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SRP078248
Rattus norvegicus
12 Downloadable Samples
Illumina 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.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 8 Downloadable Samples
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.
Sample Metadata Fields
Sex, Specimen part
View Samples- Mus musculus
- 6 Downloadable Samples
- 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.
Sample Metadata Fields
Specimen part, Cell line, Treatment
View Samples- Mus musculus
- 6 Downloadable Samples
- 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.
Sample Metadata Fields
Cell line
View Samples- Mus musculus
- 3 Downloadable Samples
- Illumina 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.
Sample Metadata Fields
Specimen part, Subject
View Samples- Mus musculus
- 2 Downloadable Samples
- Illumina 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.
Sample Metadata Fields
Specimen part, Treatment, Subject
View Samples- Mus musculus
- 2 Downloadable Samples
- Illumina 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.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 32 Downloadable Samples
- 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.
Sample Metadata Fields
Sex, Age
View Samples- Homo sapiens
- 29 Downloadable Samples
- 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.
Sample Metadata Fields
Specimen part, Disease, Disease stage
View Samples- Homo sapiens
- 15 Downloadable Samples
- 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.
Sample Metadata Fields
Sex, Age
View Samples