:rguigo@imim.es and lab (data processing) and Tom Gingeras mailto:gingeras@cshl.edu (primary investigator)). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). These tracks were generated by the ENCODE Consortia. They contain information about mouse RNAs > 200 nucleotides in length obtained as short reads off the Illumina platform. Data are available from biological replicates. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Tissue Samples: Individual tissues were harvested from mouse strain C57BL/6NJ at different timepoints according to ENCODE cell culture protocols. Whenever possible biological replicates from litermates. Library Preparation: The published cDNA sequencing protocol was used. This protocol generates directional libraries and reports the transcripts' strand of origin. Exogenous RNA spike-ins were added to each endogenous RNA isolate and carried through library construction and sequencing. The spike-in sequence and the concentrations are available for download in the supplemental directory. Sequencing and Mapping: The libraries were sequenced on the Illumina platform (either GAIIx or Hi-Seq) in mate-pair fashion (either pair-end 76 or pair-end 101) to an average depth of 100 million mate-pairs. The data were mapped against hg19 using Spliced Transcript Alignment and Reconstruction (STAR) written by Alex Dobin (CSHL). More information about STAR, including the parameters used for these data, is available from the Gingeras lab. Verification: FPKM (fragments per kilobase of exon per million fragments mapped) values were calculated for annotated exons and Spearman correlation coefficients were computed. In general, Rho values are > .90 between biological replicates.
2012-04-02 | E-GEOD-36025 | 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 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.
2011-11-10 | E-GEOD-33600 | biostudies-arrayexpress
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (mailto:georgi@caltech.edu for data coordination/informatics/experimental questions, mailto:diane@caltech.edu for informatics questions, mailto:bawilli_91125@yahoo.com for experimental questions). 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. 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, which was done here on an Illumina Genome Analyzer (GAI or GAIIx) (Mortazavi et al., 2008). The transcriptome measurements shown on these tracks were performed on polyA selected RNA (http://genome.ucsc.edu/cgi-bin/hgEncodeVocab?term=longPolyA&type=rnaExtract) from total cellular RNA (http://genome.ucsc.edu/cgi-bin/hgEncodeVocab?term=cell&type=localization) using two different protocols - one that preserves information about which strand the read is coming from and one that does not. Due to the specifics of the enzymology of library construction, gene and transcript quantification is more accurate based on the non-strand-specific protocol, while the strand-specific protocol is useful for assigning strandedness, but in general less reliable for quantification. Non-strand-specific protocol (deep "reference" transcriptome measurements, 2x75 bp reads): PolyA-selected RNA was fragmented by magnesium-catalyzed hydrolysis and then converted into cDNA by random priming and amplified. Data have been produced in two formats: single reads, each of which comes from one end of a cDNA molecule, and paired-end reads, which are obtained as pairs from both ends of cDNAs. 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. Strand specific protocol (1x75 bp reads): PolyA-selected RNA was fragmented by magnesium-catalyzed hydrolysis. 3' adapters were ligated to the 3' end of fragments, then 5' adapters were ligated to the 5' end. The resulting RNA molecules were converted to cDNA and amplified. This RNA-seq protocol does specify the coding strand as each read is in the same 5'-3' orientation as the original RNA strand. As a result, loci where both strands are transcribed can be disambiguated. However, RNA ligation is an inherently biased process and as a result greater unevenness in sequence coverage across transcripts is observed compared to the non-strand-specific data, and quantification is less accurate. Data Analysis: Reads were aligned to the hg19 human reference genome using TopHat, a program specifically designed to align RNA-seq reads and discover splice junctions de novo. Cufflinks, a de novo transcript assembly and quantification software package, was run on the TopHat alignments to discover and quantify novel transcripts and to obtain transcript expression estimates based on the GENCODE annotation. All sequence files, alignments, gene and transcript models and expression estimates files are available for download. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2012-07-12 | GSE33480 | GEO
Project description:The purified RNA from NFs (n=1) and KFs (n=1) treated with DMSO or ASC-J9 was used for the preparation of the sequencing library by the TruSeq Stranded mRNA Library Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer’s recommendations. Briefly, mRNA was purified from total RNA (1 µg) by oligo(dT)-coupled magnetic beads and fragmented into small pieces under elevated temperature. The first-strand cDNA was synthesized using reverse transcriptase and random primers. After the generation of double-strand cDNA and adenylation on the 3’ ends of DNA fragments, the adaptors were ligated and purified with the AMPure XP system (Beckman Coulter, Beverly, USA). The quality of the libraries was assessed by the Agilent Bioanalyzer 2100 system and a real-time PCR system. The qualified libraries were then sequenced on an Illumina NovaSeq 6000 platform with 150-bp paired-end reads generated by Genomics BioSci & Tech Co., Ltd.
