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accession-icon E-MEXP-271
Transcription profiling of human hep2 cells infected with Streptococcus pyogenes over time
  • organism-icon Homo sapiens
  • sample-icon 40 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133B Array (hgu133b), Affymetrix Human Genome U133A Array (hgu133a)

Description

Two biological replicate experiments were performed to estimate the bias of the gene expression pattern of infected and non-infected HEp-2 cells. Microarrays hybridized with RNA from 2 h of non-infected HEp-2 cells were used as reference chips for the comparison with microarrays hybridized with RNA from 2 h and 4 h of eukaryotic cells exposed to wt-bacteria and .fasX-mutant. As a reference for chips hybridized with RNA prepared from 6 h p. i. and 8 h p. i. of both GAS-infected HEp-2 cells we used chips that were hybridized with RNA isolated from non-infected cells 8 h p. i. We also compared the microarray data from 2 h of non-infected HEp-2 cells with those from 8 h of non-infected HEp-2 cells to determine the influence of the extended culture on the non-infected cells. Only such genes which were differentially regulated after infection with wt-bacteria and .fasX-mutant infected cells and not differentially present in unequal amounts between the 2 h and 8 h of controls were included in the subsequent statistical analysis.

Publication Title

Global epithelial cell transcriptional responses reveal Streptococcus pyogenes Fas regulator activity association with bacterial aggressiveness.

Sample Metadata Fields

Disease, Disease stage, Cell line, Time

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accession-icon SRP192234
Mus musculus strain:C57/BL6 | breed:B6 Raw sequence reads
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge IconIllumina Genome Analyzer

Description

to compare the gene expression between naive, non-Tfh, Tfh and GC-Tfh cells

Publication Title

No associated publication

Sample Metadata Fields

Sex, Specimen part, Cell line

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accession-icon GSE30101
Genome-wide profiling of whole blood from healthy adult volunteers before and after receiving non-live vaccines including seasonal influenza or pneumococcal vaccine or placebo (saline) injections
  • organism-icon Homo sapiens
  • sample-icon 693 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V3.0 expression beadchip

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Systems scale interactive exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal vaccines.

Sample Metadata Fields

Sex, Age, Race, Subject

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accession-icon GSE48762
Genome-wide profiling of whole blood from healthy adult volunteers before and after receiving non-live vaccines including seasonal influenza or pneumococcal vaccine or placebo (saline) injections II
  • organism-icon Homo sapiens
  • sample-icon 621 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V3.0 expression beadchip

Description

The objective of this study is to: 1) Characterize the immune responsiveness to administration of non-live vaccines in three cohorts of healthy adult subjects through the analysis of blood leukocytes transcriptional profiles. 2) Validate whole blood transcriptional profiles generated from standard 3mL blood draws versus 200uL blood draws obtained by finger stick. 3) Discover potential biomarkers for immune-responsiveness to non-live vaccines.

Publication Title

Systems scale interactive exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal vaccines.

Sample Metadata Fields

Sex, Age, Race, Subject

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accession-icon GSE11907
A Modular Analysis Framework for Blood Genomics Studies: Application to Systemic Lupus Erythematosus
  • organism-icon Homo sapiens
  • sample-icon 340 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

The analysis of patient blood transcriptional profiles offers a means to investigate the immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. These transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus. Thus, this work describes the implementation and application of a methodology designed to support systems-scale analysis of the human immune system in translational research settings.

Publication Title

A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus.

Sample Metadata Fields

Sex, Age, Race

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accession-icon GSE19491
Blood Transcriptional Profiles in Human Active and Latent Tuberculosis
  • organism-icon Homo sapiens
  • sample-icon 498 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V3.0 expression beadchip

