refine.bio
  • Search
      • Normalized Compendia
      • RNA-seq Sample Compendia
  • Docs
  • About
  • My Dataset
github link
Showing
of 97 results
Sort by

Filters

Technology

Platform

accession-icon GSE17933
Transcriptional Biomarkers to Predict Female Mouse Lung Tumors in Rodent Cancer Bioassays - A 26 Chemical Set
  • organism-icon Mus musculus
  • sample-icon 191 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

The process for evaluating chemical safety is inefficient, costly, and animal intensive. There is growing consensus that the current process of safety testing needs to be significantly altered to improve efficiency and reduce the number of untested chemicals. In this study, the use of short-term gene expression profiles was evaluated for predicting the increased incidence of mouse lung tumors. Animals were exposed to a total of 26 diverse chemicals with matched vehicle controls over a period of three years. Upon completion, significant batch-related effects were observed. Adjustment for batch effects significantly improved the ability to predict increased lung tumor incidence. For the best statistical model, the estimated predictive accuracy under honest five-fold cross-validation was 79.3% with a sensitivity and specificity of 71.4 and 86.3%, respectively. A learning curve analysis demonstrated that gains in model performance reached a plateau at 25 chemicals, indicating that the size of the current data set was sufficient to provide a robust classifier. The classification results showed a small subset of chemicals contributed disproportionately to the misclassification rate. For these chemicals, the misclassification was more closely associated with genotoxicity status than efficacy in the original bioassay. Statistical models were also used to predict dose-response increases in tumor incidence for methylene chloride and naphthalene. The average posterior probabilities for the top models matched the results from the bioassay for methylene chloride. For naphthalene, the average posterior probabilities for the top models over-predicted the tumor response, but the variability in predictions were significantly higher. The study provides both a set of gene expression biomarkers for predicting chemically-induced mouse lung tumors as well as a broad assessment of important experimental and analysis criteria for developing microarray-based predictors of safety-related endpoints.

Publication Title

Use of short-term transcriptional profiles to assess the long-term cancer-related safety of environmental and industrial chemicals.

Sample Metadata Fields

Sex, Age, Specimen part, Disease, Subject

View Samples
accession-icon GSE64449
Gene expression data from Min6 cells grown in serum containing and serum free condtions following Hes3 knock down
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

The basic helix-loop-helix (bHLH) transcription factor hairy and enhancer of split (Hes3) is a member of the Hes/Hey gene family that regulates developmental processes in progenitor cells from various tissues. We demonstrated the Hes3 expression in mouse pancreatic tissue, suggesting it may have a role in modulating beta-cell function. We employed a transfection approach to address specific functions of Hes3. Hes3 RNA interference opposed the growth of the mouse insulinoma cell line Min6. Western blotting and PCR approaches specifically showed that Hes3 RNA interference opposes the expression of Pdx1 and insulin. Likewise, Hes3 knock down reduced evoked insulin release from Min6 cells.

Publication Title

Hes3 is expressed in the adult pancreatic islet and regulates gene expression, cell growth, and insulin release.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE16716
MicroArray Quality Control Phase II (MAQC-II) Project
  • organism-icon Mus musculus, Homo sapiens, Rattus norvegicus
  • sample-icon 1314 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Rat Genome 230 2.0 Array (rat2302), Affymetrix Human Genome U133A Array (hgu133a), Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models

Publication Title

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Sample Metadata Fields

Sex, Age, Specimen part, Race, Compound

View Samples
accession-icon GSE24080
MAQC-II Project: Multiple myeloma (MM) data set
  • organism-icon Homo sapiens
  • sample-icon 549 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a), Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The multiple myeloma (MM) data set (endpoints F, G, H, and I) was contributed by the Myeloma Institute for Research and Therapy at the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA). Gene expression profiling of highly purified bone marrow plasma cells was performed in newly diagnosed patients with MM. The training set consisted of 340 cases enrolled on total therapy 2 (TT2) and the validation set comprised 214 patients enrolled in total therapy 3 (TT3). Plasma cells were enriched by anti-CD138 immunomagnetic bead selection of mononuclear cell fractions of bone marrow aspirates in a central laboratory. All samples applied to the microarray contained more than 85% plasma cells as determined by 2-color flow cytometry (CD38+ and CD45-/dim) performed after selection. Dichotomized overall survival (OS) and eventfree survival (EFS) were determined based on a two-year milestone cutoff. A gene expression model of high-risk multiple myeloma was developed and validated by the data provider and later on validated in three additional independent data sets.

