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accession-icon GSE18377
Gene expression profiling of human DLBCL tumor samples (FF and FFPE pairs)
  • organism-icon Homo sapiens
  • sample-icon 35 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

We profiled human DLBCL tumor samples (FF and FFPE matched pairs) to identify the transcripts which are less prone to degradation in FFPE

Publication Title

CD40 pathway activation status predicts response to CD40 therapy in diffuse large B cell lymphoma.

Sample Metadata Fields

Specimen part

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accession-icon GSE18376
Gene expression profiling of human DLBCL tumor samples (SGN-40 trial)
  • organism-icon Homo sapiens
  • sample-icon 23 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

We profiled human DLBCL patient samples to discover predictive biomarkers

Publication Title

CD40 pathway activation status predicts response to CD40 therapy in diffuse large B cell lymphoma.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE16149
Examining smoking-induced differential gene expression changes in buccal mucosa
  • organism-icon Homo sapiens
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

A tissue like buccal mucosa (from cheek swabs) would be an ideal sample material for rapid, easy collection for testing of biomarkers as an alternative to blood. A limited number of studies, primarily in the smoker/oral cancer literature, address this tissue's efficacy for quantitative PCR or microarray gene expression analysis. In this study both qPCR and microarray analyses were used to evaluate gene expression in buccal cells. An initial study comparing blood and buccal cells from the same individuals looked at relative amounts of four genes. The RNA isolated from buccal cells was degraded but was of sufficient quality to be used with RT-qPCR to detect expression of specific genes. Second, buccal cell RNA was used for microarray-based differential gene expression studies by comparing gene expression between smokers and nonsmokers. The isolation and amplification protocol allowed use of 150-fold less buccal cell RNA than had been reported previously with human microarrays. We report here the finding of a small number of significant gene expression differences between smokers and nonsmokers, using buccal cells as target material. Additionally, Gene Set Enrichment Analysis confirmed that these genes were changing expression in the same pattern as seen in an earlier buccal cell study performed by another group. Our results suggest that in spite of a high degree of RNA degradation, buccal cells from cheek mucosa could be used to detect differential gene expression between smokers and nonsmokers. However the RNA degradation, increase in sample variability and microarray failure rate show that buccal samples should be used with caution as source material in expression studies.

Publication Title

Examining smoking-induced differential gene expression changes in buccal mucosa.

Sample Metadata Fields

Specimen part

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accession-icon GSE20489
Microarray characterization of gene expression changes in blood during acute ethanol exposure
  • organism-icon Homo sapiens
  • sample-icon 51 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

As part of the civil aviation safety program to define the adverse effects of ethanol on flying performance, we present results of our DNA microarray analysis of samples from a timecourse study of individuals given ethanol orally, and then evaluated by breathalyzer to monitor blood alcohol content (BAC). At five blood alcohol levels, T1-T5, blood was drawn such that the samples represented 0%, 0.04%, 0.08% BAC, and return to 0.04%, and 0.02% BAC. Microarray analysis showed that changes in gene expression could be detected across the time-course. We verified these expression changes by quantitative polymerase chain reaction (qPCR). Candidate target genes identified from the microarray analysis were clustered by expression change pattern, examined for shared functions and functional network membership. Five coordinately expressed groups were revealed and functional analysis showed shared transcription factor binding sites and functions for members of the clusters. These functions include protein synthesis and modification, expected for changes in gene expression, hematological and immune functions, expected for a blood sample, and pancreatic and hepatic function, expected as response to ethanol. The results provide a first look at changing gene expression patterns in blood during acute increase of ethanol concentration and its depletion due to metabolism or excretion and demonstrate that it is possible to detect significant changes in gene expression using total RNA isolated from whole blood. The analysis approach for this study can be utilized as part of a workflow to identify target genes by timecourse changes in gene expression that may affect pilot performance.

Publication Title

Microarray characterization of gene expression changes in blood during acute ethanol exposure.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE61092
Expression data from the commensal bacteria Escherchia coli strain HB101 interacting with Caenorhabditis elegans and/or Giardia duodenalis
  • organism-icon Escherichia coli
  • sample-icon 11 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

Homeostatic interactions between the host and its resident microbiota is important for normal physiological functions and if altered, it could lead to dysbiosis, a change in the structure and function of the microbiota, and as a result to various pathophysiologies. Altered structure in bacterial community is associated with various pathophysiologies, but we are just beginning to understand how these structural changes translate into functional changes. Environmental factors including pathogenic infections can lead to altered interactions between the host and its resident microbiota.