Description

This series regroups different datasets (training set, test set, validation set, longitudinal set, separated cell set) to identify and characterise a specific transcriptional signature for patients with active TB, distinct from patients with latent TB and healthy controls. The training set dataset was used to identify a whole blood transcriptional signature for active TB patients in London, across a range of ethnicity. This signature was then validated in an independent cohort of patients, also recruited in London (the test set), and then further confirmed in an additional independent cohort recruited in Cape Town, South Africa (validation set), in order to confirm that the defined signature was present in both high (Cape Town, South Africa) and medium incidence regions (London, UK). The longitudinal dataset was then used to explore how successful TB treatment modifies this transcriptional signature. The separated cell set compares the transcriptional profiles in purified cell subsets (neutrophils, monocytes and T cells) to assess which cell types are contributing to the whole blood signature, and in what way. These studies may ultimately help to improve the diagnosis of active tuberculosis which normally relies on culture of the bacilli, which can take up to 6 weeks, and sometimes the bacilli cannot be obtained from sputum thus requiring invasive techniques such as bronchoalveolar lavage (BAL). In some cases (30%) the bacill cannot be grown from sputum or BAL. Any diagnostic tool would need to be valid across a range of ethnicities, and be valid in both high and low incidence countries. A further aim was to determine whether latent TB patients have a distinct homogeneous or heterogeneous signature, since it is not currently possible to determine using present tests (Tuberculin skin test - TST - or MTb antigen responsiveness of blood cells to produce IFN-gamma - IGRA assay) whether the mycobacteria have been cleared, are still present but are controlled by an active immune response, or to predict which patients will develop active TB. Defining heterogeneity in the latent TB patients would be an important step in developing diagnostics which could detect those most at risk of developing active TB, and thus enable targeted preventive therapy. The latter situation may be determined if Latent patients have a blood transcriptional signature similar to that in Active patients. The transcriptional signature in whole blood and cell subsets from Active TB patients may also provide information as to the factors leading to immunopathogenesis, thus possibly identifying therapeutic targets. The transcriptional profile in latent TB may give information regarding protective factors controlling the infection, important for vaccine development. Finally, definition of a transcriptional signature which responds to therapy could facilitate the development of surrogate biomarkers for drug or vaccine studies.

Publication Title

An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis.

Sample Metadata Fields

Sex, Age, Race

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accession-icon GSE11908
Construction of a modular analysis framework for blood Genomics Studies
  • organism-icon Homo sapiens
  • sample-icon 271 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data.

Publication Title

A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus.

Sample Metadata Fields

Sex, Age, Race

View Samples
accession-icon GSE29536
Whole Blood Transcriptional Modules generated on Illumina Hu-6 V2 Beadchips
  • organism-icon Homo sapiens
  • sample-icon 410 Downloadable Samples
  • Technology Badge IconIllumina human-6 v2.0 expression beadchip

Description

This dataset was used to establish whole blood transcriptional modules (n=260) that represent groups of coordinately expressed transcripts that exhibit altered abundance within individual datasets or across multiple datasets. This modular framework was generated to reduce the dimensionality of whole blood microarray data processed on the Illumina Beadchip platform yielding data-driven transcriptional modules with biologic meaning.

Publication Title

Interferon signature in the blood in inflammatory common variable immune deficiency.

Sample Metadata Fields

Disease

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accession-icon GSE25742
Genome-wide profiling of whole blood from patients with defects in Toll-like receptors (TLRs) and IL-1Rs (the TIR pathway) signaling
  • organism-icon Homo sapiens
  • sample-icon 365 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

The objective of this study is to: 1) Characterize the innate immune responsiveness of patients with inborn errors in Toll-IL1 receptor signaling pathway (IRAK4, MyD88 deficiencies) compared to healthy subjects, through the analysis of blood leukocytes' transcriptional profiles after stimulation with ligands for the whole set of Toll-like receptors and IL-1Rs plus whole bacteria. 2) Understand the redundancies in TLR pathway in humans. 3) Explore the use of blood profiling approaches to assess the immune status of an individual by using Primary Immune Deficiencies as a proof of principle.

Publication Title

A narrow repertoire of transcriptional modules responsive to pyogenic bacteria is impaired in patients carrying loss-of-function mutations in MYD88 or IRAK4.

Sample Metadata Fields

Sex, Race

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accession-icon GSE46734
DALIA-1 Whole Blood Longitudinal Gene Expression
  • organism-icon Homo sapiens
  • sample-icon 330 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

The DALIA-1 trial was designed to evaluate the safety and immunogenicity of a DC-based vaccine generated by culturing blood monocytes with GM-CSF and IFN-a. DCs were pulsed for 24 hrs with the ANRS LIPO5 peptide pool and activated for the last 6 hrs with LPS. In total, 19 asymptomatic HAART-treated patients were included in the study. Microarray samples were collected in both the treatment phase and follow-up phase of the study.

Publication Title

No associated publication

Sample Metadata Fields

Specimen part, Subject

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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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