Cytoscape User Manual

Table of Contents

Cytoscape 2.6 User Manual
What’s New in 2.6
Launching Cytoscape
System requirements
Getting Started
Quick Tour of Cytoscape
The Menus
Network Management
The Network Overview Window
Command Line Arguments
Cytoscape Preferences
Managing Properties
Managing Bookmarks
Managing Proxy Servers
Creating Networks
Import Fixed-Format Network Files
Import Free-Format Table Files
Import Networks from Web Services
Edit a New Network
Supported Network File Formats
SIF Format
GML Format
XGMML Format
SBML (Systems Biology Markup Language) Format
BioPAX (Biological PAthways eXchange) Format
PSI-MI Format
Delimited Text Table and Excel Workbook
Node Naming Issues in Cytoscape
Node and Edge Attributes
Cytoscape Attribute File Format
Import Attribute Table Files
Loading Gene Expression (Attribute Matrix) Data
Data File Format
General Procedure
Worked Example
Detailed file format (Advanced users)
Importing Networks and Attributes from External Databases
Web Service Client Manager
Getting Started
Example #1: Retrieving Protein-Protein Interaction Networks from IntAct
Example #2: Retrieving Protein-Protein Interaction Networks from NCBI Entrez Gene
Example #3: Retrieving Pathways and Networks from Pathway Commons
Future Directions
Import Attributes from External Database
Use Multiple Services in a Workflow
Navigation and Layout
Basic Network Navigation
Other Mouse Behaviors
Automatic Layout Algorithms
Manual Layout
Node Movement and Placement
Visual Styles
What is a Visual Style?
Introduction to the VizMapper User Interface
Introduction to Visual Styles
Visual Attributes, Graph Attributes and Visual Mappers
Visual Styles Tutorials
Advanced Topics
Managing Visual Styles
Bypassing Visual Styles
Finding and Filtering Nodes and Edges
Using New Filters
Using Old Filters
The Select Menu
Editing Networks
Add Interactions (SIF Style)
Plugins and the Plugin Manager
The Plugin Manager
Get New Plugins
Delete Existing Plugins
Update Existing Plugins
Download Plugins from a Custom Site
What are CytoPanels?
Basic Usage
Rendering Engine
What is Level of Detail (LOD)?
Ontology and Annotation File Format
Node Name Mapping
Import Ontology and Annotation
Custom Annotation Files for Ontologies Other than GO (for Advanced Users)
Adding or Removing Links
Use LinkOut from Attribute Browser
Appendix A: Old Annotation Server Format
Building your own annotation files
Load Data into Cytoscape
Getting and Reformatting GO Data
Python script examples
Appendix B: GNU Lesser General Public License
Appendix C: Increasing memory for Cytoscape
How to increase memory for Cytoscape
Changing memory allocations on Windows, Mac, and Linux machines


This document is licensed under the Creative Commons license, 2006

Authors: The Cytoscape Collaboration

The Cytoscape project is an ongoing collaboration between:

Cytoscape is a project dedicated to building open-source network visualization and analysis software. A software "Core" provides basic functionality to layout and query the network and to visually integrate the network with state data. The Core is extensible through a plug-in architecture, allowing rapid development of additional computational analyses and features.

Cytoscape's roots are in Systems Biology, where it is used for integrating biomolecular interaction networks with high-throughput expression data and other molecular state information. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape allows the visual integration of the network with expression profiles, phenotypes, and other molecular state information, and links the network to databases of functional annotations.

The central organizing metaphor of Cytoscape is a network (graph), with genes, proteins, and molecules represented as nodes and interactions represented as links, i.e. edges, between nodes.

Cytoscape version 2.6 contains several new features, plus improvements to the performance and usability of the software. These include:

Cytoscape is a Java application verified to run on Linux, Windows, and Mac OS X. Although not officially supported, other UNIX platforms such as Solaris or FreeBSD may run Cytoscape if Java version 5 or later is available for the platform.

The system requirements for Cytoscape depend on the size of the networks the user wants to load, view and manipulate.

