Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR5151140.fastq.gz | Fastqsanger.gz | |||
| SRR5151141.fastq.gz | Fastqsanger.gz | |||
| SRR5151142.fastq.gz | Fastqsanger.gz | |||
| SRR5151143_1.fastq.gz | Fastqsanger.gz | |||
| SRR5151143_2.fastq.gz | Fastqsanger.gz |
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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:Adapter trimmed fastq files, non-redundant identical reads and the results of their alignment using the TIGER pipeline.
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Project description:FastQ and alignment BAM files for MARC ONT bridging paper.
| PRJEB18955 | ENA
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Florencia Pauli mailto:fpauli@hudsonalpha.org). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). The ChIP-Seq method was used to assay chromatin fragments bound by specific or general transcription factors as described below. DNA isolated by ChIP-Seq was size-selected (~225 bp) and sequenced. Short reads of 25-36 bp were mapped to the human reference genome, and enriched regions of high read density relative to a total input chromatin control reads were identified. The sequence reads with quality scores (fastq files) and alignment coordinates (BAM files) from these experiments 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
2011-09-29 | GSE32465 | GEO
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
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:This data set contains ChEC-seq binding profiles of various TF in yeast strains deleted of other TFs. Each sample has a pair-end sequencing file and a processed file (.out) is a genomic signal track after alignment to S.cerevisiae (R64) reference genome. Mapping was done using the read end. This dataset also contains raw and processed MNase-seq data files for nucleosome occupancy. Data related to manuscript: The architecture of binding cooperativity between densely bound transcription factors.
2023-08-01 | GSE222268 | GEO
Project description:Spatially resolved gene expression was prepard by dissociated hman prostate tissue to single cells, and collected & prepped for RNA-seq using the Visium Spatial Gene Expression kit. 5000 cells were collected and sequenced at a depth of 50k cells/gene on a 2X150nt lane in a NovaSeq 6000. SpaceRanger alignment was performed to produce the RAW files
2021-01-19 | GSE159697 | GEO
Project description:Chromatin State Profilining using multiple histone modifications in human craniofacial tissue spanning 4.5 post conception weeks to 10 pcw The raw FASTQ sequence files are being deposited in dbGAP
2017-05-04 | GSE97752 | GEO
Project description:High-throughput sequencing of cDNA prepared from RNA, an approach known as RNA-seq, is coming into increasing use as a method for transcriptome analysis. Despite its many advantages, widespread adoption of the technique has been hampered by a lack of easy-to-use, integrated, open source tools for analyzing the nucleotide sequence data that are generated. Here we describe Xpression, an integrated tool for processing prokaryotic RNA-seq data. The tool is easy to use and is fully automated. It performs all essential processing tasks including nucleotide sequence extraction, alignment, quantification, normalization and visualization. Importantly, Xpression supports multiplexed and strand-specific nucleotide sequence data. It extracts and trims specific sequences from files and separately quantifies sense and antisense reads in the final results. Outputs from the tool can also be conveniently used in downstream analysis. In this paper, we show the utility of Xpression to process strand-specific RNA-seq data to identify genes regulated by CouR, a transcription factor that controls p-coumarate degradation by the bacterium Rhodopseudomonas palustris.
2012-09-01 | GSE39025 | GEO