CYTOSCAPE 2.8 MANUAL PDF

This manual describes the installation and use of Cytoscape. In Cytoscape and later versions, the Passthrough Mapping can. name: Cytoscape Web; version: ; description: network visualization library; url: ; license: opensource; built: The present Manual and the software referenced are licensed under a. Creative Commons . he plugin is compatible with Cytoscape x., is freely available at .

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Cytoscape is open-source software for integration, visualization and analysis of biological networks. It can be extended through Cytoscape plugins, enabling a broad community of scientists to contribute useful features.

This growth has occurred organically through the independent efforts of diverse authors, yielding a powerful but heterogeneous set of tools. We present a travel guide to the world of plugins, covering the publicly available plugins for Cytoscape 2. We also describe ongoing efforts to distribute, organize and maintain the quality of the collection.

High-throughput technologies allow enormous amounts of data to be collected on biological networks, including protein-protein interactions, protein-DNA interactions, kinase-substrate interactions, genetic interactions, gene coexpression and other functional relationships. One of the major computational platforms for analyzing these networks is Cytoscape, a general-purpose and freely available software platform for integration, visualization and statistical modeling of molecular networks together with other systems-level data 12.

To enable rapid prototyping and release of new methods, Cytoscape is implemented as an open-source software package with an accessible application programming interface API using the Java programming language. One of the most powerful consequences of this design is that, through the Cytoscape API, software developers can write extensions called plugins that link Cytoscape with new code and provide access to new or alternative features. Plugins provide a flexible means by which any researcher can bring new concepts in network and systems biology to a broad user base of life scientists.

Cytoscape 2.8 user manual pdf

Although some plugins come installed by default in the standard Cytoscape release, users optionally install most plugins to access the features they require Box 1. In the past several years, the number of publicly available Cytoscape plugins has grown dramatically, from a few dozen in to registered plugins in the beginning of April This growth greatly increases the power and versatility of network analysis. However, it has occurred organically across a heterogeneous community of researchers and software developers, consequently presenting the user with a diverse and sometimes bewildering array of choices.

Although most plugins provide user documentation and many are described in peer-reviewed research papers, a summary evaluation of the entire collection of plugins is needed. That is the purpose of this paper. The Cytoscape website provides a mechanism for submitting plugins http: We used the plugin registry as our primary means of identifying plugins; as of Aprilit contained a total of publicly available plugins for Cytoscape v.

Laboratories contributing plugins are distributed worldwide, with the largest contributions coming from North America and Europe Fig. Statistics for registered Cytoscape plugins. Top, plugin names are shown for the top 20 plugins.

The name and number of downloads for each plugin is in Supplementary Figure 6. We first assessed the rate of use of each plugin by tabulating the number of downloads within the past year as well as the total number of downloads overall Fig.

The former statistic indicates recent popularity and is directly comparable across plugins, whereas the latter statistic indicates all-time popularity but is skewed toward older plugins that have been consistently popular since their initial publication. The associated tags, description and total number of downloads are listed for each tag. The Supplementary Data file provides a complete list of plugins we tagged along with descriptions.

Tags will be updated over time to gradually improve the classification. Next, we validated each plugin by downloading and testing its basic function. The latest version of each plugin was installed on an appropriate version of Cytoscape as determined from the information in our plugin database.

We briefly followed basic manipulations described in tutorials and documents provided by the plugin authors. Both types of errors were communicated by email to the plugin authors, nearly all of whom replied and are currently working with us to resolve the apparent difficulties.

cytoscape/ — Research Computing Center Manual

We expect that by the time this work is published, many of the issues will have been fixed. The 20 plugins registered after April were not tested, but they are listed in the Supplementary Data. The utility of most Cytoscape plugins can be best understood within the larger context of how networks are analyzed Fig.

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Whereas experimental data on interactions are loaded directly into Cytoscape through standard file formats, public databases of interactions are accessed using plugins.

Typically, the database is queried for interactions involving a list of genes of interest or for interactions among genes that have a certain attribute, such as a common molecular function or phenotype.

Alternatively, interactions can be mined directly from the literature or through computational inference from non-interaction data such as expression profiles.

Cytoscape has dozens of plugins for literature mining manjal for network inference. Specific genes or attributes blue typically gathered in preparation for network analysis are imported and used for network generation cytlscape. Many different types of networks are available green for import, after which Cytoscape visualization enables users to efficiently explore and cytoscwpe interpret the network 65 orange. Subsequent network analysis invokes computational algorithms or statistics to interpret and organize interactions red.

Commonly used plugins associated with each level are listed. Following the import of networks and visualization in Cytoscape, a large repertoire of plugins is available for network analysis Fig. For instance, plugins for network topological analysis enable users to calculate statistics such as the distribution of network connectivity that is, node degreesand network clustering plugins allow users to extract densely connected network regions, which often correspond to functional modules such as protein complexes or pathways.

Biological functions of these modules can be inferred with plugins manuzl perform functional enrichment: Functional modules can also be identified by integrating the network with expression data to cyfoscape regions that manua coherently up- or downregulated, or by integrating networks across species to identify regions of the network with conserved interactions.

Finally, plugins for scripting and programmatic chtoscape allow control over the workflow. In what follows, we review Cytoscape plugins at each step of this workflow, with special focus on the plugins that are most widely used, that is, those that have the greatest numbers of total downloads.

