refine.bio
  • Search
      • Normalized Compendia
      • RNA-seq Sample Compendia
  • Docs
  • About
  • My Dataset
github link
Showing
of 13694 results
Sort by

Filters

Technology

Platform

accession-icon GSE81829
Mouse embryonic fibroblasts (MEFs) : AK156230 knockdown cells vs control cells
  • organism-icon Mus musculus
  • sample-icon 2 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

mRNAs expression profile of MEFs comparing AK156230 knockdown cells with control cells. The microarray with coverage of 45038 mouse mRNAs.

Publication Title

No associated publication

Sample Metadata Fields

Specimen part

View Samples
accession-icon SRP065153
Homo sapiens Transcriptome or Gene expression
  • organism-icon Homo sapiens
  • sample-icon 4 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2000

Description

Several studies have shown that long non-coding RNAs (lncRNAs) may play anessential role in Epithelial-mesenchymal transition (EMT), which is an important step in tumor metastasis, however, little is known about the global change of lncRNA transcriptome during EMT. To investigate how lncRNA transcriptome alteration contributes to EMT progression regulation, we performed a whole-transcriptome strand-specific RNA deep sequencing of MCF10A induced EMT by TGF-ß. Deep sequencing results showed that the long RNA (>=200-nt) transcriptome of MCF10A was undergone a global changed in EMT, and this alteration was determined as early as 8h after being induced using TGF-ß. 8703 linear novel genes with ambiguous protein-coding potential were identified, 512 of which were further determined to be novel lncRNAs. After analyzing the expression of 5473 known and novel lncRNAs, as well as 2208 known and novel circRNAs during EMT, we found a large numbers of lncRNAs might be involved in the regulation of EMT. Intriguingly, we identified 216 gene clusters constituted by lncRNAs and/ornovel genes in “gene desert” region. The expressions of all genes in these clusters were changed concurrently during EMT, indicating that these clusters might play important role in EMT. Our study reveals a global reprogramming of lncRNAs transcriptome in EMT and provides clues to the study of the molecular mechanism of EMT.

Publication Title

No associated publication

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP023130
Gallus gallus strain:XH chicken and BEH chicken Transcriptome or Gene expression
  • organism-icon Gallus gallus
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIllumina Genome Analyzer II

Description

The transcriptome sequencing reveals the divergence of the genetic mechanism of reproductive traits in two Chinese native breeds. XH chicken was meat-type breed with low reproduction ability, with a 70~80% incidence of broodiness in population, with the duration of 15~30 d brooding, and with a production of 60~90 eggs per year. BEH chicken was layer-type breed with high reproduction ability, with a 10%~15% incidence of broodiness in population, with the duration of 7~20 d brooding, and with a production of 180 eggs per year.

Publication Title

No associated publication

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon GSE36013
Gene-level expression data from Oryza sativa.indica (mock-treated or blast pathogen treated resistant rice line and susceptible rice line )
  • organism-icon Oryza sativa
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Rice Genome Array (rice)

Description

In this dataset, we include the expression data obtained from untreated and blast pathogen treated rice seedlings using a variety of blast resistant rice line H4, as well as the susceptible rice line Zhonger-Ruanzhan. These data are used to obtain 4087 genes that are differentially expressed in response to blast pathogen in both of rice lines,as well as 717 genes that are differentially expressed between different lines both in the moch-treated and the blast treated.

Publication Title

No associated publication

Sample Metadata Fields

Specimen part, Treatment

View Samples
accession-icon GSE75118
Expression Profile of alloreactive CD8 and CD4 induced regulatory T cells
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

