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accession-icon GSE21383
Expression data from porcine ovary tissue of sows from two prolificacy levels
  • organism-icon Sus scrofa
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Porcine Genome Array (porcine)

Description

Previous results from a genome scan in a F2 Iberian by Meishan intercross showed several chromosome regions associated with litter size traits. In order to identify candidate genes underlying these QTL we have performed an ovary gene expression analysis during pregnancy. F2 sows were ranked by their estimated breeding values for prolificacy, the six sows with higher EBV (HIGH prolificacy) and the six with lower EBV (LOW prolificacy) were selected. Samples were hybridized to Affymetrix porcine expression microarrays. The statistical analysis with a mixed-model approach identified 221 differentially expressed probes, representing 189 genes. These genes were functionally annotated in order to identify the genetic pathways overrepresented. Among the most represented functional groups the first one was immune system response activation against external stimulus. The second group was made up of genes which regulate the maternal homeostasis by complement and coagulation cascades. The last group was involved on lipid and fatty acid enzymes of metabolic processes, which participate in steroidogenesis pathway. In order to identify powerful candidate genes for prolificacy, the second approach of this study was merging microarray data with position information of QTL affecting litter size, previously detected in the same experimental cross. According to this, we have identified 27 differentially expressed genes co-localized with QTL for litter size traits, which fulfill the biological, positional and functional criteria.

Publication Title

Differential gene expression in ovaries of pregnant pigs with high and low prolificacy levels and identification of candidate genes for litter size.

Sample Metadata Fields

Specimen part

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accession-icon SRP108341
TrapSeq: An RNA Sequencing-based pipeline for the identification of genetrap insertions in mammalian cells
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Current pipelines used to map genetrap insertion sites are based on inverse- or splinkerette-PCR methods, which despite their efficacy are prone to artifacts and do not provide information on the impact of the genetrap on the expression of the targeted gene. We developed a new method, which we named TrapSeq, for the mapping of genetrap insertions based on paired-end RNA sequencing. By recognizing chimeric mRNAs containing genetrap sequences spliced to an endogenous exon, our method identifies insertions that lead to productive trapping. Overall design: We conducted two independent screenings for sensitivity against 6-thioguanine (6TG) and an ATR inhibitor (ATRi). We applied our RNAseq-based pipeline (TrapSeq) to identify mutations that provide resistance to these reagents. Importantly, and besides its use for screenings, when applied to individual clones our method provides a fast and cost-effective way that not only identifies the insertion site of the genetrap but also reveals the impact of the insertion on the expression of the trapped gene. Please note that HAP1, haploid for all chromosomes, derives from near-haploid KBM7 parent line which was in turn obtained from a chronic myeloid leukemia patient in blast crisis phase (Carette et al. Nature 477:340-343, 2011).

Publication Title

Trap<sup>Seq</sup>: An RNA Sequencing-Based Pipeline for the Identification of Gene-Trap Insertions in Mammalian Cells.

Sample Metadata Fields

Specimen part, Cell line, Subject

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accession-icon GSE76710
Inferring gene regulatory networks that control maintenance and identity of the Arabidopsis root stem cells
  • organism-icon Arabidopsis thaliana
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Arabidopsis ATH1 Genome Array (ath1121501)

Description

We isolated QC and xylem cells by sorting GFP+ cells marked with pWOX5::GFP and pTMO5::GFP respectively.

Publication Title

Predicting gene regulatory networks by combining spatial and temporal gene expression data in &lt;i&gt;Arabidopsis&lt;/i&gt; root stem cells.

Sample Metadata Fields

Specimen part

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accession-icon GSE74194
Muscle Transcriptome Profile of Resistance Exercise is Augmented by Aerobic Exercise
  • organism-icon Homo sapiens
  • sample-icon 19 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.1 ST Array (hugene21st)

Description

10 male subjects performed ~45 min one-legged cycling and 4 x 7 maximal concentric-eccentric knee extensions for each leg 15 min later. Thus, one limb performed aerobic and resistance exercise (AE+RE), while the opposing leg did resistance exercise only (RE). Biopsies were obtained from m. vastus lateralis of each leg 3 h after the resistance exercise bout.

