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accession-icon GSE29868
Inferring drug-induced gene regulatory relationships in primary human hepatocytes
  • organism-icon Homo sapiens
  • sample-icon 50 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Statins are widely used cholesterol-lowering drugs that inhibit HMG-CoA reductase, a key enzyme in cholesterol synthesis. In some cases, however, these drugs may cause a number of toxic side effects in hepatocytes and skeletal muscle tissue. Currently, the specific molecular mechanisms that cause these adverse effects are not sufficiently understood. In this work, genome-wide RNA expression changes in primary human hepatocytes of six individuals were measured at five time points upon atorvastatin treatment. A novel systems-level analysis workflow was applied to reconstruct regulatory mechanisms based on these drug-response data and available knowledge about transcription factor binding specificities, protein-protein interactions and protein-drug interactions. Several previously unknown transcription factors, regulatory cofactors and signaling molecules were found to be involved in atorvastatin-responsive gene expression. Some novel relationships, e.g., the regulatory influence of nuclear receptor NR2C2 on CYP3A4, were successfully validated in wet-lab experiments.

Publication Title

Inferring statin-induced gene regulatory relationships in primary human hepatocytes.

Sample Metadata Fields

Specimen part, Treatment, Subject

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accession-icon GSE62455
Gene expression of paired samples of hepatic stellate cells (HSC) and hepatocyte cell culture (HCC) treated with conditioned media of HSC cells
  • organism-icon Homo sapiens
  • sample-icon 34 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

All living cells rely on the communication with other cells to ensure their function and survival. Molecular signals are sent among cells of the same cell type and from cells of one cell type to another. In cancer, not only the cancer cells themselves are responsible for the malignancy, but also stromal (non-cancerous) cells and the molecular signals they send to cancer cells are important factors that determine the severity and outcome of the disease. Therefore, the identification of stromal signals and their influence on cancer cells is important when looking for novel treatment strategies.

Publication Title

Causal Modeling of Cancer-Stromal Communication Identifies PAPPA as a Novel Stroma-Secreted Factor Activating NFκB Signaling in Hepatocellular Carcinoma.

Sample Metadata Fields

Specimen part

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accession-icon GSE46667
Lymphotoxin-beta receptor activation in HBV-infected HepaRG cells
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

The objective of this experiment was to test the effect, at a transcrptomic level, of lymphotoxin-beta receptor activation in HBV-infected differentiated HepaRG cells

Publication Title

Specific and nonhepatotoxic degradation of nuclear hepatitis B virus cccDNA.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE70617
Integrating high-dimensional transcriptomics and image analysis tools into early safety screening
  • organism-icon Homo sapiens
  • sample-icon 114 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U219 Array (hgu219), Affymetrix Human Gene 1.1 ST Array (hugene11st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.

Sample Metadata Fields

Cell line, Treatment

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accession-icon GSE70611
Integrating high-dimensional transcriptomics and image analysis tools into early safety screening (I)
  • organism-icon Homo sapiens
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.1 ST Array (hugene11st)

Description

In this paper we demonstrated the potential to flag toxicity issues by utilizing data from exploratory experiments which are typically generated for target evaluation purposes during early drug discovery

Publication Title

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.

Sample Metadata Fields

Cell line, Treatment

View Samples
accession-icon GSE70613
Integrating high-dimensional transcriptomics and image analysis tools into early safety screening (II)
  • organism-icon Homo sapiens
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U219 Array (hgu219), Affymetrix Human Gene 1.1 ST Array (hugene11st)

Description

In this paper we demonstrated the potential to flag toxicity issues by utilizing data from exploratory experiments which are typically generated for target evaluation purposes during early drug discovery

Publication Title

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.

Sample Metadata Fields

Cell line, Treatment

View Samples
accession-icon GSE70614
Integrating high-dimensional transcriptomics and image analysis tools into early safety screening (III)
  • organism-icon Homo sapiens
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.1 ST Array (hugene11st)

Description

In this paper we demonstrated the potential to flag toxicity issues by utilizing data from exploratory experiments which are typically generated for target evaluation purposes during early drug discovery

Publication Title

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.

Sample Metadata Fields

Cell line, Treatment

View Samples
accession-icon GSE35976
Genome wide array analysis of endosseous implant adherent cellular phenotypes
  • organism-icon Rattus norvegicus
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Gene 1.1 ST Array (ragene11st)

Description

Objective: to identify the early molecular processes involved in osseointegration associated with a micro roughened and nanosurface featured implants.

Publication Title

Comparative molecular assessment of early osseointegration in implant-adherent cells.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE9419
The skeletal muscle transcript profile reflects responses to inadequate protein intake in younger and older males
  • organism-icon Homo sapiens
  • sample-icon 66 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Inadequate protein intake initiates an accommodative response with adverse changes in skeletal muscle function and structure. mRNA level changes due to short-term inadequate dietary protein might be an early indicator of accommodation. The aims of this study were to assess the effects of dietary protein and the diet-by-age interaction on the skeletal muscle transcript profile. Self-organizing maps were used to determine expression patterns across protein trials.

Publication Title

The skeletal muscle transcript profile reflects accommodative responses to inadequate protein intake in younger and older males.

Sample Metadata Fields

Sex

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accession-icon GSE12189
FACS-Assisted Microarray Profiling Implicates Novel Genes and Pathways in Zebrafish Gastrointestinal Tract Development
  • organism-icon Danio rerio
  • sample-icon 23 Downloadable Samples
  • Technology Badge Icon Affymetrix Zebrafish Genome Array (zebrafish)

Description

Zebrafish (Danio rerio) gutGFP transgenic embryos [Tg(XlEef1a1:GFP)s854] were collected at 4 time points: 2 days post fertilization (dpf), 3, dpf, 4 dpf, 6 dpf. Embryos were dissociated into single cells and sorted by FACS based on GFP expression.

Publication Title

FACS-assisted microarray profiling implicates novel genes and pathways in zebrafish gastrointestinal tract development.

Sample Metadata Fields

Age

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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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