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accession-icon SRP078248
Adrenalectomy plus corticosterone treatment, rat hippocampal RNA-seq
  • organism-icon Rattus norvegicus
  • sample-icon 12 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Male Sprague-Dawley rats 8 weeks old, were adrenalectomized, treated with 300ug/kg corticosterone or vehicle 3 days after surgery then sacrificed 1 hour later. Hippocampi were removed and RNA extracted and processed for sequencing at the Massachusetts General Hospital Nex-Generation Sequening Core. Overall design: Includes 6 cort treated and 6 control biological replicates

Publication Title

Stress and corticosteroids regulate rat hippocampal mitochondrial DNA gene expression via the glucocorticoid receptor.

Alternate Accession IDs

GSE84249

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE76880
Expression data from human 3D skin models in response to IL-31 treatment
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Atopic dermatitis, a chronic inflammatory skin disease with increasing prevalance, is closely associated with skin barrier defects. A cytokine related to disease severity and inhibition of keratinocyte differentiation is IL-31. To identify its molecular targets, IL-31-dependent gene expression was determined in 3-dimensional organotypic skin models.

Publication Title

Control of the Physical and Antimicrobial Skin Barrier by an IL-31-IL-1 Signaling Network.

Alternate Accession IDs

E-GEOD-76880

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE33923
2C::tomato ES cells, 2-cell embryos and wild type oocytes
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Embryonic stem cell potency fluctuates with endogenous retrovirus activity.

Alternate Accession IDs

E-GEOD-33923

Sample Metadata Fields

Specimen part, Cell line, Treatment

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accession-icon GSE33763
Expression data from 2C::tomato+ vs 2C::tomato - ES cells
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

We compared gene expression from 2C::tomato+/- ES cells from Kdm1a wt and mutant ES cultures

Publication Title

Embryonic stem cell potency fluctuates with endogenous retrovirus activity.

Alternate Accession IDs

E-GEOD-33763

Sample Metadata Fields

Cell line

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accession-icon SRP009468
RNA-Seq from two-cell (2C) stage embryos
  • organism-icon Mus musculus
  • sample-icon 3 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

To determine gene expression in 2 cell stage embryos Overall design: 3 Replicates of litters of wild type 2 cell stage embryos

Publication Title

Embryonic stem cell potency fluctuates with endogenous retrovirus activity.

Alternate Accession IDs

GSE33921

Sample Metadata Fields

Specimen part, Subject

View Samples
accession-icon SRP011988
RNA-Seq from wt and G9A knockout ES cells
  • organism-icon Mus musculus
  • sample-icon 2 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

To measure gene expression difference between wt and g9A knockout ES cells Overall design: G9A TT2 ES cells (Yokochi et al) were treated with Veh. Or 4OHT (to delete G9A)

Publication Title

Embryonic stem cell potency fluctuates with endogenous retrovirus activity.

Alternate Accession IDs

GSE36896

Sample Metadata Fields

Specimen part, Treatment, Subject

View Samples
accession-icon SRP009467
mRNA-Seq of 2C::tomato+ vs. - ES cells
  • organism-icon Mus musculus
  • sample-icon 2 Downloadable Samples
  • Technology Badge IconIllumina Genome Analyzer II

Description

We identified/quantified genes and repeat elements enriched within 2C::tomato+ cells vs. 2C::tomato - cells Overall design: 2C::tomato + and - cells were collected by FACS for RNA-Seq analysis

Publication Title

Embryonic stem cell potency fluctuates with endogenous retrovirus activity.

Alternate Accession IDs

GSE33920

Sample Metadata Fields

Specimen part, Subject

View Samples
accession-icon GSE55457
Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation [Jena]
  • organism-icon Homo sapiens
  • sample-icon 32 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendls statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.

Publication Title

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.

Alternate Accession IDs

E-GEOD-55457

Sample Metadata Fields

Sex, Age

View Samples
accession-icon GSE55235
Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation
  • organism-icon Homo sapiens
  • sample-icon 29 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendls statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.

Publication Title

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.

Alternate Accession IDs

E-GEOD-55235

Sample Metadata Fields

Specimen part, Disease, Disease stage

View Samples
accession-icon GSE55584
Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation [Leipzig]
  • organism-icon Homo sapiens
  • sample-icon 15 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendls statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for RA), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.

Publication Title

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.

Alternate Accession IDs

E-GEOD-55584

Sample Metadata Fields

Sex, Age

View Samples

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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