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accession-icon SRP094627
Transcriptional changes in the primary somatosensory cortex upon sensory deprivation
  • organism-icon Mus musculus
  • sample-icon 26 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

Experience-dependent plasticity (EDP) is essential for anatomical and functional maturation of sensory circuits during development and can be readily studied is the rodent barrel cortex. Using this model we aimed to uncover changes on the transcriptome level and applied RNA sequencing upon altered sensory experience in juvenile mice in a cortical column and layer specific manner. From column- and layer-specific barrel cortical tissue, high quality RNA was purified and sequenced. The current dataset entails an average of 50 million paired-end reads per sample, 75 base pairs in length. Overall design: Wild type mice were deprived of their C-row whiskers from P12 until P23-P24, after which acute brain slices were prepared and tissues were excised from L2/3 and L4 from specific barrel columns. RNA isolated from these tissue sections was then subjected to RNA-sequencing.

Publication Title

Transcriptional mapping of the primary somatosensory cortex upon sensory deprivation.

Sample Metadata Fields

Cell line, Subject

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accession-icon GSE15623
Expression data from mNSc after 48 hour of treatment with CD95L-T4
  • organism-icon Mus musculus
  • sample-icon 15 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)

Description

In neural stem cells, stimulation of the death receptor CD95 does not trigger apoptosis but resulted in increased stem cell survival and neuronal specification via activation of the Src /PI3K /AKT/mTOR signalling pathway. To further characterize CD95-dependent neural stem cell survival and differentiation we used conventional gene expression profiling combined with translation state array analysis. Mouse neural stem cells grown in neurosphere cultures were stimulated with a trimerized CD95L construct (CD95L-T4) and total as well as polysomal bound RNA was isolated 48 hours after stimulation and analysed by microarrays. CD95L-T4 treatment induced a global increase in ribosome-bound mRNA and protein translation as well as changes on genes involved in neurogenesis, protein synthesis and transcription factors.

Publication Title

The death receptor CD95 activates adult neural stem cells for working memory formation and brain repair.

Sample Metadata Fields

Sex, Treatment

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accession-icon GSE57091
Gene based expression changes in glioblastoma cells after downregulation of MPS1 kinase
  • organism-icon Homo sapiens
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A 2.0 Array (hgu133a2)

Description

Gene expression changes were analyzed in U251 GBM cells after downregulation of MPS1 by RNA interference technology at different time points

Publication Title

Targeting MPS1 Enhances Radiosensitization of Human Glioblastoma by Modulating DNA Repair Proteins.

Sample Metadata Fields

Cell line, Treatment

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accession-icon SRP044038
Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference. [RNA-seq]
  • organism-icon Drosophila melanogaster
  • sample-icon 72 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Genome control is operated by transcription factors (TF) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRN). Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse-engineer the GRN of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types, and inferred a co-expression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5632 direct target genes through 24926 enhancers. Using this network we found network motifs, cis-regulatory codes, and new regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general co-factor in the eye network, being bound to thousands of nucleosome-free regions. Overall design: RNA-seq gene expression profiling across Drosophila 3rd instar larval wild type tissues (brain, eye-antennal and wing discs), specific cell types from the eye-antennal disc, sorted by FACS, and genetic perturbations (TF mutants, TF over-expression, and TF RNAi knockdown).

Publication Title

Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference.

Sample Metadata Fields

Specimen part, Subject

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accession-icon GSE108004
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
  • organism-icon Homo sapiens
  • sample-icon 30 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Sample Metadata Fields

Age, Specimen part, Disease, Disease stage

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accession-icon GSE107465
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [array]
  • organism-icon Homo sapiens
  • sample-icon 30 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately.

Publication Title

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Sample Metadata Fields

Age, Specimen part, Disease, Disease stage

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accession-icon SRP126623
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [RNA-Seq]
  • organism-icon Homo sapiens
  • sample-icon 24 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents. Overall design: We measured the gene expression of samples from 30 different AML patients with acute myeloid leukemia in order to identify reliable gene expression markers for drug sensitivity. We used this dataset for validation. This Series represents 12 patient samples.

Publication Title

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Sample Metadata Fields

Age, Specimen part, Disease, Disease stage, Subject

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accession-icon GSE61758
Effect of loss of PKC theta and p50+cRel on gene expression post T-cell stimulation
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

OT-1 Transgenic CD8 T-cells were isolated from spleens of WT, PKC theta KO, and p50 cRel DKO mice. The T-cells were either cultured with non-pulsed DC (WT only and signified as "WT - UN") or with BMDCs pulsed with the OVA peptide SIINFEKL (N4) (WT, PKC theta KO, and p50 cRel DKO and signified as 'genotype - N4') at a ratio of 1:10 (DC:T-cell) for 18 hours. DCs then were depleted from the culture and RNA was made from the T-cells to measure gene expression at the early / late stage of T-cell activation

Publication Title

NF-κB is crucial in proximal T-cell signaling for calcium influx and NFAT activation.

Sample Metadata Fields

Specimen part

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accession-icon SRP077284
Dnmt3a Regulates T-cell Development and Suppresses T-ALL Transformation (RNA-seq)
  • organism-icon Mus musculus
  • sample-icon 10 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Gene expression analysis of T-cell acute lymphoblastic leukemia blast cells from either control mice or Dnmt3a knockout mice carrying a Notch1 Intracellular Domain (NICD) retrovirus Overall design: Comparison of gene expression between control and Dnmt3a-KO NICD-driven T-ALL

Publication Title

Dnmt3a regulates T-cell development and suppresses T-ALL transformation.

Sample Metadata Fields

Specimen part, Cell line, Subject

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accession-icon GSE85029
Dido as a switchboard that regulates self-renewal and differentiation in embryonic stem cells
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

DIDO as a Switchboard that Regulates Self-Renewal and Differentiation in Embryonic Stem Cells.

Sample Metadata Fields

Specimen part

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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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