Publication Title

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Sample Metadata Fields

Sex, Age

View Samples
accession-icon GSE24363
MAQC-II Project: NIEHS data set
  • organism-icon Rattus norvegicus
  • sample-icon 410 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Genome 230 2.0 Array (rat2302), Affymetrix Human Genome U133A Array (hgu133a)

Description

The NIEHS data set (endpoint C) was provided by the National Institute of Environmental Health Sciences (NIEHS) of the National Institutes of Health (Research Triangle Park, NC, USA). The study objective was to use microarray gene expression data acquired from the liver of rats exposed to hepatotoxicants to build classifiers for prediction of liver necrosis. The gene expression compendium data set was collected from 418 rats exposed to one of eight compounds (1,2-dichlorobenzene, 1,4-dichlorobenzene, bromobenzene, monocrotaline, N-nitrosomorpholine, thioacetamide, galactosamine, and diquat dibromide). All eight compounds were studied using standardized procedures, i.e. a common array platform (Affymetrix Rat 230 2.0 microarray), experimental procedures and data retrieving and analysis processes.

Publication Title

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Sample Metadata Fields

Sex, Specimen part, Compound

View Samples
accession-icon GSE20194
MAQC-II Project: human breast cancer (BR) data set
  • organism-icon Homo sapiens
  • sample-icon 267 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

The human breast cancer (BR) data set (endpoints D and E) was contributed by the University of Texas M. D. Anderson Cancer Center (MDACC, Houston, TX, USA). Gene expression data from 230 stage I-III breast cancers were generated from fine needle aspiration specimens of newly diagnosed breast cancers before any therapy. The biopsy specimens were collected sequentially during a prospective pharmacogenomic marker discovery study between 2000 and 2008. These specimens represent 70-90% pure neoplastic cells with minimal stromal contamination. Patients received 6 months of preoperative (neoadjuvant) chemotherapy including paclitaxel, 5-fluorouracil, cyclophosphamide and doxorubicin followed by surgical resection of the cancer. Response to preoperative chemotherapy was categorized as a pathological complete response (pCR = no residual invasive cancer in the breast or lymph nodes) or residual invasive cancer (RD), and used as endpoint D for prediction. Endpoint E is the clinical estrogen-receptor status as established by immunohistochemistry. RNA extraction and gene expression profiling were performed in multiple batches over time using Affymetrix U133A microarrays. Genomic analysis of a subset of this sequentially accrued patient population were reported previously. For each endpoint, the first 130 cases were used as a training set and the next 100 cases were used as an independent validation set.

Publication Title

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Sample Metadata Fields

Age, Specimen part, Race

View Samples
accession-icon GSE24061
MAQC-II Project: Hamner data set
  • organism-icon Mus musculus
  • sample-icon 88 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

The Hamner data set (endpoint A) was provided by The Hamner Institutes for Health Sciences (Research Triangle Park, NC, USA). The study objective was to apply microarray gene expression data from the lung of female B6C3F1 mice exposed to a 13-week treatment of chemicals to predict increased lung tumor incidence in the 2-year rodent cancer bioassays of the National Toxicology Program. If successful, the results may form the basis of a more efficient and economical approach for evaluating the carcinogenic activity of chemicals. Microarray analysis was performed using Affymetrix Mouse Genome 430 2.0 arrays on three to four mice per treatment group, and a total of 70 mice were analyzed and used as the MAQC-II's training set (GEO Series GSE6116). Additional data from another set of 88 mice were collected later and provided as the MAQC-II's external validation set (this Series). The training dataset had already been deposited in GEO by its provider and its accession number is GSE6116.

