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accession-icon GSE26829
Measurements of mRNA abundance and decay for two strains 211 (wt) and 212 (mutant)
  • organism-icon Saccharomyces cerevisiae
  • sample-icon 57 Downloadable Samples
  • Technology Badge Icon Affymetrix Yeast Genome 2.0 Array (yeast2)

Description

For each strain two time courses for mRNA abundance: Oxidative and MMS and two time courses for decay: reference decay and following oxidative stress

Publication Title

Transcriptome kinetics is governed by a genome-wide coupling of mRNA production and degradation: a role for RNA Pol II.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE12222
Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation
  • organism-icon Saccharomyces cerevisiae
  • sample-icon 49 Downloadable Samples
  • Technology Badge Icon Affymetrix Yeast Genome 2.0 Array (yeast2)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

No associated publication

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE12221
Decay profiles of Saccharomyces cerevisiae mRNAs following oxidative stress and DNA damage
  • organism-icon Saccharomyces cerevisiae
  • sample-icon 37 Downloadable Samples
  • Technology Badge Icon Affymetrix Yeast Genome 2.0 Array (yeast2)

Description

We subjected yeast to two stresses, oxidative stress, which under current settings induces a fast and transient response in mRNA abundance, and DNA damage, which triggers a slow enduring response. Using microarrays, we performed a transcriptional arrest experiment to measure genome-wide mRNA decay profiles under each condition. Genome-wide decay kinetics in each condition were compared to decay experiments that were performed in a reference condition (only transcription inhibition without an additional stress) to quantify changes in mRNA stability in each condition. We found condition-specific changes in mRNA decay rates and coordination between mRNA production and degradation. In the transient response, most induced genes were surprisingly destabilized, while repressed genes were somewhat stabilized, exhibiting counteraction between production and degradation. This strategy can reconcile high steady-state level with short response time among induced genes. In contrast, the stress that induces the slow response displays the more expected behavior, whereby most induced genes are stabilized, and repressed genes destabilized. Our results show genome-wide interplay between mRNA production and degradation, and that alternative modes of such interplay determine the kinetics of the transcriptome in response to stress.

Publication Title

Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE15936
Expression data from S. cerevisiae under serial stresses
  • organism-icon Saccharomyces cerevisiae
  • sample-icon 22 Downloadable Samples
  • Technology Badge Icon Affymetrix Yeast Genome 2.0 Array (yeast2)

Description

S. cerevisae cells were exposed to different series of mild stresses. Stress type include heat shock, oxidative and osmotic stresses.

Publication Title

No associated publication

Sample Metadata Fields

Time

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accession-icon GSE12220
Changes in Saccharomyces cerevisiae mRNA abundance following oxidative stress and DNA damage stress
  • organism-icon Saccharomyces cerevisiae
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Yeast Genome 2.0 Array (yeast2)

Description

We subjected yeast to two stresses, oxidative stress, which under current settings induces a fast and transient response in mRNA abundance, and DNA damage, which triggers a slow enduring response. Using microarrays we performed a conventional quantification of change in mRNA abundance.

Publication Title

No associated publication

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE33538
Context-specific microRNA analysis: identification of functional microRNAs and their mRNA targets
  • organism-icon Homo sapiens
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

MicroRNAs (miRs) function primarily as post-transcriptional negative regulators of gene expression through binding to their mRNA targets. Reliable prediction of a miRs targets is a considerable bioinformatic challenge of great importance for inferring the miRs function. Sequence-based prediction algorithms have high false-positive rates, are not in agreement, and are not biological context specific. Here we introduce CoSMic (Context-Specific MicroRNA analysis), an algorithm that combines sequence-based prediction with miR and mRNA expression data. CoSMic differs from existing methodsit identifies miRs that play active roles in the specific biological system of interest and predicts with less false positives their functional targets. We applied CoSMic to search for miRs that regulate the migratory response of human mammary cells to epidermal growth factor (EGF) stimulation. Several such miRs, whose putative targets were significantly enriched by migration processes were identified. We tested three of these miRs experimentally, and showed that they indeed affected the migratory phenotype; we also tested three negative controls. In comparison to other algorithms CoSMic indeed filters out false positives and allows improved identification of context-specific targets. CoSMic can greatly facilitate miR research in general and, in particular, advance our understanding of individual miRs function in a specific context.

Publication Title

Context-specific microRNA analysis: identification of functional microRNAs and their mRNA targets.

Sample Metadata Fields

Cell line

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accession-icon GSE11511
Identification of histone codes and crosstalk in fission yeast
  • organism-icon Schizosaccharomyces pombe
  • sample-icon 7 Downloadable Samples
  • Technology Badge Icon Affymetrix Yeast Genome 2.0 Array (yeast2)

Description

Aims: To map histone modifications with unprecedented resolution both globally and locus-specifically, and to link modification patterns to gene expression. Materials & methods: Using correlations between quantitative mass spectrometry and chromatin immunoprecipitation/microarray analyses, we have mapped histone post-translational modifications in fission yeast (Schizosaccharomyces pombe). Results: Acetylations at lysine 9, 18 and 27 of histone H3 give the best positive correlations with gene expression in this organism. Using clustering analysis and gene ontology search tools, we identified promoter histone modification patterns that characterize several classes of gene function. For example, gene promoters of genes involved in cytokinesis have high H3K36me2 and low H3K4me2, whereas the converse pattern is found ar promoters of gene involved in positive regulation of the cell cycle. We detected acetylation of H4 preferentially at lysine 16 followed by lysine 12, 8 and 5. Our analysis shows that this H4 acetylation bias in the coding regions is dependent upon gene length and linked to gene expression. Our analysis also reveals a role for H3K36 methylation at gene promoters where it functions in a crosstalk between the histone methyltransferase Set2KMT3 and the histone deacetylase Clr6, which removes H3K27ac leading to repression of transcription. Conclusion: Histone modification patterns could be linked to gene expression in fission yeast.

Publication Title

Genome-wide mapping of histone modifications and mass spectrometry reveal H4 acetylation bias and H3K36 methylation at gene promoters in fission yeast.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE56131
Transcription factor Runx1 cooperates with MyoD and c-Jun to regulate the balance of myoblast proliferation/differentiation
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Genomic-wide transcriptional profiling in primary myoblasts reveals Runx1-regulated genes in muscle regeneration.

Sample Metadata Fields

Specimen part

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accession-icon GSE45748
Addiction of t(8;21) and inv(16) AML to native RUNX1
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Addiction of t(8;21) and inv(16) acute myeloid leukemia to native RUNX1.

Sample Metadata Fields

Cell line

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accession-icon GSE89965
Expression data from meduallar thymic epithelial cells and thymic or splenic B cells
  • organism-icon Mus musculus
  • sample-icon 14 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Aire is a transcriptional regulator that induces promiscuous expression of thousands of tissue-restricted antigen (TRA) genes in medullary thymic epithelial cells (mTECs). While the target genes of Aire are well characterized, the transcriptional programs regulating its own expression remain elusive. We used Affymetrix microarrays to analyze the gene expression patterns of Aire expressing cells (mature mTECs and Thymic B cells) and compared them to control counterparts, namely immature mTECs, cortical Thymic epithelial cells and splenic B cells of tissue-restricted antigen (TRA) genes in medullary thymic epithelial cells (mTECs). While the target genes of Aire are well characterized, the transcriptional programs regulating its own expression remain elusive.

Publication Title

No associated publication

Sample Metadata Fields

Age, 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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