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accession-icon GSE25334
Asymmetric self-renewal associated (ASRA) genes
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
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Description

Cell lines geneticially engineered to undergo conditional asymmetric self-renewal were used to identify genes whose expression is asymmetric self-renewal associated (ASRA). Non-random sister chromatid segregation occurs concordantly with asymmetric self-renewal in these cell lines.

Publication Title

A resource for discovering specific and universal biomarkers for distributed stem cells.

Sample Metadata Fields

Cell line

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accession-icon GSE52024
Genome wide analysis of transcriptome and microRNAs in early stage of Alzheimer's disease
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Prediction of miRNA-mRNA associations in Alzheimer's disease mice using network topology.

Sample Metadata Fields

Sex, Age, Specimen part

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accession-icon GSE52022
Genome wide analysis of transcriptome and microRNAs in early stage of Alzheimers disease (mRNA)
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon

Description

We addressed the integrated analysis of mRNA and miRNA expression levels of Tg6799 AD model mice at 4 month and 8 months of age. Total 8 gene cluster modules for co-expression network were predicted from transcriptome data and 6 modules were show relation with AD or aging. We constructed early stage AD network using data integration between mRNA and miRNA profiles and predicted miRNAs strongly involved in module regulation. We found that ARRDC3 showed AD mutation dependent changes of expression and was related metabolic dysfunction in early stage AD.

Publication Title

Prediction of miRNA-mRNA associations in Alzheimer's disease mice using network topology.

Sample Metadata Fields

Sex, 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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