There are a number of options for downloading and installing Cytoscape. All options can be downloaded from the website.

  • Automatic installation packages exist for Windows, Mac OS X, and Linux platforms.
  • You can install Cytoscape from a compressed archive distribution.
  • You can build Cytoscape from the source code.
  • You can check out the latest and greatest software from our Subversion repository.

Cytoscape installations (regardless of platform) containing the following files and directories:

Table 3. 




Main Cytoscape application (Java archive)

Script to run Cytoscape from command line (Linux, Mac OS X)


Script to run Cytoscape (Windows)


Cytoscape GNU LGPL License


library jar files needed to run Cytoscape.


Manuals in different formats. What you are reading now.


Licence files for the various libraries distributed with Cytoscape.


Directory containing cytoscape plugins, in .jar format.



galFiltered.gml -- Sample molecular interaction network file *

galFiltered.sif -- Identical network in Simple Interaction Format *

galExpData.pvals -- Sample gene expression matrix file *

galFiltered.nodeAttrTable.xls -- Sample node attribute file in Microsoft Excel format

galFiltered.cys -- Sample session file created from datasets above plus annotations from several databases *

BINDyeast.sif -- Network of all yeast protein-protein interactions in the BIND database as of Dec, 2006 **

BINDhuman.sif -- Network of all human protein-protein interactions in the BIND database as of Dec, 2006 **

yeastHighQuality.sif -- Sample molecular interaction network file ***

interactome_merged.networkTable.gz -- Human interactome network file in tab-delimited format ****

sampleStyles.props -- Additional sample Visual Styles

* From Ideker et al., Science 292:929 (2001)

** Obtained from data hosted at

** From von Mering et al., Nature, 417:399 (2002) and Lee et al, Science 298:799 (2002)

**** Created from Cytoscape tutorial web page. Original data sets are available at: http://www.cytoscape.org from "A merged human interactome" by Andrew Garrow, Yeyejide Adeleye and Guy Warner (Unilever, Safety and Environmental Assurance Center).

Double-click on the icon created by the installer or by running from the command line (Linux or Mac OS X) or double-clicking cytoscape.bat (Windows). Alternatively, you can pass the .jar file to Java directly using the command java -Xmx512M -jar cytoscape.jar -p plugins. The -Xmx512M flag tells java to allocate more memory for Cytoscape and the -p plugins option tells cytoscape to load all of the plugins in the plugins directory. Loading the plugins is important because many key features like layouts, filters and the attribute browser are included with Cytoscape as plugins in the plugins directory. See the Command Line chapter for more detail. In Windows, it is also possible to directly double-click the .jar file to launch it. However, this does not allow specification of command-line arguments (such as the location of the plugin directory).

When you succeed in launching Cytoscape, a window will appear that looks like this (captured on Mac OS 10.4):


For users interested in loading large networks, the amount of memory needed by Cytoscape will increase. Memory usage depends on both number of network objects (nodes+edges) and the number of attributes. Here are some rough suggestions for memory allocation:

Suggested Memory Size Without View

Table 4. 

Number of Objects (nodes + edges)

Suggested Memory Size

0 - 70,000

512M (default)

70,000 - 150,000


Suggested Memory Size With View

Table 5. 

Number of Objects (nodes + edges)

Suggested Memory Size

0 - 20,000

512M (default)

20,000 - 70,000


70,000 - 150,000


When a network is loaded, Cytoscape will look something like the image below:


The main window here has several components:

The network management and attribute browser panels are dockable tabbed panels known as CytoPanels. You can undock any of these panels by clicking on the Float Window control attachment:float_icon.png in the upper-right corner of the CytoPanel.

If you select this control, e.g. on the attribute browser panel, you will now have two Cytoscape windows, the main window, and a new window labeled CytoPanel 2, similar to the one shown below. Popup will be displayed when you put the mouse pointer on a cell.

Note that CytoPanel 2 now has a Dock Window control. If you select this control, the window will dock onto the main window.

