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accession-icon GSE4648
Earliest Changes in the Left Ventricular Transcriptome Post-Myocardial Infarction
  • organism-icon Mus musculus
  • sample-icon 198 Downloadable Samples
  • Technology Badge Icon Affymetrix Murine Genome U74A Version 2 Array (mgu74av2)

Description

We report a genome-wide survey of early responses of the mouse heart transcriptome to acute myocardial infarction (AMI). For three regions of the left ventricle (LV), namely ischemic/infarcted tissue (IF), the surviving LV free wall (FW) and the interventricular septum (IVS), 36,899 transcripts were assayed at six time points from 15 min to 48 h post-AMI in both AMI and sham surgery mice. For each transcript, temporal expression patterns were systematically compared between AMI and sham groups, which identified 515 AMI-responsive genes in IF tissue, 35 in the FW, 7 in the IVS, with three genes induced in all three regions. Using the literature, we assigned functional annotations to all 519 nonredundant AMI-induced genes and present two testable models for central signaling pathways induced early post-AMI. First, the early induction of 15 genes involved in assembly and activation of the activator protein-1 (AP-1) family of transcription factors implicates AP-1 as a dominant regulator of earliest post-ischemic molecular events. Second, dramatic increases in transcripts for arginase 1 (ARG1), the enzymes of polyamine biosynthesis and protein inhibitor of nitric oxide synthase (NOS) activity indicates that NO production may be regulated, in part, by inhibition of NOS and coordinate depletion of the NOS substrate, L-arginine. ARG1 was the single most highly induced transcript in the database (121-fold in IF region) and its induction in heart has not been previously reported.

Publication Title

Earliest changes in the left ventricular transcriptome postmyocardial infarction.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP095604
Genome-wide transcriptome profiles in Control and Schizophrenia hiPSC-dervied NPC [RNA-seq]
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

We Report the genome-wide RNA expression levels in control and schizophrenia hiPSC dervied NPC treated with neuronal media for 2 days. In total about 15,000 gene expression were detected in all samples, of which 1349 were dysregualted. Overall design: Examination, identification and comparision of mRNA expression profliles in control and schizophrenia npc

Publication Title

Common developmental genome deprogramming in schizophrenia - Role of Integrative Nuclear FGFR1 Signaling (INFS).

Sample Metadata Fields

Specimen part, Subject

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accession-icon GSE1907
Sarcoidosis + Follow-up study
  • organism-icon Homo sapiens
  • sample-icon 32 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U95A Array (hgu95a)

Description

Sarcoidosis + Follow-up 6 month after

Publication Title

Functional genomics and prognosis in sarcoidosis--the critical role of antigen presentation.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE29832
Expression data from pure/mixed blood and breast to test feasability of deconvolution of clinical samples
  • organism-icon Homo sapiens
  • sample-icon 14 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Blood, for example, contains many different cell types that are derived from a distinct lineage and carry out a different immunological purpose. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent statistical studies.

Publication Title

Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE8319
A LysM Receptor-like Kinase Mediates Chitin Perception and Fungal Resistance in Arabidopsis
  • organism-icon Arabidopsis thaliana
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Arabidopsis ATH1 Genome Array (ath1121501)

Description

A LysM Receptor-like Kinase Mediates Chitin Perception and Fungal Resistance in Arabidopsis

Publication Title

A LysM receptor-like kinase plays a critical role in chitin signaling and fungal resistance in Arabidopsis.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE33252
Chromatin based modeling of transcription rates identifies the contribution of different regulatory layers to steady-state mRNA levels
  • organism-icon Mus musculus
  • sample-icon 19 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Messenger RNA levels in eukaryotes are balanced by two consecutive regulatory layers. Primary, transcriptional regulation at the level of chromatin and secondary, post-transcriptional regulation of the initial transcript in the cytoplasm. Each layer is individually studied in mechanistic detail, while integration of both processes is required to quantify the individual contribution to steady-state RNA levels. Here we show that chromatin features are sufficient to model transcription rate but with different sensitivities in dividing versus post mitotic cells. In both cases chromatin derived transcript levels explains over 80% of variance in measured RNA level enabling to separate transcription from different post-transcriptional processes. By further inclusion of measurements of mRNA half-life and micro RNA expression data we identify a low quantitative contribution of RNA decay by either micro RNA or general differential turnover to final mRNA levels. Together this establishes a chromatin based quantitative model for the contribution of transcriptional and posttranscriptional processes to steady-state levels of messenger RNA.

Publication Title

Chromatin measurements reveal contributions of synthesis and decay to steady-state mRNA levels.

Sample Metadata Fields

Specimen part, Disease, Treatment, Time

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accession-icon GSE19830
Expression data from pure/mixed brain, liver and lung to test feasability and sensitivity of statistical deconvolution
  • organism-icon Rattus norvegicus
  • sample-icon 42 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Genome 230 2.0 Array (rat2302)

Description

Tissues are often made up of multiple cell-types. Blood, for example, contains many different cell-types, each with its own functional attributes and molecular signature. In humans, because of its accessibility and immune functionality, blood cells have been used as a source for RNA-based biomarkers for many diseases. Yet, the proportions of any given cell-type in the blood can vary markedly, even between normal individuals. This results in a significant loss of sensitivity in gene expression studies of blood cells and great difficulty in identifying the cellular source of any perturbations. Ideally, one would like to perform differential expression analysis between patient groups for each of the cell-types within a tissue but this is impractical and prohibitively expensive.

Publication Title

Cell type-specific gene expression differences in complex tissues.

Sample Metadata Fields

Specimen part

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accession-icon GSE20300
Whole blood gene expression analysis of stable and acute rejection pediatric kidney transplant patients
  • organism-icon Homo sapiens
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Full title: Expression data from whole blood gene expression analysis of stable and acute rejection pediatric kidney transplant patients

Publication Title

Cell type-specific gene expression differences in complex tissues.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP053290
Genome-wide transcriptome profiles in pluripotent mouse Embryonic Stem Cells and during Retinoic Acid-induced differentiation
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

We report the genome-wide RNA expression levels in pluripotent mESC and as mESC differentiate towards a neuronal lineage in response to high levels of Retinoic Acid treatment in vitro. RNA-seq was performed to identify all RNAs expressed in both ESCs and neuronal cells. In total, In total, 14,443 expressed genes were detected, of which 1,834 were up-regulated and 1,477 down-regulated (fold change (FC) > -/+2.0 and p-value < 0.035) during RA-induced neuronal differentiation. The top down-regulated genes included members of the pluripotency core transcriptional network, including Klf4, Sox2, Oct4, Nanog, Suz12, Esrrb, Stat3 and Tcfcp2l1. The top up-regulated genes are important for neuronal differentiation (e.g. Pax3, Irx3, Rest and Foxd3) and reside in the RA-pathway (e.g. various homeobox genes), the retinoic acid receptors and the RA-degradation enzyme Cyp26a1. Overall design: Examination, identification and comparision of mRNA expression profliles in two cellular states.

Publication Title

Global Developmental Gene Programing Involves a Nuclear Form of Fibroblast Growth Factor Receptor-1 (FGFR1).

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE109403
Proteogenomic Analysis of Medulloblastoma
  • organism-icon Homo sapiens
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.0 ST Array (hugene20st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Proteomic analysis of Medulloblastoma reveals functional biology with translational potential.

Sample Metadata Fields

Sex, 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)

fund-icon Fund the CCDL

Developed by the Childhood Cancer Data Lab

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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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