Further descriptive use-cases of plugins are available in previous reviews 13. To enable users to find suitable plugins at each step, we have developed a plugin classification system based on a broad set of 41 tags and a companion plugin cytosscape http: As an example, Supplementary Table 1 shows the cytoscwpe ten tags according to the number of plugins annotated to each. This information can also be illustrated by a network Fig. Relationships between Cytoscape plugins and tags.

Orange diamonds, tags; blue rectangles, plugins. The full map of the plugin tags can be found in Supplementary Figure 1. A plugin is counted once for each tag assignment; it is counted multiple times if it is assigned multiple tags.

Note that only plugins which passed our basic validation test are shown.

The generic tabular formats and SIF are especially useful when users wish to import their own experimental interaction data, which often consist of a simple list of gene pairs that have been found to interact. The network-specific formats can represent many additional details about each interaction when known, for example, the type, strength, mathematical details and functional consequence of interaction and, if applicable, the direction of information flow.

Although the ability to recognize interaction data in these formats is provided by manuao Cytoscape core application, in many cases the user does not have new data but instead seeks to access the large online databases of previously cytosczpe interactions. Therefore, to complement the core Cytoscape functionality, several plugins are available to import existing interaction data catalogued in public databases.

For example, the BioGridPlugin can be used to import an entire interactome that is, the full set of interactions mapped for a species to date from BioGrid 7one of several large databases of molecular and genetic interactions. Alternatively, a amnual may wish to import interactions involving a defined subset of genes or proteins; many plugins have been developed for this purpose. The ConsensusPathDB plugin allows users to computationally validate whether there is previous support for a set of interactions in their own data.

Some specialized cytoscae have been designed to import and visualize metabolic networks in particular, which can consist of multiple types of nodes enzymes, small molecules and cofactors or edges reversible or irreversible reactions. This plugin is powerful for superposition of a metabolic network with user-defined data on enzyme expression levels or compound concentrations. Other plugins for importing metabolic networks into Cytoscape include the BioCycPlugin, which provides access to the BioCyc metabolic network database http: Specialized plugins have also been developed to import canonical signaling or regulatory networks curated from literature.

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We also recommend the Pathway Commons 16 website http: The large corpus of published papers cytoscxpe information about interactions that are not yet available in public databases. Thus, extraction of interactions based on 28 literature mining has become an important activity.

The chief means in Cytoscape v. After a user enters search terms, the plugin finds matching records, extracts genes and their associations described within the record and displays them as a network. Although interaction networks based on automatic literature mining usually contain substantial false positives, they allow users to visualize a draft set of protein interactions that may not be present in other databases.

The cytlscape that support each interaction can be manually reviewed to eliminate false positives. Demand for AgilentLiteratureSearch is high: Examples of plugin outputs. A network created by literature mining left and abstracts of scientific papers used to derive the network right are shown. Modules extracted from the network are shown.

A subnetwork containing manula kinase-substrate interactions is analyzed, with cell cycle related genes found to be enriched. For many species, genome-wide interaction manuwl have cytsocape been conducted, and users thus cannot assemble networks for these species.

Even in an organism such as budding yeast, in which large-scale genetic and physical interaction experiments have been performed, complete network coverage has not yet been achieved Cyytoscape, many methods have been developed to predict novel interactions and generate networks from currently available data.

For a defined set cytowcape genes or proteins, it integrates data from many sources, including physical interactions, genetic interactions, pairs of coexpressed genes, pairs of genes in the same pathway or pairs of genes with the same subcellular location, and then visualizes the possible molecular associations among the given genes and other genes Supplementary Fig. ExpressionCorrelation and MONET 21 are plugins that predict functionally interacting pairs of proteins from expression data.

MONET also incorporates biological annotations of genes to predict a regulatory network. Finally, for inference of metabolic network models, the CytoSEED plugin interfaces Cytoscape with the Model SEED 22 resource for automatic generation of metabolic models from prokaryotic genome sequences. Network topology refers to the arrangement or pattern of interactions within a network; several Cytoscape plugins have been developed to calculate topological properties.

The NetworkAnalyzer 23 plugin is installed in Cytoscape by default and calculates network ,anual such as the distribution of node degrees node degree refers to the number of interactions involving a node; it has been shown to correlate with the essential status of genes Users may also try CentiScaPe 2526 for this purpose Fig. Modules in a protein-protein interaction network, for instance, are suggestive of functional protein complexes.

Plugins typically extract such modules by identifying densely connected subgraphs.

MCODE weights nodes by local neighborhood density, then performs an outward traversal from a locally dense seed protein node to isolate larger dense regions, and finally graphically displays extracted modules and associated information Fig. NeMo 30 identifies densely connected and bipartite network modules on the basis of the combination cyyoscape a unique neighbor-sharing score with hierarchical agglomerative clustering.

Different network clustering plugins can yield quite different network modules from the same data. Plugin developers typically argue that more recently developed algorithms work better than older ones, with performance often measured by the ability to recapitulate known protein complexes or .28.

However, performance may also depend on particular characteristics of the input network: MINE was shown to outperform other algorithms including MCODE and NeMo specifically when analyzing the protein-protein interaction network of Caenorhabditis eleganswhich has high interaction density Users should therefore test several different approaches to extract network modules and investigate which predicted modules make more biological sense.