0c903f58-2e89-4994-932f-a802828f8767 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001008499 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:The dataset includes 6 FASTQ files with single cell transcriptome sequencing data of normal breast myoepithelial cells from ducts and TDLUs derived from reduction mammoplasties from three patients. Chromium Single Cell 3’ Reagent Kit v2 or v3 (10x Genomics) were used for processing of cells, whereafter sequencing was performed using the Illumina® NextSeq500/550 High Output Kit v2. Cell Ranger was used for generating FASTQ files and files from different lanes were concatenated prior to uploading the data to EGA.
| EGAD00001008468 | EGA
Project description:Temporal analysis of Irf4 and PU.1 genome binding during B cell activation and differentiation in vitro using antigen (NP-Ficoll) CD40L and IL-2/4/5 cytokines (see Molecular Systems Biology 7:495 for details of cellular system). The results provide insight in the target genes and binding specificity of IRF4 and PU.1 during coordination of different programs of B cell differentiation. Regrettably three of the FASTQ raw sequence files in our study were corrupted during storage. FASTQ data from our experimental and control groups are available for download via GEO SRA; however, two groups are missing select raw sequence files. These include one PU.1 Day 3 group file (Sample GSM1133499) and two of four input files used to generate a concatenated “super” input file (Sample GSM1133490); the raw data provided for input consists of the two input files recovered. Importantly, FASTA sequences for both of these datasets are available as supplementary data through GEO, and we can make available upon request (rsciamma@uchicago.edu) all files in our study in the ELAND-extended alignment format. Please note that GEO no longer supports this format.
2013-05-04 | GSE46607 | GEO
Project description:We perform 10X Genomics scRNA-Seq on P2, P21, P2 + 7 days post wound, P21 + 7 days post wound, with 3 bioreplicates for each condition. Fastq files and output folders were generated from Cellranger pipeline. Loom files were generated using Velocyto.py for downstream analysis. Chemistry: V2
2020-07-01 | GSE153596 | GEO
Project description:5000-10000 HSCs (KL CD150+ CD48- CD34-) were sorted into lysis buffer from the pools of naive or 1-month infected WT or Dnmt3a-/- mice (n=10-12 per group). RNA was isolated with the NucleoSpin ® RNA Plus XS kit (Macherey Nagel). RNA-seq libraries were prepared by using SMARTer® Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Takara Bio Usa). Illumina NovaSeq SP was used for sequencing with a paired-end sequencing length of 10bp. FASTQ files were preprocessed using HTS stream (https://github.com/ibest/HTStream) and the clean FASTQ file were aligned using STAR. Differential expression (DE) analysis of gene expression was performed using Limma-Voom. False discovery rate (FDR)<0.05 was considered statistically significant. We performed gene ontology analysis for differentially expressed genes with q value <0.05.
2021-03-23 | GSE169352 | GEO
Project description:The transcription factor IRF4 regulates immunoglobulin class switch recombination and plasma cell differentiation. Its differing concentrations appear to regulate mutually antagonistic programs of B and plasma cell gene expression. We show IRF4 to be also required for generation of germinal center (GC) B cells. Its transient expression in vivo induced the expression of key GC genes including Bcl6 and Aicda. In contrast, sustained and higher concentrations of IRF4 promoted the generation of plasma cells while antagonizing the GC fate. IRF4 cobound with the transcription factors PU.1 or BATF to Ets or AP-1 composite motifs, associated with genes involved in B cell activation and the GC response. At higher concentrations, IRF4 binding shifted to interferon sequence response motifs; these enriched for genes involved in plasma cell differentiation. Our results support a model of "kinetic control" in which signaling-induced dynamics of IRF4 in activated B cells control their cell-fate outcomes. Regrettably three of the FASTQ raw sequence files in our study were corrupted during storage. FASTQ data from our experimental and control groups are available for download via GEO SRA; however, two groups are missing select raw sequence files. These include one PU.1 Day 3 group file (Sample GSM1133499) and two of four input files used to generate a concatenated “super” input file (Sample GSM1133490); the raw data provided for input consists of the two input files recovered. Importantly, FASTA sequences for both of these datasets are available as supplementary data through GEO, and we can make available upon request (rsciamma@uchicago.edu) all files in our study in the ELAND-extended alignment format. Please note that GEO no longer supports this format.
