Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Genoscape: a Cytoscape plug-in to automate the retrieval and integration of gene expression data and molecular networks.

ABSTRACT: UNLABELLED: Genoscape is an open-source Cytoscape plug-in that visually integrates gene expression data sets from GenoScript, a transcriptomic database, and KEGG pathways into Cytoscape networks. The generated visualisation highlights gene expression changes and their statistical significance. The plug-in also allows one to browse GenoScript or import transcriptomic data from other sources through tab-separated text files. Genoscape has been successfully used by researchers to investigate the results of gene expression profiling experiments. AVAILABILITY: Genoscape is an open-source software freely available from the Genoscape webpage (http://www.pasteur.fr/recherche/unites/Gim/genoscape/). Installation instructions and tutorial can also be found at this URL.

SUBMITTER: Clement-Ziza M

PROVIDER: S-EPMC2752617 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Genoscape: a Cytoscape plug-in to automate the retrieval and integration of gene expression data and molecular networks.

Clément-Ziza Mathieu M Malabat Christophe C Weber Christian C Moszer Ivan I Aittokallio Tero T Letondal Catherine C Rousseau Sandrine S

Bioinformatics (Oxford, England) 20090803 19

<h4>Unlabelled</h4>Genoscape is an open-source Cytoscape plug-in that visually integrates gene expression data sets from GenoScript, a transcriptomic database, and KEGG pathways into Cytoscape networks. The generated visualisation highlights gene expression changes and their statistical significance. The plug-in also allows one to browse GenoScript or import transcriptomic data from other sources through tab-separated text files. Genoscape has been successfully used by researchers to investigate ...[more]

PMID: 19654116

Similar Datasets

Integration of biological networks and gene expression data using Cytoscape.

Project description:Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.

| S-EPMC3685583 | biostudies-literature

ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks.

Project description:We have developed ClueGO, an easy to use Cytoscape plug-in that strongly improves biological interpretation of large lists of genes. ClueGO integrates Gene Ontology (GO) terms as well as KEGG/BioCarta pathways and creates a functionally organized GO/pathway term network. It can analyze one or compare two lists of genes and comprehensively visualizes functionally grouped terms. A one-click update option allows ClueGO to automatically download the most recent GO/KEGG release at any time. ClueGO provides an intuitive representation of the analysis results and can be optionally used in conjunction with the GOlorize plug-in.

| S-EPMC2666812 | biostudies-other

Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks.

Project description:SummaryMetscape is a plug-in for Cytoscape, used to visualize and interpret metabolomic data in the context of human metabolic networks. We have developed a metabolite database by extracting and integrating information from several public sources. By querying this database, Metscape allows users to trace the connections between metabolites and genes, visualize compound networks and display compound structures as well as information for reactions, enzymes, genes and pathways. Applying the pathway filter, users can create subnetworks that consist of compounds and reactions from a given pathway. Metscape allows users to upload experimental data, and visualize and explore compound networks over time, or experimental conditions. Color and size of the nodes are used to visualize these dynamic changes. Metscape can display the entire metabolic network or any of the pathway-specific networks that exist in the database.AvailabilityMetscape can be installed from within Cytoscape 2.6.x under 'Network and Attribute I/O' category. For more information, please visit http://metscape.ncibi.org/tryplugin.html.

| S-EPMC2844990 | biostudies-literature

CryGetter: a tool to automate retrieval and analysis of Cry protein data.

Project description:For many years, the use of chemical agents to control crop pests has been degrading the environment, bringing problems to humans and all living things. An alternative to deal with the pests is the use of biopesticides, biological agents capable of controlling these harmful organisms. One kind of biopesticide is Bacillus thuringiensis, a Gram-positive bacterium that synthesizes a protein that, when ingested by the pests, kills them and does not harm other species.Since the economical importance of Bacillus thuringiensis and its proteins significance, this work presents a software tool, called CryGetter, that is capable of retrieving data related to these proteins, store it and present it in a user friendly manner. The tool also aims to align the protein sequences and generate reports containing some statistical data concerning the alignments that were made.CryGetter was created to help researchers of Bacillus thuringiensis and its proteins to speed up their data retrieval and analysis, allowing them to generate more accurate results. In this sense, the tool circumvents the error prone task of manually getting all the necessary data and processing them in various software systems to get the same result as CryGetter gets in a unique semiautomatic environment.

| S-EPMC5004295 | biostudies-literature

iRefScape. A Cytoscape plug-in for visualization and data mining of protein interaction data from iRefIndex.

