RNA-seq from ENCODE/LICR
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
SUBMITTER: UCSC ENCODE DCC
PROVIDER: E-GEOD-36026 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:RNA-Seq reads and TopHat (Trapnell et al. Bioinformatics 2009) alignments of K562 cell-line transcriptome. These were used to validate the expression of short peptides idenitified by Mass-Spectrometry in K562 cells.
2012-11-14 | GSE34740 | GEO
Project description:Purpose: The goal of this study is to identify differentially expressed genes in SETDB2 deficient mouse primary hepatocytes (MPH) treated with dexamethasone when compared to control MPH Methods: Total RNA from MPH treated with either shControl or shSETDB2 adenovirus (pool of 3 mice per group) was isolated by the TRIzol method (Life Technologies) according to manufacturer’s instructions. Total RNA was DNase-treated and RNA integrity was evaluated by Biolanalyzer (Agilent). RNA sequencing was performed by the SBP Genomics Core Facility in Lake Nona (Orlando, FL) using the Illumina HiSeq platform and the following parameters: paired-end read length: 100 BP; number of output reads: 300m reads. The Tophat+Cufflinks pipeline was used for the RNA-seq analysis as described previously (Haas et al., 2013; Trapnell et al., 2012). Differentially expressed genes in shSETDB2 vs. shControl MPH were determined using a 2-fold cutoff, p-value of less than 0.05, and FPKM (fragments per kilobase of transcript per million mapped reads) cutoff of 1. The shSETDB2 and shControl RNA-seq reads were mapped to the mm10 genome using Tophat (Trapnell et al., 2009). The mapping results were further analyzed by Cufflinks, which outputs the final differential gene list.
2016-08-28 | GSE85278 | GEO
Project description:The aim of this study was to use NGS RNAseq deep-sequencing in order to characterize the polyadenylated mRNAs and lncRNAs expressed in LNCaP cells treated with androgen hormone compared with untreated LNCaP cells (GSE79301). Trimmed reads were mapped using the hg19 genome with TopHat v.2.0.12 and Bowtie v.2.2.3. The assembly was guided by a custom GTF file created with transcripts fromhuman lncRNA annotations from GENCODE v19 (Harrow, Frankish et al.2012) and those already annotated as lincRNAs in (Cabili, Trapnell et al. 2011; Prensner, Iyer et al. 2011; Hangauer, Vaughn et al. 2013). The diferencial expression was calculated by the sum of exon read count per gene with HTSeq (Anders, Pyl et al. 2015), followed by a DESeq2 analysis (Love, Huber et al. 2014).
2017-12-31 | GSE102305 | GEO
Project description:We examined all transcriptome-level expressions in three initial cell-population densities (862, 1724 and 5172 cells/cm2) in the first two days of differentiation in N2B27. We collected cells in 10-mL tubes and centrifuged them using a pre-cooled centrifuge. We then extracted RNA from each cell-pellet using the PureLink RNA Mini Kit (Ambion, Life Technologies) according to its protocol. We next prepared the cDNA library with the 3′ mRNASeq library preparation kit (Quant-Seq, Lexogen) according to its protocol. We then loaded the cDNA library onto an Illumina MiSeq system using the MiSeq Reagent Kit v3 (Illumina) according to its protocol. We analyzed the resulting RNA-seq data as previously described (Trapnell et al., Nat Protoc 2012). We performed the read alignment using TopHat, read assembly using Cufflinks and analyses of differential gene expression data using Cuffdiff. We used the reference genome for Mus musculus from UCSC (mm10). We performed enrichment analysis of genes based on their FPKM values (e.g., more than 2-fold expressed when two initial population densities are compared) by using GO-terms from PANTHER (Mi et al., Nucl Acids Res 2019) and custom MATLAB script (MathWorks). We visualized results of pre-sorted, Yap1-related genes (LeBlanc et al., Elife 2018; Mugahid et al., Elife 2020; Yu et al., Oncogene 2018; Huh et al., Cells 2019; Zhu et al., Nature Sci Rep 2018; Zhou et al., Int J Mol Sci 2016; Vigneron & Vousden, EMBO J 2012; Kim et al., Cell 2015) into heat maps that displays the normalized expression value (row Z-score) for each gene and each condition.