Publication Title

Giardia duodenalis-induced alterations of commensal bacteria kill Caenorhabditis elegans: a new model to study microbial-microbial interactions in the gut.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE93664
Comparison of the transcriptomic profile of P. falciparum reactive polyfunctional and IFNg monofunctional human CD4 T cells
  • organism-icon Homo sapiens
  • sample-icon 3 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.0 ST Array (hugene20st)

Description

Pathogen-specific polyfunctional T cell responses have been associated with favorable clinical outcomes but it is not known whether polyfunctional T cells are distinct from monofunctional cytokine producing T cells. In this study we compared the transcriptomic profile of P. falciparum reactive polyfunctional and IFNg monofunctional CD4 T cells by microarray analysis and show that polyfunctional CD4 T cells are associated with a unique transcriptomic signature.

Publication Title

Polyfunctional and IFN-<b>γ</b> monofunctional human CD4<sup>+</sup> T cell populations are molecularly distinct.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE70881
Expression analysis of draculin (drl) expressing cells in embryonic zebrafish
  • organism-icon Danio rerio
  • sample-icon 5 Downloadable Samples
  • Technology Badge Icon Zebrafish Gene 1.0 ST Array (zebgene10st)

Description

drl expression initiates during gastrulation and condenses as a band of cells at the prospective lateral embryo margin. In late epiboly, drl:EGFP is detectable as a band of scattered EGFP-fluorescent cells; after gastrulation, drl:EGFP-positive cells coalesce at the embryo margin that then in somitogenesis break down into the anterior and posterior lateral plate with subsequent cell migrations that form the posterior vascular/hematopoietic stripes and the anterior cardiovascular and myeloid precursors.

Publication Title

Chamber identity programs drive early functional partitioning of the heart.

Sample Metadata Fields

Age, Specimen part

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accession-icon GSE28540
Activity of SigB Modulates Virulence Gene Expression in a Murine Staphylococcus aureus Infection Model but Does Not Influence the Host Kidney Gene Expression
  • organism-icon Mus musculus
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Background. Infections caused by Staphylococcus aureus are associated with significant morbidity and mortality and are an increasing threat not only in hospital settings. The expression of the staphylococcal virulence factor repertoire is known to be affected by the alternative sigma factor B (SigB). However, its impact during infection still is a matter of debate. Methods. Kidney tissue of controls or mice infected with S. aureus HG001 or its isogenic sigB mutant was analyzed by transcriptome profiling to monitor the host response, and additionally expression of selected S. aureus genes was monitored by RT-qPCR. Results. Direct transcript analysis by RT-qPCR revealed significant SigB activity in all mice infected with the wild type strain (WT), but not in its isogenic sigB mutant (p<0.0001). Despite a clear cut difference in the SigB-dependent transcription pattern of virulence genes (clfA, aur, and hla), the host reaction to infection (either WT or sigB mutant) was almost identical. Conclusions. Despite its significant activity in vivo, loss of SigB did not have an effect on the outcome of infection as well as on murine kidney gene expression pattern. Thus, these data support the role of SigB as virulence modulator rather than being a virulence determinant by itself.

Publication Title

The alternative sigma factor B modulates virulence gene expression in a murine Staphylococcus aureus infection model but does not influence kidney gene expression pattern of the host.

Sample Metadata Fields

Sex, Specimen part

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accession-icon SRP096954
Genome-wide maps of metabolic labeled RNA in Drosophila S2 cells.
  • organism-icon Drosophila melanogaster
  • sample-icon 11 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000, Illumina HiSeq 2500

Description

We report the application of ultrashort metabolic labeling of RNA for high-throughput profiling of RNA processing in Drosophila S2 cells. Overall design: Examination of 3 different labeling timepoints in Drosophila S2 cells.

Publication Title

The kinetics of pre-mRNA splicing in the <i>Drosophila</i> genome and the influence of gene architecture.

Sample Metadata Fields

Cell line, Subject

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accession-icon SRP065286
GATA-1 and heme regulate the erythroid cell transcriptome.
  • organism-icon Mus musculus
  • sample-icon 20 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Alas2 gene encodes the rate-limiting enzyme in heme biosynthesis. CRISPR/Cas9-mediated ablation of two Alas2 intronic cis-elements strongly reduced GATA-1-induced Alas2 transcription, heme biosynthesis, and GATA-1 regulation of other vital constituents of the erythroid cell transcriptome. Bypassing Alas2 function in Alas2 cis-element-mutant (double mutant) cells by providing its catalytic product 5-aminolevulinic acid (5-ALA) rescued heme biosynthesis and the GATA-1-dependent genetic network. We discovered a GATA factor- and heme-dependent circuit that establishes the erythroid cell transcriptome. Overall design: G1E-ER-GATA-1 WT and double mutant cells were examined. Untreated WT, beta-estradiol-treated WT, beta-estradiol-treated double-mutant, and beta-estradiol/5-ALA-treated double-mutant cells were subjected to RNA-seq.

Publication Title

Mechanism governing heme synthesis reveals a GATA factor/heme circuit that controls differentiation.

Sample Metadata Fields

Treatment, Subject

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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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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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