Cytoscape also has an editor that enables you to build and modify networks interactively by dragging and dropping nodes and edges from a palette onto the main network view window. The Node shapes and Edge arrows on the palette are defined by the currently used Visual Style. To edit a network, just select the Editor tab on CytoPanel 1. An example of an editor, with the palette contained in CytoPanel 1 and defined by the BioMoleculeEditor Visual Style, is shown below.

Cytoscape 2.3 and newer versions allow multiple networks to be loaded at a time, either with or without a view. A network stores all the nodes and edges that are loaded by the user and a view displays them. You can have many views of the same network. Networks (and their optionally associated views) can be organized hierarchically.

An example where a number of networks have been loaded and arranged hierarchically is shown below:


The network manager (top-right tree view in CytoPanel 1) shows the networks that are loaded. Clicking on a network here will make that view active in the main window, if the view exists (green highlighted networks only). Each network has a name and size (number of nodes and edges), which are shown in the network manager. If a network is loaded from a file, the network name is the name of the file.

Some networks are very large (thousands of nodes and edges) and can take a long time to display. For this reason, a network in Cytoscape may not contain a ‘view’. Networks that have a view are highlighted in green and networks that don’t have a view are highlighted in red. You can create or destroy a view for a network by right-clicking the network name in the network manager or by choosing the appropriate option in the Edit menu. You can also destroy previously loaded networks this way. In the picture above, seven networks are loaded, six green ones with views and one red one without a view.

Certain operations in Cytoscape will create new networks. If a new network is created from an old network, for example by selecting a set of nodes in one network and copying these nodes to a new network (via the File → New → Network option), it will be shown as a child of the network that it was derived from. In this way, the relationships between networks that are loaded in Cytoscape can be seen at a glance. Networks in the top part of the tree in the figure above were generated in this manner.

The available network views are also arranged as multiple overlapping windows in the network view window. You can maximize, minimize, and destroy network views by using the normal window controls for your operating system.

Cytoscape recognizes a number of optional command line arguments, including run-time specification of network files, attribute files, and session files. This is the output generated when the cytoscape is executed with the "-h" or "--help" flag:

usage: java -Xmx512M -jar cytoscape.jar [OPTIONS]
 -h,--help                      Print this message.
 -v,--version                 Print the version number.
 -s,--session <file>        Load a cytoscape session (.cys) file.
 -N,--network <file>     Load a network file (any format).
 -e,--edge-attrs <file>    Load an edge attributes file (edge attribute format).
 -n,--node-attrs <file>   Load a node attributes file (node attribute format).
 -m,--matrix <file>        Load a node attribute matrix file (table).
 -p,--plugin <file>         Load a plugin jar file, directory of jar files,
                                     plugin class name, or plugin jar URL.
 -P,--props <file>         Load cytoscape properties file (Java properties
                                    format) or individual property: -P name=value.
 -V,--vizmap <file>      Load vizmap properties file (Java properties format).

Any file specified for an option may be specified as either a path or as a URL. For example you can specify a network as a file (assuming that myNet.sif exists in the current working directory): -N myNet.sif. Or you can specify a network as a URL: -N

Table 7. 




This flag generates the help output you see above and exits.


This flag prints the version number of Cytoscape and exits.

-s,--session <file>

This option specifies a session file to be loaded. Since only one session file can be loaded at a given time, this option may only specified once on a given command line. The option expects a .cys Cytoscape session file. It is customary, although not necessary, for session file names to contain the .cys extension.

-N,--network <file>

This option is used to load all types of network files. SIF, GML, and XGMML files can all be loaded using the -N option. You can specify as many networks as desired on a single command line.

-e,--edge-attrs <file>

This option specifies an edge attributes file. You may specify as many edge attribute files as desired on a single command line.

-n,--node-attrs <file>

This option specifies a node attributes file. You may specify as many node attribute files as desired on a single command line.

-m,--matrix <file>

This option specifies a data matrix file. In a biological context, the data matrix consists of expression data. All data matrix files are read into node attributes. You may specify as many data matrix files as desired on a single command line.

