EXamine: Visualizing annotated networks in Cytoscape.
Ontology highlight
ABSTRACT:
SUBMITTER: Spohr P
PROVIDER: S-EPMC6013758 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BACKGROUND: Biological networks have a growing importance for the interpretation of high-throughput "omics" data. Integrative network analysis makes use of statistical and combinatorial methods to extract smaller subnetwork modules, and performs enrichment analysis to annotate the modules with ontology terms or other available knowledge. This process results in an annotated module, which retains the original network structure and includes enrichment information as a set system. A major bottleneck is a lack of tools that allow exploring both network structure of extracted modules and its annotations. RESULTS: This paper presents a visual analysis approach that targets small modules with many set-based annotations, and which displays the annotations as contours on top of a node-link diagram. We introduce an extension of self-organizing maps to lay out nodes, links, and contours in a unified way. An implementation of this approach is freely available as the Cytoscape app eXamine CONCLUSIONS: eXamine accurately conveys small and annotated modules consisting of several dozens of proteins and annotations. We demonstrate that eXamine facilitates the interpretation of integrative network analysis results in a guided case study. This study has resulted in a novel biological insight regarding the virally-encoded G-protein coupled receptor US28.
| S-EPMC4084410 | biostudies-literature
Project description:Networks that represent connections between individuals can be valuable analytic tools. The Social Network Cytoscape app is capable of creating a visual summary of connected individuals automatically. It does this by representing relationships as networks where each node denotes an individual and an edge linking two individuals represents a connection. The app focuses on creating visual summaries of individuals connected by co-authorship links in academia, created from bibliographic databases like PubMed, Scopus and InCites. The resulting co-authorship networks can be visualized and analyzed to better understand collaborative research networks or to communicate the extent of collaboration and publication productivity among a group of researchers, like in a grant application or departmental review report. It can also be useful as a research tool to identify important research topics, researchers and papers in a subject area.
| S-EPMC4760270 | biostudies-literature
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
Project description:Thanks to the unbiased exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. In parallel, network-based approaches have proven to be essential to understand the molecular mechanisms underlying human diseases. The use of these approaches has been boosted by the abundance of information about disease associated genes and variants, high quality human interactomics data, and the emergence of new types of omics data. The DisGeNET Cytoscape App combines the capabilities of Cytoscape with those of DisGeNET, a knowledge platform based on a comprehensive catalogue of disease-associated genes and variants. The DisGeNET Cytoscape App contains functions to query, analyze, and visualize different network representations of the gene-disease and variant-disease associations available in DisGeNET. It supports a wide variety of applications through its query and filter functionalities, including the annotation of foreign networks generated by other apps or uploaded by the user. The new release of the DisGeNET Cytoscape App has been designed to support Cytoscape 3.x and incorporates novel distinctive features such as visualization and analysis of variant-disease networks, disease enrichment analysis for genes and variants, and analytic support through Cytoscape Automation. Moreover, the DisGeNET Cytoscape App features an API to access its core functionalities via the REST protocol fostering the development of reproducible and scalable analysis workflows based on DisGeNET data.
| S-EPMC8163863 | biostudies-literature
Project description:CyLineUp is a Cytoscape 3 app for the projection of high-throughput measurement data from multiple experiments/samples on a network or pathway map using "small multiples". This visualization method allows for easy comparison of different experiments in the context of the network or pathway. The user can import various kinds of measurement data and select any appropriate Cytoscape network or WikiPathways pathway map. CyLineUp creates small multiples by replicating the loaded network as many times as there are experiments/samples (e.g. time points, stress conditions, tissues, etc.). The measurement data for each experiment are then mapped onto the nodes (genes, proteins etc.) of the corresponding network using a color gradient. Each step of creating the visualization can be customized to the user's needs. The results can be exported as a high quality vector image.
| S-EPMC4909101 | biostudies-literature
Project description:CyAnimator (http://apps.cytoscape.org/apps/cyanimator) is a Cytoscape app that provides a tool for simple animations of Cytoscape networks. The tool allows you to take a series of snapshots (CyAnimator calls them frames) of Cytoscape networks. For example, the first frame might be of a network shown from a "zoomed out" viewpoint and the second frame might focus on a specific group of nodes. Once these two frames are captured by the tool, it can animate between them by interpolating the changes in location, zoom, node color, node size, edge thickness, presence or absence of annotations, etc. The animations may be saved as a series of individual frames, animated GIFs, or H.264/MP4 movies. CyAnimator is available from within the Cytoscape App Manager or from the Cytoscape app store.
| S-EPMC4752027 | biostudies-literature
Project description:Network analysis provides deep insight into real complex systems. Revealing the link between topological and functional role of network elements can be crucial to understand the mechanisms underlying the system. Here we propose a Cytoscape plugin (GIANT) to perform network clustering and characterize nodes at the light of a modified Guimerà-Amaral cartography. This approach results into a vivid picture of the a topological/functional relationship at both local and global level. The plugin has been already approved and uploaded on the Cytoscape APP store.
| S-EPMC4183485 | biostudies-literature
Project description:Single-cell RNA-sequencing (scRNA-seq) has revolutionized molecular biology and medicine by enabling high-throughput studies of cellular heterogeneity in diverse tissues. Applying network biology approaches to scRNA-seq data can provide useful insights into genes driving heterogeneous cell-type compositions of tissues. Here, we present scNetViz - a Cytoscape app to aid biological interpretation of cell clusters in scRNA-seq data using network analysis. scNetViz calculates the differential expression of each gene across clusters and then creates a cluster-specific gene functional interaction network between the significantly differentially expressed genes for further analysis, such as pathway enrichment analysis. To automate a complete data analysis workflow, scNetViz integrates parts of the Scanpy software, which is a popular Python package for scRNA-seq data analysis, with Cytoscape apps such as stringApp, cyPlot, and enhancedGraphics. We describe our implementation of methods for accessing data from public single cell atlas projects, differential expression analysis, visualization, and automation. scNetViz enables users to analyze data from public atlases or their own experiments, which we illustrate with two use cases. Analysis can be performed via the Cytoscape GUI or CyREST programming interface using R (RCy3) or Python (py4cytoscape).
| S-EPMC8593621 | biostudies-literature
Project description:Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.
| S-EPMC5089131 | biostudies-literature
Project description:For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network. Here we demonstrate how this 'metadata' can be used to improve our understanding of network structure. We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone. Crucially, the method does not assume that the metadata are correlated with the communities we are trying to find. Instead, the method learns whether a correlation exists and correctly uses or ignores the metadata depending on whether they contain useful information. We demonstrate our method on synthetic networks with known structure and on real-world networks, large and small, drawn from social, biological and technological domains.
| S-EPMC4912639 | biostudies-literature