E-GEOD-52774
Ontology highlight
ABSTRACT:
PROVIDER: E-GEOD-52774 | ExpressionAtlas | 2025-01-24
REPOSITORIES: ExpressionAtlas
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| E-GEOD-52774-analysis-methods.tsv | Tabular | |||
| E-GEOD-52774-atlasExperimentSummary.Rdata | Rdata | |||
| E-GEOD-52774-configuration.xml | Xml | |||
| E-GEOD-52774-percentile-ranks.tsv | Tabular | |||
| E-GEOD-52774.condensed-sdrf.tsv | Tabular |
Items per page: 5 1 - 5 of 52 |
Similar Datasets
Project description:Gene expression study in CLL of B-cell receptor triggering (mRNA Study)
| PRJNA229996 | ENA
Project description:T Cell Receptor Based Therapy of Metastatic Colorectal Cancer With mRNA-engineered T Cells Targeting Transforming Growth Factor Beta Receptor Type II (TGFβII)
| 2265728 | ecrin-mdr-crc
Project description:Studying the complex relationship between transcription, translation and protein degradation is essential to our understanding of biological processes in health and disease. The limited correlations observed between mRNA and protein abundance suggest pervasive regulation of post-transcriptional steps and support the importance of profiling mRNA levels in parallel to protein synthesis and degradation rates. In this work, we applied an integrative multi-omic approach to study gene expression along the mammalian cell cycle by simultaneously analyzing mRNA levels, translation rates and protein abundance. Our analysis sheds new light on the significant contribution of both mRNA translation and protein degradation to the variance in protein expression. Furthermore, we find that translation regulation plays an important role at S-phase, while progression through mitosis is predominantly controlled by changes in either mRNA levels or protein stability. Specific molecular functions are found to be co-regulated and share similar patterns of mRNA, translation and protein expression along the cell cycle. Notably, these include genes and entire pathways not previously implicated in cell cycle progression, demonstrating the capacity of this approach to identify novel regulatory mechanisms beyond those revealed by traditional expression profiling. Through this three-level analysis, we characterize different mechanisms of gene expression, discover new cycling gene products and highlight the importance and utility of combining datasets generated using different techniques that monitor distinct steps of gene expression.
2017-03-29 | MSV000080771 | MassIVE
Project description:Gene expression study in CLL of B-cell receptor triggering (miRNA study)
| PRJNA229997 | ENA
Project description:Gene expression study in CLL of B-cell receptor triggering
| PRJNA229993 | ENA
Project description:Model destription:
The model describes the stochastic dynamics of two variables, protein and mRNA of a gene with constitutive expression.
Publication:
A New Efficient Approach to Fit Stochastic Models on the Basis of High-throughput Experimental Data Using a Model of IRF7 Gene Expression as Case Study
Authors
Luis U. Aguilera, Christoph Zimmer and Ursula Kummer.
2017-03-17 | MODEL1608100000 | BioModels
Project description:This a model from the article:
Mathematical modelling of prolactin-receptor interaction and the corollary for prolactin receptor gene expression in skin
Soboleva TK, Vetharaniam I, Nixon AJ, Montenegro R, Pearson AJ, Sneyd J. J Theor Biol.
(2005) 234(2); 289-98 15757685
,
Abstract:
A mathematical model of prolactin regulating its own receptors was developed, and compared with experimental data on a qualitative level. The model incorporates the kinetics of prolactin-receptor interactions and subsequent signalling by prolactin-receptor dimers to regulate the production of receptor mRNA and hence the receptor population. The model relates changes in plasma prolactin concentration to prolactin receptor (PRLR) gene expression, and can be used for predictive purposes. The cell signalling that leads to the activation of target genes, and the mechanisms for regulation of transcription, were treated empirically in the model. The model's parameters were adjusted so that model simulations agreed with experimentally observed responses to administration of prolactin in sheep. In particular, the model correctly predicts insensitivity of receptor mRNA regulation to a series of subcutaneous injections of prolactin, versus sensitivity to prolonged infusion of prolactin. In the latter case, response was an acute down-regulation followed by a prolonged up-regulation of mRNA, with the magnitude of the up-regulation increasing with the duration of infusion period. The model demonstrates the feasibility of predicting the in vivo response of prolactin target genes to external manipulation of plasma prolactin, and could provide a useful tool for identifying optimal prolactin treatments for desirable outcomes.
This model was taken from the CellML repository
and automatically converted to SBML.
The original model was:
soboleva, vetharaniam, nixon, montenegro, pearson, sneyd, 2005, version01
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd(at)auckland.ac.nz
The University of Auckland
Auckland Bioengineering Institute
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2005-01-01 | MODEL7896869925 | BioModels
Project description:Intervention type:DRUG. Intervention1:Huaier, Dose form:GRANULES, Route of administration:ORAL, intended dose regimen:20 to 60/day by either bulk or split for 3 months to extended term if necessary. Control intervention1:None.
Primary outcome(s): For mRNA libraries, focus on mRNA studies. Data analysis includes sequencing data processing and basic sequencing data quality control, prediction of new transcripts, differential expression analysis of genes. Gene Ontology (GO) and the KEGG pathway database are used for annotation and enrichment analysis of up-regulated genes and down-regulated genes.
For small RNA libraries, data analysis includes sequencing data process and sequencing data process QC, small RNA distribution across the genome, rRNA, tRNA, alignment with snRNA and snoRNA, construction of known miRNA expression pattern, prediction New miRNA and Study of their secondary structure Based on the expression pattern of miRNA, we perform not only GO / KEGG annotation and enrichment, but also different expression analysis.. Timepoint:RNA sequencing of 240 blood samples of 80 cases and its analysis, scheduled from June 30, 2022..
| 2612481 | ecrin-mdr-crc
Project description:Interventions: Gold Standard:pathological diagnosis;Index test:AQP8
Primary outcome(s): mRNA of AQP8;AQP8 expression level
Study Design: Factorial
| 2704477 | ecrin-mdr-crc
Project description:The purpose of this interventional study is to investigate whether there is evidence that panitumumab in combination with XELOX (capecitabine plus oxaliplatin) chemotherapy will safely increase progression-free survival, above that of XELOX alone in subjects with KRAS wild-type metastatic colorectal cancer who have not responded to or progressed after first line therapy with irinotecan and a fluoropyrimidine.
Further Objectives Exploratory objectives may include investigation of potential correlations between the treatment regimen and epidermal growth factor receptor (EGFR) expression, detection of the functional genetic polymorphisms of the EGFR gene, EGFR gene amplification (FISH), EGFR downstream protein and gene expression parameters, proteomics and epigenetics.
| 2076032 | ecrin-mdr-crc