-p,--plugin <file>

This option specifies a cytoscape plugin (.jar) file to be loaded by Cytoscape. This option also subsumes the previous "resource plugin option". You may specify a class name that identifies your plugin and the plugin will be loaded if the plugin is in Cytoscape's CLASSPATH. For example, assuming that the class MyPlugin can be found in the CLASSPATH, you could specify the plugin like this: -p MyPlugin.class. A final means of specifying plugins is to specify a file name whose contents contain a list of plugin jar files.

-P,--props <file>

This option specifies Cytoscape properties. Properties can be specified either as a properties file (in Java's standard properties format), or as individual properties. To specify individual properties, you must specify the property name followed by the property value where the name and value are separated by the '=' sign. For example to specify the defaultSpeciesName: -P defaultSpeciesName=Human. If you would like to include spaces in your property, simply enclose the name and value in quotation marks: -P "defaultSpeciesName=Homo Sapiens". The property option subsumes previous options -noCanonicalization, -species, and -bioDataServer. Now it would look like: -P defaultSpeciesName=Human -P noCanonicalization=true -P bioDataServer=myServer.

-V,--vizmap <file>

This option specifies a visual properties file.

All options described above (including plugins) can be loaded from the GUI once Cytoscape is running.

Table 8. 

Important! If you have used previous versions of Cytoscape, you will notice that handling of properties has changed. The most important change is that properties are no longer saved by default to the current directory or to your home .cytoscape directory. Properties are stored by default in Cytoscape session files (.cys extension). The cytoscape.props file still exists in the .cytoscape directory but is only written to when the user explicitly requests that the current settings be made the defaults for all future sessions of Cytoscape. Unless you have something important in your .cytoscape/cytoscape.props file, your best bet will be to delete the file and use the defaults.

The Cytoscape Properties editor, accessed via Edit → Preferences → Properties…, is used to specify general and default properties. Properties are now stored in Cytoscape session files, so changes to general properties will be saved as part of the current session, but will only carry over to subsequent sessions if they are set as defaults or exported using the File → Export function.

Cytoscape properties are configurable via Add, Modify and Delete operations.

  • attachment:prefs_editor.png

Some common properties are described below.

There are 4 different ways of creating networks in Cytoscape:

Network files can be specified in any of the formats described in the Supported Network Formats chapter. Networks are imported into Cytoscape through the "Import Network" window, which can be accessed by going to File → Import → Network (multiple file types). The network file can either be located directly on the local computer, or found on a remote computer (in which case it will be referenced with a URL).

The Import Networks dialog is also capable of importing network files using a URL. To do this, set the Data Source Type to Remote and insert the appropriate URL, either manually or using URL bookmarks. Bookmarked URLs can be accessed by clicking on the arrow to the right of the text field (see the Bookmark Manager in Preferences for more details on bookmarks). Also, you can drag and drop links from web browser to the URL text box. Once a URL has been specified, click on the Import button to load the network.


Importing networks from URL addresses has an important caveat. Because Cytoscape determines file type primarily (not exclusively) by file extension, it can have trouble importing networks with URLs that don't end in a human readable file name. If Cytoscape does not recognize a meaningful file name and extension in the URL, it will attempt to guess the type of file based on MIME type. If the MIME type is not recognizable to any of our import handlers, then the import will fail.

Another issue for network import is the presence of firewalls, which can affect which files are accessible to a computer. To work around this problem, Cytoscape supports the use of proxy servers. To configure the proxy server, go to Edit → Preferences→ Proxy Server... . This is further described in the Preferences chapter.

Introduced in version 2.4, Cytoscape now supports the import of networks from delimited text files and Excel workbooks using Edit → Import → Network from Table (Text/MS Excel)... . An interactive GUI allows users to specify parsing options for specified files. The screen provides a preview that shows how the file will be parsed given the current configuration. As the configuration changes, the preview updates automatically. In addition to specifying how the file will be parsed, the user must also choose the columns that represent the Source nodes, the Target nodes, and an optional edge interaction type.


