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accession-icon GSE70302
Gene expression data of C57BL/6, Il1a-knockout and Il1b-knockout mice at 24 hours after spinal cord injury
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

We have previously shown that Il1a-knockout (KO) mice exhibit rapid (at day 1) and persistent improvements in locomotion associated with reduced lesion volume compared with Il1b-KO mice and C57BL/6 controls after traumatic spinal cord injury (SCI). To investigate the mechanism by which Il1a mediates its detrimental effect, we analyzed the transcriptome of the injured spinal cord of Il1a-KO, Il1b-KO and C57BL/6 mice at 24 hours after SCI using GeneChip microarrays.

Publication Title

IL-1α Gene Deletion Protects Oligodendrocytes after Spinal Cord Injury through Upregulation of the Survival Factor Tox3.

Sample Metadata Fields

Specimen part

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accession-icon GSE22207
Identification of promoter sequence elements involved in specific recognition by the S subunit of bacterial RNA polymerase.
  • organism-icon Escherichia coli str. k-12 substr. mg1655
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

Promoter recognition by bacterial RNA polymerase is mediated by subunits, which assemble transiently to RNA polymerase core enzyme (E) during transcription initiation. subunits drive transcription of specific sets of genes by allowing RNA polymerase to interact with different promoter sequences. However, 70, the housekeeping subunit, and S, an alternative subunit mainly active during slow growth and in response to cellular stresses, appear to recognize almost identical promoter sequences, raising the question of how promoter selectivity is achieved in the bacterial cell. To identify sequence determinants for selective promoter recognition, we performed a run-off/microarray experiment (ROMA): in vitro transcription experiments were carried out with RNA polymerase saturated either with 70 (E70) or with S (ES) using the whole Escherichia coli genome as DNA template, and transcript levels were determined by microarray analysis. We found that several genes associated with bacterial growth (e.g., ribosomal operons) were transcribed more efficiently by E70. In contrast, ES transcribed preferentially genes involved in stress responses, secondary metabolism, as well as regulatory RNAs and intergenic regions with yet unknown function. Genes preferentially recognized in vitro by ES showed reduced expression in ES -deficient mutant strain of E. coli. Sequence comparison of E70- versus ES dependent promoters confirms that the presence of a -35 sequence and the relative location of UP elements affect promoter interaction with either form of RNA polymerase, and suggests that a G/C bias in the -2/+1 nucleotides would favour efficient promoter recognition by E70.

Publication Title

In vitro transcription profiling of the σS subunit of bacterial RNA polymerase: re-definition of the σS regulon and identification of σS-specific promoter sequence elements.

Sample Metadata Fields

Disease

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accession-icon GSE11078
A six-gene signature predicting breast cancer lung metastasis
  • organism-icon Homo sapiens
  • sample-icon 23 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients.

Publication Title

A six-gene signature predicting breast cancer lung metastasis.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE25485
Gene expression data in Bone Marrow Derived Dendritic Cells (BMDC) following nanoemulsion adjuvant exposure
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Antigen uptake, processing and presentation by dendritic cells are regulated by complex intra- and inter-cellular signalling events. Typical vaccine adjuvants lead to the transcription of pro-inflammatory cytokines and chemokines which relate to immune induction.

Publication Title

Nanoemulsion mucosal adjuvant uniquely activates cytokine production by nasal ciliated epithelium and induces dendritic cell trafficking.

Sample Metadata Fields

Sex, Age, Specimen part, Time

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accession-icon SRP041414
Dicer knockout NSCLC RNAseq and miRseq
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon

Description

Dicer knockout NSCLC mRNAseq profiles the transcriptome, Dicer knockout NSCLC miRseq profiles the miRnome Overall design: DicerHet and DicerKO NSCLC, 2 biological reps each genotype for mRNAseq, 1 biological rep each for miRseq

Publication Title

Global microRNA depletion suppresses tumor angiogenesis.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP001417
modENCODE RNA-Seq of Drosophila Kc167
  • organism-icon Drosophila melanogaster
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIllumina Genome Analyzer

Description

Deep Sequencing of Kc167 mRNA. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Overall design: Seq of Poly-A+ RNA from D. melanogaster Kc167

Publication Title

The transcriptional diversity of 25 Drosophila cell lines.