2020-09-09 | GSE157642 | GEO
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Yin Shen mailto:y7shen@ucsd.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track shows a comprehensive survey of cis-regulatory elements in the mouse genome by using ChIP-seq (Robertson et al., 2007) to identify transcription factor binding sites and chromatin modification profiles in many mouse (C57Bl/6) tissues and primary cells, including bone marrow, cerebellum, cortex, heart, kidney, liver, lung, spleen, mouse embryonic fibroblast cells (MEFs) and embryonic stem (ES) cells. In specific, the Ren lab examined RNA polymerase II (PolII), co-activator protein p300, the insulator protein CTCF, and two chromatin modification marks H3K4me3 and H3K4me1 due to their demonstrated utilities in identifying promoters, enhancers and insulator elements (Barski et al., 2007; Blow et al., 2010; Heintzman et al., 2009; Kim et al., 2007; Kim et al., 2005a; Visel et al., 2009). Enrichment of H3K4me3 or PolII signals is a strong indicator of active promoter, while the presence of p300 or H3K4me1 outside of promoter regions has been used as a mark for enhancers. CTCF binding sites are considered as a mark for potential insulator elements. For each transcription factor or chromatin mark in each tissue, ChIP-seq was carried out with at least two biological replicates. Each experiment produced 20-30 million monoclonal, uniquely mapped tags. 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/ENCODE/protocols/cell/mouse). Enrichment and Library Preparation: Chromatin immunoprecipitation was performed according to Ren Lab ChIP Protocol (http://bioinformatics-renlab.ucsd.edu/RenLabChipProtocolV1.pdf). Library construction was performed according to Ren Lab Library Protocol (http://bioinformatics-renlab.ucsd.edu/RenLabLibraryProtocolV1.pdf). Sequencing and Analysis: Samples were sequenced on Illumina Genome Analyzer II Genome Analyzer IIx, and HiSeq 2000 platforms for 36 cycles. Image analysis, base calling and alignment to the mouse genome version mm9 were performed using Illumina's RTA and Genome Analyzer Pipeline software. Alignment to the mouse genome was performed using ELAND or Bowtie (Langmead et al., 2009) with a seed length of 25 and allowing up to two mismatches. Only the sequences that mapped to one location were used for further analysis. Of those sequences, clonal reads, defined as having the same start position on the same strand, were discarded. BED and wig files were created using custom perl scripts.
2011-07-30 | E-GEOD-31039 | biostudies-arrayexpress
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Yin Shen mailto:y7shen@ucsd.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track shows a comprehensive survey of cis-regulatory elements in the mouse genome by using ChIP-seq (Robertson et al., 2007) to identify transcription factor binding sites and chromatin modification profiles in many mouse (C57Bl/6) tissues and primary cells, including bone marrow, cerebellum, cortex, heart, kidney, liver, lung, spleen, mouse embryonic fibroblast cells (MEFs) and embryonic stem (ES) cells. In specific, the Ren lab examined RNA polymerase II (PolII), co-activator protein p300, the insulator protein CTCF, and two chromatin modification marks, H3K4me3 and H3K4me1, due to their demonstrated utilities in identifying promoters, enhancers and insulator elements (Barski et al., 2007; Blow et al., 2010; Heintzman et al., 2009; Kim et al., 2007; Kim et al., 2005a; Visel et al., 2009). Enrichment of H3K4me3 or PolII signals is a strong indicator of an active promoter, while the presence of p300 or H3K4me1 outside of promoter regions has been used as a mark for enhancers. CTCF binding sites are considered as a mark for potential insulator elements. For each transcription factor or chromatin mark in each tissue, ChIP-seq was carried out with at least two biological replicates. Each experiment produced 20-30 million monoclonal, uniquely mapped tags. 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://genome.ucsc.edu/ENCODE/protocols/cell/mouse). Enrichment and Library Preparation Chromatin immunoprecipitation was performed according to Ren Lab ChIP Protocol (http://bioinformatics-renlab.ucsd.edu/RenLabChipProtocolV1.pdf). Library construction was performed according to Ren Lab Library Protocol (http://bioinformatics-renlab.ucsd.edu/RenLabLibraryProtocolV1.pdf). Sequencing and Analysis Samples were sequenced on Illumina Genome Analyzer II, Genome Analyzer IIx and HiSeq 2000 platforms for 36 cycles. Image analysis, base calling and alignment to the mouse genome version mm9 were performed using Illumina's RTA and Genome Analyzer Pipeline software. Alignment to the mouse genome was performed using ELAND or Bowtie (Langmead et al., 2009) with a seed length of 25 and allowing up to two mismatches. Only the sequences that mapped to one location were used for further analysis. Of those sequences, clonal reads, defined as having the same start position on the same strand, were discarded. BED and wig files were created using custom perl scripts.