The "Import Network from Table" function supports delimited text files and single-sheet Microsoft Excel Workbooks. The following is a sample table file:

source  target  interaction  boolean attribute  string attribute        floating point attribute
YJR022W YNR053C pp      TRUE    abcd12371       1.2344543
YER116C YDL013W pp      TRUE    abcd12372       1.2344543
YNL307C YAL038W pp      FALSE   abcd12373       1.2344543
YNL216W YCR012W pd      TRUE    abcd12374       1.2344543
YNL216W YGR254W pd      TRUE    abcd12375       1.2344543

The network table files should contain at least two columns: source nodes and target nodes. The interaction type is optional in this format. Therefore, a minimal network table looks like the following:


One row in a network table file represents an edge and its edge attributes. This means that a network file is considered a combination of network data and edge attributes. A table may contain columns that aren't meant to be edge attributes. In this case, you can choose not to import those columns by clicking on the column header in the preview window. This function is useful when importing a data table like the following (1):

Unique ID A     Unique ID B     Alternative ID A        Alternative ID B        Aliases A       Aliases B       Interaction detection methods   First author surnames   Pubmed IDs      species A       species B       Interactor types        Source database Interaction ID  Interaction labels      Cross-references        Associated Files        Experiment files        Experiment labels       Different techniques    Different Pubmed articles       Different sources       Weight

7205    5747    TRIP6   PTK2    Q15654  Q05397-1        vv|HPRD Currently not available 14688263|15892868(Marcotte)     Mammalia        Homo sapiens    protein|protein HPRD|Marcotte   0       Thyroid hormone receptor interactor 6-FAK-|PTK2-TRIP6   NA(HPRD)|NA(Marcotte)   HPRD/02859_psimi.xml|other/ORIGINAL_DATA_MARCOTTE.txt   vv(HPRD/02859_psimi.xml)|HPRD(other/ORIGINAL_DATA_MARCOTTE.txt) 17651(ExptRef)|Marcotte 2       2       2       2

4174    7311    MCM5    UBA52   P33992  P62987  neighbouring_reaction   Currently not available 15608231(Reactome)      Homo sapiens    Homo sapiens    protein|protein Reactome        1       P33992-P62988   Reaction:68944<->Reaction:68946(Reactome)|Reaction:68946<->Reaction:68944(Reactome)     other/ORIGINAL_DATA_MARCOTTE.txt        neighbouring_reaction(other/REACTOMEhomo_sapiens.interactions.txt)      Reactome        1       1       1       1

7040    7040    TGFB1   TGFB1   P01137  P01137  nmr: nuclear magnetic resonance Currently not available 8679613 Homo sapiens    Homo sapiens    protein|protein BIND    2       TGFB1-TGFB1-    72085(BIND)     BIND/bind_taxid9606.1.psi.xml   nmr: nuclear magnetic resonance(BIND/bind_taxid9606.1.psi.xml)  NotAvailable    1       1       1       1

This data file is a tab-delimited text and contains network data (interactions), edge attributes, and node attributes. To import network and edge attributes from this table, you need to choose Unique ID A as source, Unique ID B as target, and Interactor types as interaction type. Then you need to turn off columns used for node attributes (Alternative ID A, species B, etc.). Other columns can be imported as edge attributes.

The network import function cannot import node attributes - only edge attributes. To import node attributes from this table, please see the Attributes section of this manual.

Note (1): This data is taken from the A merged human interactome datasets by Andrew Garrow, Yeyejide Adeleye and Guy Warner (Unilever, Safety and Environmental Assurance Center, 12 October 2006). Actual data files are available at http://www.cytoscape.org

To import network text/Excel tables, please follow these steps:

Cytoscape can read network/pathway files written in the following formats:

The SIF format specifies nodes and interactions only, while other formats store additional information about network layout and allow network data exchange with a variety of other network programs and data sources. Typically, SIF files are used to import interactions when building a network for the first time, since they are easy to create in a text editor or spreadsheet. Once the interactions have been loaded and network layout has been performed, the network may be saved to GML or XGMML format for interaction with other systems. All file types listed (except Excel) are text files and you can edit and view them in a regular text editor.