2022-06-01 | GSE198095 | GEO
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Terry Furey mailto:tsfurey@duke.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). These tracks display Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE) evidence as part of the four Open Chromatin track sets. FAIRE is a method to isolate and identify nucleosome-depleted regions of the genome. FAIRE was initially discovered in yeast and subsequently shown to identify active regulatory elements in human cells (Giresi et al., 2007). Similar to DNaseI HS, FAIRE appears to identify functional regulatory elements that include promoters, enhancers, silencers, insulators, locus control regions and novel elements. Together with DNaseI HS and ChIP-seq experiments, these tracks display the locations of active regulatory elements identified as open chromatin in multiple cell types (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?type=cellType) from the Duke, UNC-Chapel Hill, UT-Austin, and EBI ENCODE group. Within this project, open chromatin was identified using two independent and complementary methods: DNaseI hypersensitivity (HS) and these FAIRE assays, combined with chromatin immunoprecipitation (ChIP) for select regulatory factors. DNaseI HS and FAIRE provide assay cross-validation with commonly identified regions delineating the highest confidence areas of open chromatin. ChIP assays provide functional validation and preliminary annotation of a subset of open chromatin sites. Each method employed Illumina (formerly Solexa) sequencing by synthesis as the detection platform. The Tier 1 and Tier 2 cell types were additionally verified by a second platform, high-resolution 1% ENCODE tiled microarrays supplied by NimbleGen. Release 1 (March 2011) of this track consists of a remapping of all previously released experiments to the human reference genome GRCh37/hg19 (these data were previously mapped to NCBI36/hg18; please see the Release Notes section of the hg18 Open Chromatin track (http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg18&g=wgEncodeChromatinMap) for information on the NCBI36/hg18 releases of the data). -There are 12 new FAIRE experiments in this release, on 10 new cell lines. -New to this release is a reconfiguration of how this track is displayed in relation to other tracks from the Duke/UNC/UT-Austin/EBI group. -A synthesis of open chromatin evidence from the three assay types was compiled for Tier 1 and 2 cell lines plus NHEK will also be added in this release and can be previewed in: Open Chromatin Synthesis (http://genome-preview.cse.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeOpenChromSynth). -Enhancer and Insulator Functional assays: A subset of DNase and FAIRE regions were cloned into functional tissue culture reporter assays to test for enhancer and insulator activity. Coordinates and results from these experiments can be found at http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeOpenChromFaire/supplemental/. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2012-01-24 | GSE35239 | GEO
Project description:To understand the molecular mechanism of CCE, we performed the RNA-seq analysis. Cells were seeded at 5x10^5 cells/well in 6-well plates then treated with 1μg/mL CCE for 24 h. MDA-MB-231 cells were harvested, and RNA was isolated with TRIzol. The libraries were constructed by using the Quick cDNA Library Preparation Kit and sequenced on an XTEN sequencing system. RNA-seq data were mapped to the reference genome (hg19) by using HISAT2. The reads were counted, and StringTie was used to calculate the FPKM value of the genes. The differentially expressed genes (DEGs) were identified by comparing the experimental group with the control group.
2021-11-12 | GSE125356 | GEO