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accession-icon GSE25127
Ewing Sarcoma cell lines treated with mithramycin
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The study aims to define gene expression changes associated with mithramycin treatment of Ewing Sarcoma cell lines.

Publication Title

Identification of an inhibitor of the EWS-FLI1 oncogenic transcription factor by high-throughput screening.

Sample Metadata Fields

Cell line, Treatment

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accession-icon SRP164953
Selective Disruption of Core Regulatory Transcription [RNA-seq]
  • organism-icon Homo sapiens
  • sample-icon 53 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

Activation of identity determining transcription factors (TFs), or core regulatory TFs, is governed by cell-type specific enhancers, an important subset of these being super enhancers (SEs). This mechanism is distinct from constitutive expression of housekeeping genes. The characterization of drug-like small molecules to selectively inhibit core regulatory circuitry is of high interest for treatment of cancers, which are addicted to core regulatory TF function at SEs. Surprisingly, we find histone deacetylases (HDAC) to be an indispensable component of SE-driven transcription. While histone acetylation is a marker for active genes, over accumulation of acetylation selectively halts core regulatory transcription. We show this conundrum may in part be explained by a SE-specific need for resetting histones to maintain SE boundaries, to facilitate enhancer-promoter looping and high levels of transcription. Overall design: RNA-seq data for FP-RMS cells treated with various concentrations of various small molecules modulators of epigenetic processes.

Publication Title

Chemical genomics reveals histone deacetylases are required for core regulatory transcription.

Sample Metadata Fields

Specimen part, Cell line, Treatment, Subject

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accession-icon GSE11103
Study of human immune and memory T cells using microarray
  • organism-icon Homo sapiens
  • sample-icon 39 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus.

Sample Metadata Fields

Specimen part, Disease

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accession-icon GSE11057
Memory T Cell Subsets
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Microarray deconvolution is a technique for quantifying the relative abundance of constituent cells in a mixture based on that mixture's microarray signature and the signatures of the purified constituents. It has been applied to yeast and other systems but not to blood samples.

Publication Title

Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus.

Sample Metadata Fields

Specimen part, Disease

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accession-icon GSE11058
Immune Cell Line Mixtures
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Microarray deconvolution is a technique for quantifying the relative abundance of constituent cells in a mixture based on that mixture's microarray signature and the signatures of the purified constituents. Its ability to discriminate related human cells is unknown.

Publication Title

Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP007823
Dynamic Transformations of Genome-wide Epigenetic Marking and Transcriptional Control Establish T Cell Identity [RNA-Seq]
  • organism-icon Mus musculus
  • sample-icon 11 Downloadable Samples
  • Technology Badge IconIllumina Genome Analyzer II

Description

T cell development comprises a stepwise process of commitment from a multipotent precursor. To define molecular mechanisms controlling this progression, we probed five stages spanning the commitment process using deep sequencing RNA-seq and ChIP-seq methods to track genome-wide shifts in transcription, cohorts of active transcription factor genes, histone modifications at diverse classes of cis-regulatory elements, and binding patterns of GATA-3 and PU.1, transcription factors with complementary roles in T-cell development. The results locate potential promoter-distal cis-elements in play and reveal both activation sites and diverse mechanisms of repression that silence genes used in alternative lineages. Histone marking is dynamic and reversible, and while permissive marks anticipate, repressive marks often lag behind changes in transcription. In vivo binding of PU.1 and GATA-3 relative to epigenetic marking reveals distinctive, factor-specific rules for recruitment of these crucial transcription factors to different subsets of their potential sites, dependent on dose and developmental context. Overall design: Genome-wide expression profiles, global distributions of three different histone modifications, and global occupancies of two transcription factors were examined in five developmentally related immature T populations. High throughput sequencing generated on average 9-30 million of mappable reads (single-read) for each ChIP-seq sample, and 10-15 million (single-read) for RNA-seq. Independent biological replicates were analyzed for individual populations. Terminology: FLDN1_RNA-seq_sample1 and FLDN1_RNA-seq_sample2 are independent biological replicates for the same cell type.

Publication Title

Dynamic transformations of genome-wide epigenetic marking and transcriptional control establish T cell identity.

Sample Metadata Fields

Specimen part, Cell line, Subject

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accession-icon GSE42892
Microarray Analysis of a Familial Hypertrophic Cardiomyopathy Mouse Model Rescued by a Phospholamban Knockout
  • organism-icon Mus musculus
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Familial hypertrophic cardiomyopathy (FHC) is a disease characterized by ventricular hypertrophy, fibrosis, and aberrant systolic and/or diastolic function. Our laboratories have previously developed 2 mouse models that affect cardiac performance. One transgenic mouse model encodes an FHC-associated mutation in -tropomyosin (Tm180) that displays severe cardiac hypertrophy with fibrosis and impaired physiological performance. The other model was a gene knockout of phospholamban (PLB), a regulator of calcium uptake in the sarcoplasmic reticulum of cardiomyocytes; the hearts of these mice exhibit hypercontractility with no pathological abnormalities. Previous work in our laboratories show that the hearts of mice that were genetically crossed between the Tm180 and PLB KO mice rescues the hypertrophic phenotype and improves their cardiac morphology and function.

Publication Title

Microarray analysis of active cardiac remodeling genes in a familial hypertrophic cardiomyopathy mouse model rescued by a phospholamban knockout.

Sample Metadata Fields

Age, Specimen part

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accession-icon SRP018838
Single Cell RNA-Seq
  • organism-icon Homo sapiens
  • sample-icon 62 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2000

Description

RNA-seq transcriptome measurements are typically performed by isolating RNA from large numbers of cells in culture or tissues. While highly informative, such experiments mask the variability in gene expression patterns that exists between individual cells. To gain insight into the dynamics of gene expression on the level of single-cells, we have carried out the transcriptomes of single-cells from the GM12878 cell line using RNA-seq. Overall design: Single GM12878 cells were picked and RNA-seq libraries were generated using the SMART-seq protocol. We also carried out RNA-seq experiments on pools of 10, 30 and 100 cells, on 100pg and 10ng of total RNA, and on pools of 10 cells that were subsequently split into 10 separate sample and processed as if they were single cells in order to assess technical variation in our experiments.

Publication Title

From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP038871
Noncoding RNA transcriptome analysis during cellular reprogramming
  • organism-icon Mus musculus
  • sample-icon 119 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500, Illumina HiSeq 2000

Description

We report the application of single-cell and bulk RNA sequencing technology to examine the noncoding transcriptome of cells undergoing reprogramming to the pluripotent state. Overall design: Examination of noncoding RNAs in reprogrammming cells. We examined iPS cells grown in standard ES cell culture conditions, as well as iPS cells grown in "2i" conditions (small molecule inhibition of Mek and Gsk3). We also compared our iPS samples to male and female ES cells (mES, fES).

Publication Title

Single-cell transcriptome analysis reveals dynamic changes in lncRNA expression during reprogramming.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE60489
Global heart transcript data from fasted male BXD strains on chow or high fat diet
  • organism-icon Mus musculus
  • sample-icon 79 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

Transcript data from heart tissue from fasted-state male BXD strains on chow or high fat diet

Publication Title

Quantifying and Localizing the Mitochondrial Proteome Across Five Tissues in A Mouse Population.

Sample Metadata Fields

Specimen part, Treatment

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...

refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Developed by the Childhood Cancer Data Lab

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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