Publication Title

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Sample Metadata Fields

Specimen part, Compound

View Samples
accession-icon GSE17739
Circadian gene profiling in the distal nephron and collecting ducts
  • organism-icon Mus musculus
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Renal excretion of water and major electrolytes exhibits a significant circadian rhythm. This functional periodicity is believed to result, at least in part, from circadian changes in secretion/reabsorption capacities of the distal nephron and collecting ducts. Here, we studied the molecular mechanisms underlying circadian rhythms in the distal nephron segments, i.e. distal convoluted tubule (DCT) and connecting tubule (CNT) and, the cortical collecting duct (CCD). Temporal expression analysis performed on microdissected mouse DCT/CNT or CCD revealed a marked circadian rhythmicity in the expression of a large number of genes crucially involved in various homeostatic functions of the kidney. This analysis also revealed that both DCT/CNT and CCD possess an intrinsic circadian timing system characterized by robust oscillations in the expression of circadian core clock genes (clock, bma11, npas2, per, cry, nr1d1) and clock-controlled Par bZip transcriptional factors dbp, hlf and tef. The clock knockout mice or mice devoid of dbp/hlf/tef (triple knockout) exhibit significant changes in renal expression of several key regulators of water or sodium balance (vasopressin V2 receptor, aquaporin-2, aquaporin-4, alphaENaC). Functionally, the loss of clock leads to a complex phenotype characterized by partial diabetes insipidus, dysregulation of sodium excretion rhythms and a significant decrease in blood pressure. Collectively, this study uncovers a major role of molecular clock in renal function.

Publication Title

Molecular clock is involved in predictive circadian adjustment of renal function.

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon SRP065202
The effect of Heterogeneous Transcription Start Sites (TSS) on the Translatome: Implications for the Mammalian Cellular Phenotype.
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge IconIlluminaGenomeAnalyzerII

Description

Sequencing of total RNA and polysomal RNA of two cell lines, MCF7 (tumoral) and MCF10A (non-tumoral) Overall design: Polysomal and total RNA was prepared from biological triplicates from the two cell lines. For each biological triplicate sub-confluent cell monolyers were lysed to produce cytoplasmic extracts. Half of each extract was fractionated on a sucrose gradient and the polysomal fraction recovered. Polysomal-associated RNA was recovered by Trizol extraction. From the second half of each sample total cyoplasmic RNA was recovered (Trizol extraction).

Publication Title

The effect of heterogeneous Transcription Start Sites (TSS) on the translatome: implications for the mammalian cellular phenotype.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP068418
Targeted deletion of circadian clock gene Arntl in the nephron results in dysregulation of diverse metabolic pathways
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500, Illumina HiSeq 2000

Description

The circadian clock controls a wide variety of metabolic and homeostatic processes in a number of tissues, including the kidney. However, the role of the renal circadian clocks remains largely unknown. To address this question we performed transcriptomic analysis in mice with inducible and conditional ablation of the circadian clock system in the renal tubular cells (Bmal1lox/lox/Pax8-rtTA/LC1 mice). Deep sequencing of the renal transcriptome revealed significant changes in the expression of genes related to metabolic pathways and organic anion transport. In parallel, kidneys from Bmal1lox/lox/Pax8-rtTA/LC1 mice exhibited a significant decrease in the NAD+/NADH ratio suggesting an increased anaerobic glycolysis and/or decreased mitochondrial function. In-depth analysis of two selected pathways revealed (i) a significant increase in plasma urea levels correlating with increased renal arginase 2 (Arg2) activity, hyperargininemia and increase of the kidney arginine content; (ii) a significantly increased plasma creatinine concentration and reduced capacity of the kidney to secrete anionic drugs (furosemide), paralleled by a ~80% decrease in the expression levels of organic anion transporter OAT3 (SLC22a8). Collectively, these results indicate that the renal circadian clocks control a variety of metabolic/homeostatic processes at both the intra-renal and systemic levels and are involved in drug disposition. Overall design: Mice with a specific ablation of the Arntl gene encoding BMAL1 in the renal tubular cells were compared to wild-type littermate at ZT4 and ZT16 (ZT – Zeitgeber time units; ZT0 is the time of light on and ZT12 is the time of light off).

Publication Title

Nephron-Specific Deletion of Circadian Clock Gene Bmal1 Alters the Plasma and Renal Metabolome and Impairs Drug Disposition.

Sample Metadata Fields

Specimen part, Subject, Time

View Samples
...

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

Powered by Alex's Lemonade Stand Foundation

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.

BSD 3-Clause LicensePrivacyTerms of UseContact