The simple interaction format is convenient for building a graph from a list of interactions. It also makes it easy to combine different interaction sets into a larger network, or add new interactions to an existing data set. The main disadvantage is that this format does not include any layout information, forcing Cytoscape to re-compute a new layout of the network each time it is loaded.

Lines in the SIF file specify a source node, a relationship type (or edge type), and one or more target nodes:

nodeA <relationship type> nodeB
nodeC <relationship type> nodeA
nodeD <relationship type> nodeE nodeF nodeB
nodeY <relationship type> nodeZ

A more specific example is:

node1 typeA node2
node2 typeB node3 node4 node5

The first line identifies two nodes, called node1 and node2, and a single relationship between node1 and node2 of type typeA. The second line specifies three new nodes, node3, node4, and node5; here "node2" refers to the same node as in the first line. The second line also specifies three relationships, all of type typeB and with node2 as the source, with node3, node4, and node5 as the targets. This second form is simply shorthand for specifying multiple relationships of the same type with the same source node. The third line indicates how to specify a node that has no relationships with other nodes. This form is not needed for nodes that do have relationships, since the specification of the relationship implicitly identifies the nodes as well.

Duplicate entries are ignored. Multiple edges between the same nodes must have different edge types. For example, the following specifies two edges between the same pair of nodes, one of type xx and one of type yy:

node1 xx node2
node1 xx node2
node1 yy node2

Edges connecting a node to itself (self-edges) are also allowed:

node1 xx node1

Every node and edge in Cytoscape has an identifying name, most commonly used with the node and edge data attribute structures. Node names must be unique, as identically named nodes will be treated as identical nodes. The name of each node will be the name in this file by default (unless another string is mapped to display on the node using the visual mapper). This is discussed in the section on visual styles. The name of each edge will be formed from the name of the source and target nodes plus the interaction type: for example, sourceName (edgeType) targetName.

The tag <relationship type> can be any string. Whole words or concatenated words may be used to define types of relationships, e.g. geneFusion, cogInference, pullsDown, activates, degrades, inactivates, inhibits, phosphorylates, upRegulates, etc.

Some common interaction types used in the Systems Biology community are as follows:

  pp .................. protein – protein interaction
  pd .................. protein -> DNA   
  (e.g. transcription factor binding upstream of a regulating gene.)

Some less common interaction types used are:

  pr .................. protein -> reaction
  rc .................. reaction -> compound
  cr .................. compound -> reaction
  gl .................. genetic lethal relationship
  pm .................. protein-metabolite interaction
  mp .................. metabolite-protein interaction

In contrast to SIF, GML is a rich graph format language supported by many other network visualization packages. The GML file format specification is available at:

It is generally not necessary to modify the content of a GML file directly. Once a network is built in SIF format and then laid out, the layout is preserved by saving to and loading from GML. Visual attributes specified in a GML file will result in a new visual style named when that GML file is loaded.

Cytoscape has native support for Microsoft Excel files (.xls) and delimited text files. The tables in these files can have network data and edge attributes. Users can specify columns containg source nodes, target nodes, interaction types, and edge attributes during file import. Some of the other network analysis tools, such as igraph (, has feature to export graph as simple text files. Cytoscape can read these text files and build networks from them. For more detail, please read the Import Free-Format Tables section section of the Creating Networks chapter.


Network generated by igraph's Watts-Strogatz small-world model (50k nodes and 250k esges) visualized by Cytoscape: You can import networks created by other applications using this Table Import feature.

Typically, genes are represented by nodes, and interactions (or other biological relationships) are represented by edges between nodes. For compactness, a gene also represents its corresponding protein. Nodes may also be used to represent compounds and reactions (or anything else) instead of genes.