Publication Title

Aerobic exercise augments muscle transcriptome profile of resistance exercise.

Sample Metadata Fields

Sex, Specimen part, Treatment, Subject

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accession-icon GSE24142
Gene expression analysis of adult and fetal T-cell progenitors
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)

Description

Development of T-cells provides a unique opportunity to study cell-fate determination due to the accessability and the well defined stages of development. In order to understand the genetic programs underlying fetal and adult Tcell fate specification we subjected highly purified fetal and adult T-cell progenitor populations to a genomewide transcriptional analysis. The aim was to identify molecular elements that govern T-cell fate specification as a whole but ultimately to isolate elements that were specific for a given population in a specific developmental window.

Publication Title

Global transcriptional analysis of primitive thymocytes reveals accelerated dynamics of T cell specification in fetal stages.

Sample Metadata Fields

Sex

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accession-icon GSE65945
Transcriptional profiling of proliferating and differentiating SPC04 human neural stem cell line
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

Here we used microarray expression profiling to characterise global changes in gene expression during stages of proliferation and differentiation of human neural stem cells

Publication Title

Associations of the Intellectual Disability Gene MYT1L with Helix-Loop-Helix Gene Expression, Hippocampus Volume and Hippocampus Activation During Memory Retrieval.

Sample Metadata Fields

Specimen part, Cell line

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accession-icon GSE14758
Expression data from mediastinal lymph nodes of piglets experimentally infected with porcine circovirus type 2 (PCV2)
  • organism-icon Sus scrofa
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Porcine Genome Array (porcine)

Description

This study aimed to characterize differences in gene expression in piglets inoculated with porcine circovirus type 2 (PCV2), the essential causative agent of postweaning multisystemic wasting syndrome (PMWS). Comparisons between control and PCV2-inoculated pigs were done at five different time points: 1, 2, 5, 8, and 29 days post-inoculation.

Publication Title

Time course differential gene expression in response to porcine circovirus type 2 subclinical infection.

Sample Metadata Fields

Age, Specimen part

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accession-icon GSE14900
Transcriptional response of human cells to the absence of mitochondrial DNA
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

Mitochondrial biogenesis is under the control of two different genetic systems: the nuclear genome (nDNA) and the mitochondrial genome (mtDNA). mtDNA is a circular genome of 16.6 kb encoding 13 of the approximately 90 subunits that form the respiratory chain, the remaining ones being encoded by the nuclear genome (nDNA). Eukaryotic cells are able to monitor and respond to changes in mitochondrial function through alterations in nuclear gene expression, a phenomenon first defined in yeast and known as retrograde regulation. With this experiment we aimed to identify the set of nuclear genes that significantly change their expression level in response to depletion of mtDNA.

Publication Title

How do human cells react to the absence of mitochondrial DNA?

Sample Metadata Fields

Cell line

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accession-icon GSE56371
Transcriptional profiling reveals functional links between RasGrf1 and Pttg1 in pancreatic beta cells
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Expression 430A Array (moe430a)

Description

We used microarrays to investigate gene expression changes in the pancreas of RasGrf1 KO mice. These animals have a reduction in the number and size of the pancreatic islets which lead to lower levels of insulin and glucagon in their blood.

Publication Title

Transcriptional profiling reveals functional links between RasGrf1 and Pttg1 in pancreatic beta cells.

Sample Metadata Fields

Specimen part

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accession-icon GSE10898
Transcriptome architecture across tissues in the pig
  • organism-icon Sus scrofa
  • sample-icon 63 Downloadable Samples
  • Technology Badge Icon Affymetrix Porcine Genome Array (porcine)

Description

Artificial selection has resulted in animal breeds with extreme phenotypes. As an organism is made up of many different tissues and organs, each with its own genetic programme, it is pertinent to ask what are the relative contributions of breed or sex when assessed across tissues.

Publication Title

Transcriptome architecture across tissues in the pig.

Sample Metadata Fields

Age

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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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