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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.

Alternate Accession IDs

E-GEOD-70617

Sample Metadata Fields

Cell line, Treatment

View Samples
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.

Alternate Accession IDs

E-GEOD-70611

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.

Alternate Accession IDs

E-GEOD-70613

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.

Alternate Accession IDs

E-GEOD-70614

Sample Metadata Fields

Cell line, Treatment

View Samples
accession-icon GSE82160
FAS signalling in retroviral neuroinflammation
  • organism-icon Homo sapiens
  • sample-icon 15 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

We comprehensively explored Fas expression (protein and mRNA) and function in lymphocyte activation, apoptosis, proliferation and transcriptome, using flow cytometry, [3H]-thymidine incorporation and microarray analysis in PBMC from HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP) patients.

Publication Title

A Fas<sup>hi</sup> Lymphoproliferative Phenotype Reveals Non-Apoptotic Fas Signaling in HTLV-1-Associated Neuroinflammation.

Alternate Accession IDs

E-GEOD-82160

Sample Metadata Fields

Specimen part, Disease stage, Treatment

View Samples
accession-icon GSE21653
A gene expression signature identifies two prognostic subgroups of basal breast cancer
  • organism-icon Homo sapiens
  • sample-icon 265 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (p=9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR=0.52, p=3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative.

Publication Title

A gene expression signature identifies two prognostic subgroups of basal breast cancer.

Alternate Accession IDs

E-GEOD-21653

Sample Metadata Fields

Age, Specimen part

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accession-icon GSE55902
Expression data from Rice leaves
  • organism-icon Oryza sativa
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Rice Genome Array (rice)

Description

Senescence is a developmental process and chlorophyll is an indicator of leaf senescene. In plants cytokinin plays a role in delaying leaf senescence. Chlorophyll degradation is tightly regulated during senescence and cytokinin might interplay in the chrorophyll degradation pathway to regulate leaf greening.

Publication Title

Cytokinin delays dark-induced senescence in rice by maintaining the chlorophyll cycle and photosynthetic complexes.

Alternate Accession IDs

E-GEOD-55902

Sample Metadata Fields

Specimen part

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accession-icon SRP045664
KLF1 null neonates display hydrops fetalis and a deranged erythroid transcriptome
  • organism-icon Homo sapiens
  • sample-icon 3 Downloadable Samples
  • Technology Badge IconIonTorrentProton

Description

We describe a case of severe neonatal anemia with kernicterus due to compound heterozygosity for null mutations in KLF1, each inherited from asymptomatic parents. One of the mutations is novel. This is the first described case of a KLF1 null human. The phenotype of severe DAT-negative non-spherocytic hemolytic anaemia (NSHA), jaundice, hepato-splenomegaly, and marked erythroblastosis is more severe than that present in CDA type IV due to dominant mutations in the second zinc-finger of KLF1. There was a very high level of HbF expression into childhood (>70%), consistent with a key role for KLF1 in human hemoglobin switching. We performed RNA-seq on circulating erythroblasts and found human KLF1 acts like mouse Klf1 to coordinate expression of many genes required to build a red cell including those encoding globins, cytoskeletal components, AHSP, heme synthesis enzymes, cell cycle regulators, and blood group antigens. We identify novel KLF1 target genes including KIF23 and KIF11 which are required for proper cytokinesis. We also identify new roles for KLF1 in autophagy, global transcriptional control and RNA splicing. We suggest loss of KLF1 should be considered in otherwise unexplained cases of severe neonatal NSHA or hydrops fetalis. Overall design: mRNA sequencing on peripheral blood from a family trio (mother, father and proband) where parents were asymptomatic and proband had severe neonatal anemia.

Publication Title

KLF1-null neonates display hydrops fetalis and a deranged erythroid transcriptome.

Alternate Accession IDs

GSE60514

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE76563
The study of inflammatory responses in mammalian macrophages with LPS stimulation
  • organism-icon Mus musculus, Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.0 ST Array (mogene20st), Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Gene Regulatory Network Inference of Immunoresponsive Gene 1 (IRG1) Identifies Interferon Regulatory Factor 1 (IRF1) as Its Transcriptional Regulator in Mammalian Macrophages.

Alternate Accession IDs

E-GEOD-76563

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE76561
LPS stimulation of human PBMC-derived macrophages
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Immunoresponsive gene 1 (IRG1) is one of the highest induced genes in macrophages under pro-inflammatory conditions and its function has been recently described: it codes for immune-responsive gene 1 protein/cis-aconitic acid decarboxylase (IRG1/CAD), an enzyme catalyzing the production of itaconic acid from cis-aconitic acid, a tricarboxylic acid (TCA) cycle intermediate. Itaconic acid possesses specific antimicrobial properties inhibiting isocitrate lyase, the first enzyme of the glyoxylate shunt, an anaplerotic pathway that bypasses the TCA cycle and enables bacteria to survive on limited carbon conditions. To elucidate the mechanisms underlying itaconic acid production through IRG1 induction in macrophages, we examined the transcriptional regulation of IRG1. Using a combination of literature information, transcription factor prediction models and genome-wide expression arrays, we inferred the regulatory network of IRG1 in mouse and human macrophages.

Publication Title

Gene Regulatory Network Inference of Immunoresponsive Gene 1 (IRG1) Identifies Interferon Regulatory Factor 1 (IRF1) as Its Transcriptional Regulator in Mammalian Macrophages.

Alternate Accession IDs

E-GEOD-76561

Sample Metadata Fields

Specimen part

View Samples
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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)

fund-icon Fund the CCDL

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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