Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Gene expression profiles of 400 siRNA knocked down on HUVEC

ABSTRACT: Gene regulatory networks inferred from RNA abundance data have generated significant interest. Several high-profile studies have suggested that gene networks may be useful for identifying molecular pathways related to development, human disease and drug action. However despite, this potential, gene network approaches are used relatively infrequently and their use generally requires input from specialist bioinformaticians. To make their use and evaluation more accessible to non-specialist bioinformaticians and to laboratory scientists, we have assembled a comprehensive but easily used suite of tools for the generation and evaluation of gene regulatory networks, applicable to both microarray and RNA-seq data. We illustrate the use of these tools with two previously unpublished microarray datasets generated in primary human endothelial cells by 400 siRNA-mediated knockdowns. Using these tools and data sets, we reverse-engineered a range of different types of transcriptional regulatory network model, and compared these networks with known molecular pathways reported in the literature.

ORGANISM(S): Homo sapiens

PROVIDER: GSE27869 | GEO | 2011/11/29

SECONDARY ACCESSION(S): PRJNA141887

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Transcriptomic profiles of tissues from rats treated with drug combinations [Study 3]

Project description:Combinations of anticancer agents may have synergistic anti-tumor effects, but enhanced toxicity often limit their clinical use. The risk that combinations of two or more drugs will cause adverse effects that are more severe than drugs used as monotherpies can be hypothesized from comprehensive analysis of each compound’s activity. We generated microarray gene expression data following a single dose of agents administered individually with that of the agents administered in a combination. The key objective of this initiative is to generate and make publicly available key high-content gene expression data sets for mechanistic hypothesis generation for several anticancer drug combinations. The expectation is that availability of tissue-based genomic information that are derived from target tissues will facilitate the generation and testing of mechanistic hypotheses. The view is that availability of these data sets for bioinformaticians and other scientists will contribute to analysis of these data and evaluation of the approach.

2018-09-11 | GSE119133 | GEO

Transcriptomic profiles of tissues from rats treated with drug combinations [Study 2]

2018-09-11 | GSE119129 | GEO

Transcriptomic profiles of tissues from rats treated with drug combinations [Study 1]

2018-09-11 | GSE119122 | GEO

An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network.

Project description:Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism-environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis, we use new methods to integrate transcriptomics data with computational tools to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. To assess the accuracy of our model we tested 2030 predictions and confirmed 1485 regulatory interactions (36% of the full model), thus significantly increasing our understanding of various cell processes, such as spore formation.

2015-03-20 | GSE67023 | GEO

Gallus gallus

Project description:SV tools evaluation

| PRJNA1058414 | ENA

Transcriptome and proteome derived networks reveal distinct and complementary gene relationships - additional phosphoproteomics data

Project description:Co-expression networks and gene regulatory networks (GRNs) are emerging as important tools for predicting the functional roles of individual genes at a system-wide scale. To enable network reconstructions we built a large-scale gene expression atlas comprised of 62,547 mRNAs, 17,862 non-modified proteins, and 6,227 phosphoproteins harboring 31,595 phosphorylation sites quantified across maize development. There was little edge conservation in co-expression and GRNs reconstructed using transcriptome versus proteome data yet networks from either data type were enriched in ontological categories and effective in predicting known regulatory relationships. This integrated gene expression atlas provides a valuable community resource. The networks should facilitate plant biology research and they provide a conceptual framework for future systems biology studies highlighting the importance of studying gene regulation at several levels.

| MSV000079303 | MassIVE

Transcriptome and proteome derived networks reveal distinct and complementary gene relationships

| MSV000079290 | MassIVE

Reconstruction of the dynamic regulatory network that controls Th17 cell differentiation by systematic perturbation in primary cells (ChIP-Seq)

Project description:Despite their enormous importance, the molecular circuits that control the differentiation of Th17 cells remain largely unknown. Recent studies have reconstructed regulatory networks in mammalian cells, but have focused on short-term responses and relied on perturbation approaches that cannot be applied to primary T cells. Here, we develop a systematic strategy – combining transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing gene perturbations in primary T cells – to derive and experimentally validate a temporal model of the dynamic regulatory network that controls Th17 differentiation. The network is arranged into two self-reinforcing and mutually antagonistic modules that either suppress or promote Th17 differentiation. The two modules contain 12 novel regulators with no previous implication in Th17 differentiation, which may be essential to maintain the appropriate balance of Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors that are embedded within a comprehensive temporal network and identifies novel drug targets and organizational principles for the differentiation of Th17 cells. DNA binding of TSC22D3 in Th17 cells compared to WCE

2013-03-07 | E-GEOD-43949 | biostudies-arrayexpress

Reconstruction of the dynamic regulatory network that controls Th17 cell differentiation by systematic perturbation in primary cells (Th17 differentiation timecourse)

Project description:Despite their enormous importance, the molecular circuits that control the differentiation of Th17 cells remain largely unknown. Recent studies have reconstructed regulatory networks in mammalian cells, but have focused on short-term responses and relied on perturbation approaches that cannot be applied to primary T cells. Here, we develop a systematic strategy – combining transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing gene perturbations in primary T cells – to derive and experimentally validate a temporal model of the dynamic regulatory network that controls Th17 differentiation. The network is arranged into two self-reinforcing and mutually antagonistic modules that either suppress or promote Th17 differentiation. The two modules contain 12 novel regulators with no previous implication in Th17 differentiation, which may be essential to maintain the appropriate balance of Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors that are embedded within a comprehensive temporal network and identifies novel drug targets and organizational principles for the differentiation of Th17 cells. Time course microarray data for Th17 differentiation, including Th0 control

2013-03-07 | E-GEOD-43955 | biostudies-arrayexpress

Genetic reconstruction of a functional transcriptional regulatory network

Project description:Although global analyses of transcription factor binding provide one view of potential transcriptional regulatory networks, regulation also occurs at levels distinct from transcription factor binding. Here, we use a genetic approach to identify targets of transcription factors in yeast and reconstruct a functional regulatory network. First, we profiled transcriptional responses in S. cerevisiae strains with individual deletions of 263 transcription factors. Then we used directed-weighted graph modeling and regulatory epistasis analysis to identify indirect regulatory relationships between these transcription factors, and from this we reconstructed a functional transcriptional regulatory network. The enrichment of promoter motifs and Gene Ontology annotations provide insight into the biological functions of the transcription factors. Data values are X scores and corresponding p-values derived using an error model. We profiled transcriptional responses upon the individual deletion of more than 260 TFs. We used a directed-weighted graph modelling approach and regulatory epistasis analysis to identify indirect regulatory relationships, and thus constructed a refined, functional transcriptional regulatory network.

2010-07-02 | E-GEOD-4654 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data