Project description:BACKGROUND: The iRefIndex consolidates protein interaction data from ten databases in a rigorous manner using sequence-based hash keys. Working with consolidated interaction data comes with distinct challenges: data are redundant, overlapping, highly interconnected and may be collected and represented using different curation practices. These phenomena were quantified in our previous studies. RESULTS: The iRefScape plug-in for the Cytoscape graphical viewer addresses these challenges. We show how these factors impact on data-mining tasks and how our solutions resolve them in a simple and efficient manner. A uniform accession space is used to limit redundancy and support search expansion and searching on multiple accession types. Multiple node and edge features support data filtering and mining. Node colours and features supply information about search result provenance. Overlapping evidence is presented using a multi-graph and a bi-partite representation is used to distinguish binary and n-ary source data. Searching for interactions between sets of proteins is supported and specifically includes searches on disease-related genes found in OMIM. Finally, a synchronized adjacency-matrix view facilitates visualization of relationships between sets of user defined groups. CONCLUSIONS: The iRefScape plug-in will be of interest to advanced users of interaction data. The plug-in provides access to a consolidated data set in a uniform accession space while remaining faithful to the underlying source data. Tools are provided to facilitate a range of tasks from a simple search to knowledge discovery. The plug-in uses a number of strategies that will be of interest to other plug-in developers.

| S-EPMC3228863 | biostudies-literature

KEGGscape: a Cytoscape app for pathway data integration.

Project description:In this paper, we present KEGGscape a pathway data integration and visualization app for Cytoscape ( http://apps.cytoscape.org/apps/keggscape). KEGG is a comprehensive public biological database that contains large collection of human curated pathways. KEGGscape utilizes the database to reproduce the corresponding hand-drawn pathway diagrams with as much detail as possible in Cytoscape. Further, it allows users to import pathway data sets to visualize biologist-friendly diagrams using the Cytoscape core visualization function (Visual Style) and the ability to perform pathway analysis with a variety of Cytoscape apps. From the analyzed data, users can create complex and interactive visualizations which cannot be done in the KEGG PATHWAY web application. Experimental data with Affymetrix E. coli chips are used as an example to demonstrate how users can integrate pathways, annotations, and experimental data sets to create complex visualizations that clarify biological systems using KEGGscape and other Cytoscape apps.

| S-EPMC4141640 | biostudies-other

dot-app: a Graphviz-Cytoscape conversion plug-in.

Project description:dot-app is a Cytoscape 3 app that allows Cytoscape to import and export Graphviz (.dot, .gv) files, also known as DOT files due to the .dot extension and their conformance to the DOT language syntax. The DOT format was originally created in the early 2000s to represent graph topologies, layouts and formatting. DOT-encoded files are produced and consumed by a number of open-source graph applications, including Graphviz, Gephi, Tulip, and others. While DOT-based graph applications are popular, they emphasize general graph layout and styling over the topological and semantic analysis functions available in domain-focused applications such as Cytoscape. While domain-focused applications have easy access to large networks (10,000 to 100,000 nodes) and advanced analysis and formatting, they do not have as many styling options as the Graphviz software suite. dot-app enables the interchange of networks between Cytoscape and DOT-compatible applications so that users can benefit from the features of both. dot-app was first deployed to the Cytoscape App Store in August 2015, has since registered more than 1,200 downloads, and has been highly rated by more than 20 users.

| S-EPMC5105883 | biostudies-literature

Reconciling differential gene expression data with molecular interaction networks.

Project description:MotivationMany techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease.ResultsTo avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression P-values such that two genes connected by an interaction show similar changes in their gene expression values. We provide a novel evaluation of four methods from this class of algorithms. We enumerate three desirable properties that this class of algorithms should address. These properties seek to maintain that the returned gene rankings are specific to the condition being studied. Moreover, to ease interpretation, highly ranked genes should participate in coherent network structures and should be functionally enriched with relevant biological pathways. We comprehensively evaluate the extent to which each algorithm addresses these properties on a compendium of gene expression data for 54 diverse human diseases. We show that the reconciled gene rankings can identify novel disease-related functions that are missed by analyzing expression data alone.AvailabilityC++ software implementing our algorithms is available in the NetworkReconciliation package as part of the Biorithm software suite under the GNU General Public License: http://bioinformatics.cs.vt.edu/?murali/software/biorithm-docs.

| S-EPMC3582271 | biostudies-literature

Integration of steady-state and temporal gene expression data for the inference of gene regulatory networks.

Project description:We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

| S-EPMC3743784 | biostudies-literature

Dynamic changes in protein functional linkage networks revealed by integration with gene expression data.

Project description:Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein:protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein:protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data.

| S-EPMC2580820 | biostudies-literature

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