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accession-icon GSE22931
Transcript profiling in the liver of piglets fed carnitine
  • organism-icon Sus scrofa
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Porcine Genome Array (porcine)

Description

Carnitine is a water soluble quaternary amine which is essential for normal function of all tissues.

Publication Title

Effect of L-carnitine on the hepatic transcript profile in piglets as animal model.

Sample Metadata Fields

Sex, Age, Specimen part

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accession-icon GSE65088
Biomarker-based classification of bacterial and fungal whole-blood infections in a genome-wide expression study
  • organism-icon Homo sapiens
  • sample-icon 57 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

Sepsis is a clinical syndrome that can be caused by bacteria or fungi. Early knowledge on the nature of the causative agent is a prerequisite for targeted anti-microbial therapy. Besides currently used detection methods like blood culture and PCR-based assays, the analysis of the transcriptional response of the host to infecting organisms holds great promise. In this study, we aim to examine the transcriptional footprint of infections caused by the bacterial pathogens Staphylococcus aureus and Escherichia coli and the fungal pathogens Candida albicans and Aspergillus fumigatus in a human whole-blood model. Moreover, we use the expression information to build a random forest classifier to determine if the pathogen is bacterial, fungal or neither of the two. After normalizing the transcription intensities using stably expressed reference genes, we filtered the gene set for biomarkers of bacterial or fungal blood infections. This selection is based on differential expression and an additional gene relevance measure. In this way, we identified 38 biomarker genes, including IL6, SOCS3, and IRG1 which were already associated to sepsis by other studies. Using these genes, we trained the classifier and assessed its performance. It yielded a 96% accuracy (sensitivities >93%, specificities >97%) for a 10-fold stratified cross-validation and a 92% accuracy (sensitivities and specificities >83%) for an additional dataset comprising Cryptococcus neoformans infections. Furthermore, the noise-robustness of the classifier suggests high rates of correct class predictions on datasets of new species. In conclusion, this genome-wide approach demonstrates an effective feature selection process in combination with the construction of a well-performing classification model. Further analyses of genes with pathogen-dependent expression patterns can provide insights into the systemic host responses, which may lead to new anti-microbial therapeutic advances.

Publication Title

Biomarker-based classification of bacterial and fungal whole-blood infections in a genome-wide expression study.

Sample Metadata Fields

Sex, Specimen part, Subject, Time

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accession-icon GSE78000
Genome-Wide Expression Profiling Reveals S100B as Biomarker for Invasive Aspergillosis
  • organism-icon Homo sapiens
  • sample-icon 44 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U219 Array (hgu219)

Description

Invasive aspergillosis (IA) is a devastating opportunistic infection and its treatment constitutes a considerable burden for the health care system. Immunocompromised patients are at an increased risk for IA, which is mainly caused by the species Aspergillus fumigatus. An early and reliable diagnosis is required to initiate the appropriate antifungal therapy. However, diagnostic sensitivity and accuracy still needs to be improved, which can be achieved at least partly by the definition of new biomarkers. Besides the direct detection of the pathogen by the current diagnostic methods, the analysis of the host response is a promising strategy towards this aim. Following this approach, we sought to identify new biomarkers for IA. For this purpose, we analyzed gene expression profiles of haematological patients and compared profiles of patients suffering from IA with non-IA patients. Based on microarray data, we applied a comprehensive feature selection using a random forest classifier. We identified the transcript coding for the S100 calcium-binding protein B (S100B) as a potential new biomarker for the diagnosis of IA. Considering the expression of this gene, we were able to classify samples from patients with IA with 82.3% sensitivity and 74.6% specificity. Moreover, we validated the expression of S100B in a real-time RT-PCR assay and we also found a down-regulation of S100B in A.fumigatus stimulated DCs. An influence on the IL1B and CXCL1 downstream levels was demonstrated by this S100B knockdown. In conclusion, this study covers an effective feature selection revealing a key regulator of the human immune response during IA. S100B may represent an additional diagnostic marker that in combination with the established techniques may improve the accuracy of IA diagnosis.

Publication Title

Genome-Wide Expression Profiling Reveals S100B as Biomarker for Invasive Aspergillosis.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE58203
Stimulation of RA SFBs with IL1 or PDGF-D
  • organism-icon Homo sapiens
  • sample-icon 59 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Synovial fibroblasts of 6 RA patients were treated with IL1 or PDGF-D. The aim of this study was to outline mechanism of the disease RA by a treatment with one of these cytokines.

Publication Title

Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients.

Sample Metadata Fields

Treatment, Subject, Time

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accession-icon GSE57542
Expression data measured by Nanostring and microarray of monocyte-derived dendritic cells from healthy individuals stimulated with LPS, influenza, or IFN-beta, or left unstimulated
  • organism-icon Homo sapiens
  • sample-icon 228 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Common genetic variants modulate pathogen-sensing responses in human dendritic cells.

Sample Metadata Fields

Sex, Age, Race, Subject

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accession-icon GSE36139
SNP and Expression data from the Cancer Cell Line Encyclopedia (CCLE)
  • organism-icon Homo sapiens
  • sample-icon 882 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

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE36133
Expression data from the Cancer Cell Line Encyclopedia (CCLE)
  • organism-icon Homo sapiens
  • sample-icon 882 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The Cancer Cell Line Encyclopedia (CCLE) project is a collaboration between the Broad Institute, the Novartis Institutes for Biomedical Research and the Genomics Novartis Foundation to conduct a detailed genetic and pharmacologic characterization of a large panel of human cancer models

Publication Title

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE17312
BI Human Reference Epigenome Mapping Project
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The NIH Roadmap Epigenomics Mapping Consortium aims to produce a public resource of epigenomic maps for stem cells and primary ex vivo tissues selected to represent the normal counterparts of tissues and organ systems frequently involved in human disease.

Publication Title

The NIH Roadmap Epigenomics Mapping Consortium.

Sample Metadata Fields

Sex, Specimen part, Disease, Subject

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accession-icon GSE5258
Connectivity Map dataset (build01)
  • organism-icon Homo sapiens
  • sample-icon 346 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

A reference collection of genome-wide transcriptional expression data for bioactive small molecules.

Publication Title

The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE26863
MMRC expression and aCGH reference collection
  • organism-icon Homo sapiens
  • sample-icon 299 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

Initial genome sequencing and analysis of multiple myeloma.

Sample Metadata Fields

Specimen part, Disease, Disease stage

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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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