2013-05-04 | GSE46608 | GEO
Project description:Transcriptomic profile of human adipose tissue progenitor cells was performed as follows. For AmpliSeq transcriptome sequencing library construction, AmpliSeq™ Library PLUS, AmpliSeq Transcriptome Human Gene Expression Panel and AmpliSeq CD indexes SetA kits were purchased from Illumina and sequencing libraries were constructed as described in AmpliSeq for Illumina Transcriptome Human Gene Expression Panel reference guide (Illumina). Equimolar concentrations of libraries were pooled at 4 nM and denatured and diluted as described in Denature and Dilute Libraries Guide (Illumina) and adjusted to final concentration of 1.4 pM. Resulting library was sequenced on NextSeq 500 using NextSeq 500/550 High Output v2 kit with 2 X 151 bp cycle. Generated raw files were converted to FASTQ files and used for data analysis. AmpliSeq transcriptome FASTQ files were analyzed on Array studio V10.0 (Omicsoft, Qiagen). Following raw read QC, first and last 10 bases were trimmed and mapped to reference genome Human.B38. The read count data was generated using GeneModel RefGene20170606. Resulting data was normalized by DESeq package, transformed to log2 value and used for ANOVA analyses.
2023-01-12 | GSE222749 | GEO
Project description:<p>To determine IL-17-induced global transcriptome changes in midbrain neurons derived from induced pluripotent stem cells (iPSC) from three sporadic Parkinson's disease (PD) patients and three age- and sex-machted controls, deep RNA sequencing (RNA-Seq) of IL-17-treated and untreated PD and control iPSC-dderived midbrain neurons was performed. Total RNA was isolated from untreated and IL-17-treated cells with the TruSeq RNA Sample Preparation Kit v2 (Illumina). RNA libraries were quantified using the KAPA SYBR FAST ABI Prism Library Quantification Kit (Kapa Biosystems) and cluster generation was performed on the cBot with the TruSeq SR Cluster Kit v3 (Illumina). The sequencing run was performed on a HiSeq 1000 instrument (Illumina) using the indexed, 50 cycles single read (SR) protocol and the TruSeq SBS v3 Kit (Illumina). Image analysis and base calling resulted in .bcl files that were then converted into .fastQ files by the CASAVA1.8.2 software. FastQ files were aligned to the human genome (hg19) using STAR.and annotated with gencode.v19. DESeq2 was used to determine differential expression. Criteria to determine significantly dysregulated genes were as follows: adjusted p-value below 0.05 and log2FC (fold change) of greater than one. Only genes with a mean expression value of greater than one RPKM (reads per kilobase per million mapped reads) throughout the dataset were considered. Control and PD samples were analyzed as two independent datasets.</p> <p>Upon IL-17 treatment only 17 genes were found to be dysregulated in controls but 125 genes were dysregulated in iPSC-derived midbrain neurons from PD patients. The 125 IL-17-dependent genes in PD iPSC-derived neurons separated the treated from untreated PD samples using an unsupervised, hierarchical clustering applying an Euclidean distance metric.</p> <p>More detailed study information can be found in Sommer A, Maxreiter F, Krach F, Fadler T, Grosch J, Maroni M, Graef D, Eberhardt E, Riemenschneider MJ, Yeo GW, Kohl Z, Xiang W, Gage FH, Winkler J, Prots I, Winner B. Th17 Lymphocytes Induce Neuronal Cell Death in a Human iPSC-Based Model of Parkinson's Disease. Cell Stem Cell. 2018 Jul 5;23(1):123-131.e6. doi: 10.1016/j.stem.2018.06.015. PMID: 29979986</p>
| phs001686 | dbGaP
Project description:Temporal analysis of Irf4 and PU.1 genome binding during B cell activation and differentiation in vitro using antigen (NP-Ficoll) CD40L and IL-2/4/5 cytokines (see Molecular Systems Biology 7:495 for details of cellular system). The results provide insight in the target genes and binding specificity of IRF4 and PU.1 during coordination of different programs of B cell differentiation. Regrettably three of the FASTQ raw sequence files in our study were corrupted during storage. FASTQ data from our experimental and control groups are available for download via GEO SRA; however, two groups are missing select raw sequence files. These include one PU.1 Day 3 group file (Sample GSM1133499) and two of four input files used to generate a concatenated “super” input file (Sample GSM1133490); the raw data provided for input consists of the two input files recovered. Importantly, FASTA sequences for both of these datasets are available as supplementary data through GEO, and we can make available upon request (rsciamma@uchicago.edu) all files in our study in the ELAND-extended alignment format. Please note that GEO no longer supports this format. Resting mature peripheral primary B cells were enriched from the spleens of B1-8i (anti-NP gene targeted) mice. We sought to compare the genome-binding landscape of Irf4 and PU.1 prior to differentiation yet after B cell activation (Day 1) and after B cell differentiation (Day 3) of activated B cells into plasma cells (see Molecular Systems Biology 7:495 for description of cellular system). To this end, we used ChIP-seq (using the Illumina GA2 system) to obtain millions of unbiased, genome-wide, binding events. Sequences were mapped to the reference genome (mm9) and enrichment was calculated, relative to an Input sample, using QuEST algorithms.
2013-05-04 | E-GEOD-46607 | biostudies-arrayexpress