2012-04-19 | E-GEOD-36027 | biostudies-arrayexpress
Project description:RNA-seq was performed using RNA extracted from blasted CD4+ T cells from individuals with TCF3 (haploinsufficiency [HI], null, or dominant negative [DN] mutations and healthy controls for the following study groups: healthy controls (HC, n=4) and patients with TCF3 mutations (HI=4, null=1, DN=1 ). Libraries were prepared using the AmpliSeq for Illumina Transcriptome Human Gene Expression panel. RNA-seq was performed on the Illumina HiSeq 2500 (Illumina; AmpliSeq for Illumina/HiSeq 2500). Demultiplexed reads were mapped to the hg19 genome using the splice-aware aligner Tophat (Trapnell et al., 2009). Gene-level counts data were generated using the Rsubread feature counts a read summarization program that counts mapped reads for genomic features such as genes (Liao et al., 2019). Differential expression analysis was performed using R (v.3.5.3) and DESeq2 (v.1.22.2)(Love et al., 2014).
2022-12-01 | GSE218787 | GEO
Project description:Aim: Identification of long noncoding RNAs using transcriptional analysis of e15.5 mouse embryonic pancreas and adult mouse islets Methods: rRNA-depleted total RNA from e15.5 embryonic mouse pancreata and 12-week-old mouse islets was prepared using the Ribo-Zero rRNA removal kit. Biological replicates indicate that each RNA-seq data set was generated from individual mice (islets) or 3-4 pooled e15.5 embryonic pancreata. Libraries were prepared with Illumina TruSeq RNA sample preparation kit and then sequenced with 60 million, 2 x 100 paired reads on an Illumina HiSeq 2000 V3 instrument. To reconstruct the transcriptomes, we first mapped all reads of total RNAs to the mouse reference genome (mm10) with TopHat v1.3.2 (Trapnell et al., 2012). Cufflinks (v1.2.1) was subsequently applied to assemble the whole transcriptome and to identify all possible transcripts. Significantly differentially expressed genes were calculated using DEseq2.
2019-10-10 | GSE122033 | GEO
Project description:Purpose: Identify Top2b-dependent genes by comparing retinal transcriptome profiling (RNA-seq) Methods: Retinal mRNA profiles of neonatal (postnatal day 0 and 6) wild-type (WT) and Topoisomerase IIbeta (Top2b) conditional knockout (Top2b−/−, or cKO) mice were generated using the SOLiD System (Applied Biosystems). Data analysis was performed according to published protocols (Trapnell et al., 2012) with minor modifications. Briefly, colorspace data of raw 50 bp reads was aligned to the mouse genome (mm9 or GRCm37) using Bowtie (Langmead et al., 2009). Gene expression levels were analyzed with Cufflinks, with differentially expressed genes determined by Cuffdiff (Trapnell et al., 2010). Results: After sequencing, ~120 million 50 bp reads for each time point and each group were obtained and mapped to the mouse genome (MGI, as of Feb 15, 2012). Over 62,000 transcripts were identified; these include ~22,000 annotated genes (including alternative-spliced variants) which cover 76% of the whole mouse genome. Among all the annotated genes, 8.80% (1,935/22,000) for P0 and 1.25% (274/22,000) for P6 showed differential expression between the control and cKO samples (p-value ≤ 0.05, q-value ≤ 0.05). Conclusions: All genes with differential expression levels in Top2b cKO samples are direct or indirect dependent to Top2b.
2017-02-12 | GSE86187 | GEO
Project description:RNA-seq was performed using RNA extracted from enriched T cell blasts (CD3 T cells were enriched from the T-cell blasts, purity >90%, Stemcell Technologies, 19051) or naïve B cells (purity >90%, Stemcell technologies, 19254) for the following study groups: healthy controls (HC, n=3-4) and patients with AIOLOS N160S (n=1 for naïve B cells [A.III.1] and n=2 for T cell blasts [A.II.2 and A.III.1]). Libraries were prepared using the AmpliSeq for Illumina Transcriptome Human Gene Expression panel, which captures 20,802 genes (>95% of human RefSeq genes). RNA-seq was performed on the Illumina HiSeq 2500 (Illumina; AmpliSeq for Illumina/HiSeq 2500). Demultiplexed reads were mapped to the hg19 genome using the splice-aware aligner Tophat (Trapnell et al., 2009). Gene-level counts data were generated using the Rsubread feature counts a read summarization program that counts mapped reads for genomic features such as genes(Liao et al., 2019). Differential expression analysis was performed using R (v.3.5.3) and DESeq2 (v.1.22.2)(Love et al., 2014).
2021-09-14 | GSE183966 | GEO