Sample Metadata Fields

Cell line, Subject

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accession-icon GSE73731
Clear cell renal cell carcinoma (ccRCC) samples
  • organism-icon Homo sapiens
  • sample-icon 261 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

To select signatures of ccRCC, 265 ccRCC samples were obtained from the Van Andel Research Institute.

Publication Title

Recognizing the Continuous Nature of Expression Heterogeneity and Clinical Outcomes in Clear Cell Renal Cell Carcinoma.

Sample Metadata Fields

Sex, Specimen part, Disease stage

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accession-icon SRP067710
CXCR5+ Follicular Cytotoxic T cells Control Viral Infection in B Cell follicles
  • organism-icon Mus musculus
  • sample-icon 16 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 1500

Description

Lymphocytic Choriomeningitis Virus (LCMV) specific CD8+ T cells (P14) were transferred into congenic WT mice followed by LCMV(DOCILE) infection. CXCR5-expressing (CXCR5+) or CXCR5 non-expressing (CXCR5-) P14 were purified on day 8 after infection, and total mRNA were sequenced from these populations. mRNA of P14 from uninfected mice (Naive P14) was also sequenced. Overall design: Examination of mRNA level in CXCR5 expressing P14 (CXCR5+P14) and non-expressing P14 (CXCR5-P14) from LCMV infected mice day 8 post infection. mRNA of P14 from uninfected mice (Naïve P14) was also examined.

Publication Title

CXCR5(+) follicular cytotoxic T cells control viral infection in B cell follicles.

Sample Metadata Fields

Subject

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accession-icon SRP063567
Complementarity and redundancy of IL-22-producing innate lymphoid cells
  • organism-icon Mus musculus
  • sample-icon 16 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Homeostasis of the gut microbiota is pivotal to the survival of the host. Intestinal T cells and Innate Lymphoid cells (ILCs) control the composition of the microbiota and respond to its perturbations. Interleukin 22 (IL-22) plays a pivotal role in the immune control of gut commensal and pathogenic bacteria and is secreted by a heterogeneous population of intestinal T cells, NCR- ILC3 and NCR+ILC3. Expression of NCR by ILC3 is believed to define an irreversible effector ILC3 end-state fate in which these cells are key to control of bacterial infection via their production of IL-22. Here we identify the core transcriptional signature that drives the differentiation of NCR- ILC3 into NCR+ ILC3 and reveal that NCR+ILC3 exhibit more plasticity than originally thought, as NCR+ ILC3 can revert to NCR- ILC3. Contrary to the prevailing understanding of NCR+ ILC3 genesis and function, in vivo analyses of mice conditionally deleted of the key ILC3 genes Stat3, Il22, Tbet and Mcl1 demonstrated that NCR+ ILC3 were not essential for the control of colonic infections in the presence of T cells. However, NCR+ ILC3 were mandatory for homeostasis of the caecum. Our data identify that the interplay of intestinal T cells and ILC3 results in robust complementary fail-safe mechanisms that ensure gut homeostasis. Overall design: Transcriptional profiling of wild-type and T-bet knockout innate lymphoid cells (ILC3) using RNA sequencing

Publication Title

Complementarity and redundancy of IL-22-producing innate lymphoid cells.

Sample Metadata Fields

Specimen part, Cell line, Subject

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accession-icon GSE69079
Expression data of sleeping, waking, and sleep deprived adult heterozygous aldh1l1 eGFP-L10a mice
  • organism-icon Mus musculus
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Transcriptomic studies revealed that hundreds of mRNAs show differential expression in the brains of sleeping versus awake rats, mice, flies, and sparrows. Although these results have offered clues regarding the molecular consequences of sleep and sleep loss, their functional significance thus far has been limited. This is because the previous studies pooled transcripts from all brain cells, including neurons and glia.

Publication Title

Transcriptome profiling of sleeping, waking, and sleep deprived adult heterozygous Aldh1L1 - eGFP-L10a mice.

Sample Metadata Fields

Disease

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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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