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accession-icon SRP062555
Global analysis of pre-mRNA subcellular localization upon splicing inhibition by spliceostatin A
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2000

Description

RNA-Seq analysis of SSA treated cells Overall design: HeLa cells, nuclear and cytoplasmic fractions, treated with SSA or MeOH

Publication Title

Global analysis of pre-mRNA subcellular localization following splicing inhibition by spliceostatin A.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE84844
Multi-omics profiling of patients with primary Sjogren's syndrome
  • organism-icon Homo sapiens
  • sample-icon 56 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Multi-omics study was conducted to elucidate the crucial molecular mechanisms of primary Sjgrens syndrome (SS) pathology. We generated multiple data set from well-defined patients with SS, which includes whole-blood transcriptomes, serum proteomes and peripheral immunophenotyping. Based on our newly generated data, we performed an extensive bioinformatic investigation. Our integrative analysis identified SS gene signatures (SGS) dysregulated in widespread omics layers, including epigenomes, mRNAs and proteins. SGS predominantly involved the interferon signature and ADAMs substrates. Besides, SGS was significantly overlapped with SS-causing genes indicated by a genome-wide association study and expression trait loci analyses. Combining the molecular signatures with immunophenotypic profiles revealed that cytotoxic CD8 T cells were associated with SGS. Further, we observed the activation of SGS in cytotoxic CD8 T cells isolated from patients with SS. Our multi-omics investigation identified gene signatures deeply associated with SS pathology and showed the involvement of cytotoxic CD8 T cells. These integrative relations across multiple layers will facilitate our understanding of SS at the system level.

Publication Title

Multiomic disease signatures converge to cytotoxic CD8 T cells in primary Sjögren's syndrome.

Sample Metadata Fields

Sex, Age, Specimen part, Disease

View Samples
accession-icon GSE93683
CD8 T-cells from pSS patients and human healthy volunteers
  • organism-icon Homo sapiens
  • sample-icon 48 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Multi-omics study was conducted to elucidate the crucial molecular mechanisms of primary Sjgrens syndrome (SS) pathology. We generated multiple data set from well-defined patients with SS, which includes whole-blood transcriptomes, serum proteomes and peripheral immunophenotyping. Based on our newly generated data, we performed an extensive bioinformatic investigation. Our integrative analysis identified SS gene signatures (SGS) dysregulated in widespread omics layers, including epigenomes, mRNAs and proteins. SGS predominantly involved the interferon signature and ADAMs substrates. Besides, SGS was significantly overlapped with SS-causing genes indicated by a genome-wide association study and expression trait loci analyses. Combining the molecular signatures with immunophenotypic profiles revealed that cytotoxic CD8 T cells were associated with SGS. Further, we observed the activation of SGS in cytotoxic CD8 T cells isolated from patients with SS. Our multi-omics investigation identified gene signatures deeply associated with SS pathology and showed the involvement of cytotoxic CD8 T cells. These integrative relations across multiple layers will facilitate our understanding of SS at the system level.

Publication Title

Multiomic disease signatures converge to cytotoxic CD8 T cells in primary Sjögren's syndrome.

Sample Metadata Fields

Sex, Specimen part, Disease, Disease stage, Subject

View Samples
accession-icon GSE94510
CD4 T-cells from pSS patients and human healthy volunteers
  • organism-icon Homo sapiens
  • sample-icon 36 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Multi-omics study was conducted to elucidate the crucial molecular mechanisms of primary Sjgrens syndrome (SS) pathology. We generated multiple data set from well-defined patients with SS, which includes whole-blood transcriptomes, serum proteomes and peripheral immunophenotyping. Based on our newly generated data, we performed an extensive bioinformatic investigation. Our integrative analysis identified SS gene signatures (SGS) dysregulated in widespread omics layers, including epigenomes, mRNAs and proteins. SGS predominantly involved the interferon signature and ADAMs substrates. Besides, SGS was significantly overlapped with SS-causing genes indicated by a genome-wide association study and expression trait loci analyses. Combining the molecular signatures with immunophenotypic profiles revealed that cytotoxic CD8 T cells were associated with SGS. Further, we observed the activation of SGS in cytotoxic CD8 T cells isolated from patients with SS. Our multi-omics investigation identified gene signatures deeply associated with SS pathology and showed the involvement of cytotoxic CD8 T cells. These integrative relations across multiple layers will facilitate our understanding of SS at the system level.

Publication Title

Multiomic disease signatures converge to cytotoxic CD8 T cells in primary Sjögren's syndrome.

Sample Metadata Fields

Sex, Specimen part, Disease, Subject

View Samples
accession-icon GSE12885
Genome-wide changes in DNA methylation and copy number play a role in deregulation of gene expression in osteosarcoma
  • organism-icon Homo sapiens
  • sample-icon 14 Downloadable Samples
  • Technology Badge IconAgilent-014693 Human Genome CGH Microarray 244A (Feature number version), Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Identification of interactive networks of gene expression associated with osteosarcoma oncogenesis by integrated molecular profiling.

Sample Metadata Fields

Specimen part

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accession-icon GSE11416
Comparison of osteosarcoma cell lines and normal human osteoblasts
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconAgilent-014693 Human Genome CGH Microarray 244A (Feature number version), Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

In vitro analysis of integrated global high-resolution DNA methylation profiling with genomic imbalance and gene expression in osteosarcoma.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE12865
Gene expression of human paediatric osteosarcoma tumour samples relative to normal human osteoblasts
  • organism-icon Homo sapiens
  • sample-icon 14 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Gain or loss of genes and deregulation of gene expression can result in cumulative and progressive disruptions of normal cellular functions.

Publication Title

Identification of interactive networks of gene expression associated with osteosarcoma oncogenesis by integrated molecular profiling.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE11414
Gene expression of osteosarcoma cell (U2OS, MG63) lines relative to normal human osteoblasts (HOB)
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Gain or loss of genes and deregulation of gene expression can result in cumulative and progressive disruptions of normal cellular functions.

Publication Title

In vitro analysis of integrated global high-resolution DNA methylation profiling with genomic imbalance and gene expression in osteosarcoma.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE93777
Multi-omics monitoring of drug response in rheumatoid arthritis.
  • organism-icon Homo sapiens
  • sample-icon 286 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

Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission.

Sample Metadata Fields

Sex, Age, Specimen part, Disease, Disease stage, Subject

View Samples
accession-icon GSE93272
Whole blood gene expression of rheumatoid arthritis
  • organism-icon Homo sapiens
  • sample-icon 238 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Sustained clinical remission (CR) without drug treatment has not been achieved in patients with rheumatoid arthritis (RA). This implies a substantial difference between CR and the healthy state, but it has yet to be quantified. We report a longitudinal monitoring of the drug response at multi-omics levels in the peripheral blood of patients with RA. Our data reveal that drug treatments alter the molecular profile closer to that of HCs at the transcriptome, serum proteome and immunophenotype level. Patient follow-up suggests that the molecular profile after drug treatments is associated with long-term stable CR. In addition, we identify molecular signatures that are resistant to drug treatments. These signatures are associated with RA independently of known disease severity indexes and are largely explained by the imbalance of neutrophils, monocytes, and lymphocytes. This high-dimensional phenotyping provides a quantitative measure of molecular remission and illustrates a multi-omics approach to understanding drug response.

Publication Title

Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission.

Sample Metadata Fields

Sex, Age, Specimen part, Disease, Disease stage, Subject

View Samples
...

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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