Adoptive natural regulatory T cell (nTreg) therapy has improved the outcome for patients suffering from graft-versus-host disease (GVHD) following allogeneic hematopoietic cell transplantation (allo-HCT). However, fear of broad immune suppression and subsequent dampening of beneficial graft-versus-leukemic (GVL) responses remains a challenge. To address this concern, we generated alloreactive induced Tregs (iTregs) from resting CD4 or CD8 T cells and tested their ability to suppress GVH and maintain GVL responses. We utilized major mismatched and haploidentical murine models of HCT with host-derived lymphoma or leukemia cell lines to evaluate GVH and GVL responses simultaneously. Alloreactive CD4 iTregs were effective in preventing GVHD, but abrogated the GVL effect against aggressive leukemia. Alloreactive CD8 iTregs moderately attenuated GVHD while sparing the GVL effect. Hence, we reasoned that using a combination of CD4 and CD8 iTregs could achieve the optimal goal of allo-HCT. Indeed, the combinational therapy was superior to CD4 or CD8 iTreg singular therapy in GVHD control; importantly, the combinational therapy maintained GVL responses. Cellular analysis uncovered potent suppression of both CD4 and CD8 effector T cells by the combinational therapy that resulted in effective prevention of GVHD, which could not be achieved by either singular therapy. Gene expression profiles revealed alloreactive CD8 iTregs possess elevated expression of multiple cytolytic molecules compared to CD4 iTregs, which likely contributes to GVL preservation. Our study uncovers unique differences between alloreactive CD4 and CD8 iTregs that can be harnessed to create an optimal iTreg therapy for GVHD prevention with maintained GVL responses.

Publication Title

No associated publication

Sample Metadata Fields

Specimen part, Treatment

View Samples
accession-icon GSE44104
COL11A1 promotes tumor progression and predicts poor clinical outcome in ovarian cancer.
  • organism-icon Homo sapiens
  • sample-icon 55 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Biomarkers that predict disease progression might assist the development of better therapeutic strategies for aggressive cancers, such as ovarian cancer. Here, we investigated the role of collagen type XI alpha 1 (COL11A1) in cell invasiveness and tumor formation and the prognostic impact of COL11A1 expression in ovarian cancer. Microarray analysis suggested that COL11A1 is a disease progression-associated gene that is linked to ovarian cancer recurrence and poor survival.

Publication Title

COL11A1 promotes tumor progression and predicts poor clinical outcome in ovarian cancer.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE145127
Microarray analysis of dithranol-treated psoriasis
  • organism-icon Homo sapiens
  • sample-icon 36 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.1 ST Array (hugene21st)

Description

Microarray analysis of dithranol-treated psoriasis lesions before, during and after therapy

Publication Title

Dithranol targets keratinocytes, their crosstalk with neutrophils and inhibits the IL-36 inflammatory loop in psoriasis.

Sample Metadata Fields

Time

View Samples
accession-icon GSE67851
Expression data from AT/RTs, AT/RT-like tumors and medulloblastomas
  • organism-icon Homo sapiens
  • sample-icon 26 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

Integrated genomics has identified a new AT/RT-like yet INI1-positive brain tumor subtype among primary pediatric embryonal tumors.

Sample Metadata Fields

Sex, Specimen part, Disease, Disease stage

View Samples
accession-icon GSE46495
Transcriptome signature of white adipose tissue, liver, and skeletal muscle in 24 hours fasted mice (C57Bl/6J)
  • organism-icon Mus musculus
  • sample-icon 30 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.1 ST Array (mogene11st)

Description

Fasting is the process of metabolic adaption to food deprivation that is taking place in most organisms, e.g. during the daily resting phase in mammals. Furthermore, in biomedical research fasting is used in most metabolic studies to synchronize nutritional states of study subjects. Because there is a lack of standardization for this procedure, we need a deeper understanding of the dynamics and the molecular players in fasting. In this study we investigated the transcriptome signature of white adipose tissue, liver, and skeletal muscle in 24 hours fasted mice (and chow fat controls) using Affymetrix whole-genome microarrays.

Publication Title

Metabolite and transcriptome analysis during fasting suggest a role for the p53-Ddit4 axis in major metabolic tissues.

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon GSE63941
Expression data from cultured human esophageal squamous cell carcinoma cell lines and cultured human fibroblasts.
  • organism-icon Homo sapiens
  • sample-icon 26 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Cancer cells express different sets of receptor type tyrosine kinases. These receptor kinases may be activated through autocrine or paracrine mechanisms. Fibroblasts may modify the biologic properties of surrounding cancer cells through paracrine mechansms.

Publication Title

The role of HGF/MET and FGF/FGFR in fibroblast-derived growth stimulation and lapatinib-resistance of esophageal squamous cell carcinoma.

Sample Metadata Fields

Specimen part, Cell line

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)

fund-icon Fund the CCDL

Developed by the Childhood Cancer Data Lab

Powered by Alex's Lemonade Stand Foundation

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.

BSD 3-Clause LicensePrivacyTerms of UseContact