If a network of genes or proteins is to be integrated with Gene Ontology (GO) annotation or gene expression data, the gene names must exactly match the names specified in the other data files. We strongly encourage naming genes and proteins by their systematic ORF name or standard accession number; common names may be displayed on the screen for ease of interpretation, so long as these are available to the program in the annotation directory or in a node attribute file. Cytoscape ships with all yeast ORF-to-common name mappings in a synonym table within the annotation/ directory. Other organisms will be supported in the future.

Why do we recommend using standard gene names? All of the external data formats recognized by Cytoscape provide data associated with particular names of particular objects. For example, a network of protein-protein interactions would list the names of the proteins, and the attribute and expression data would likewise be indexed by the name of the object.

The problem is in connecting data from different data sources that don't necessarily use the same name for the same object. For example, genes are commonly referred to by different names, including a formal "location on the chromosome" identifier and one or more common names that are used by ordinary researchers when talking about that gene. Additionally, database identifiers from every database where the gene is stored may be used to refer to a gene (e.g. protein accession numbers from Swiss-Prot). If one data source uses the formal name while a different data source used a common name or identifier, then Cytoscape must figure out that these two different names really refer to the same biological entity.

Cytoscape has two strategies for dealing with this naming issue, one simple and one more complex. The simple strategy is to assume that every data source uses the same set of names for every object. If this is the case, then Cytoscape can easily connect all of the different data sources.

To handle data sources with different sets of names, as is usually the case when manually integrating gene information from different sources, Cytoscape needs a data server that provides synonym information (see the chapter on Annotation). A synonym table gives a canonical name for each object in a given organism and one or more recognized synonyms for that object. Note that the synonym table itself defines which set of names are the "canonical" names. For example, in budding yeast, the ORF names are commonly used as the canonical names.

If a synonym server is available, then by default Cytoscape will convert every name that appears in a data file to the associated canonical name. Unrecognized names will not be changed. This conversion of names to a common set allows Cytoscape to connect the genes present in different data sources, even if they have different names – as long as those names are recognized by the synonym server.

For this to work, Cytoscape must also be provided with the species to which the objects belong, since the data server requires the species in order to uniquely identify the object referred to by a particular name. This is usually done in Cytoscape by specifying the species name on the command line with the –P option ( -P "defaultSpeciesName=Saccharomyces cerevisiae") or by editing the properties (under Edit → Preferences → Properties...).

The automatic canonicalization of names can be turned off using the -P option ( -P canonicalizeName=false") or by editing the properties (under Edit → Preferences → Properties...). This canonicalization of names currently does not apply to expression data. Expression data should use the same names as the other data sources or use the canonical names as defined by the synonym table.

Interaction networks are useful as stand-alone models. However, they are most powerful for answering scientific questions when integrated with additional information. Cytoscape allows the user to add arbitrary node, edge and network information to Cytoscape as node/edge/network attributes. This could include, for example, annotation data on a gene or confidence values in a protein-protein interaction. These attributes can then be visualized in a user-defined way by setting up a mapping from data attributes to visual attributes (colors, shapes, and so on). The section on visual styles discusses this in greater detail.

Node and edge attribute files are simply formatted: a node attribute file begins with the name of the attribute on the first line (note that it cannot contain spaces). Each following line contains the name of the node, followed by an equals sign and the value of that attribute. Numbers and text strings are the most common attribute types. All values for a given attribute must have the same type. For example:

YAL001C = metabolism
YAR002W = apoptosis
YBL007C = ribosome

An edge attribute file has much the same structure, except that the name of the edge is the source node name, followed by the interaction type in parentheses, followed by the target node name. Directionality counts, so switching the source and target will refer to a different (or perhaps non-existent) edge. The following is an example edge attributes file:

YAL001C (pp) YBR043W = 0.82
YMR022W (pd) YDL112C = 0.441
YDL112C (pd) YMR022W = 0.9013

Since Cytoscape treats edge attributes as directional, the second and third edge attribute values refer to two different edges (source and target are reversed, though the nodes involved are the same).

Each attribute is stored in a separate file. Node and edge attribute files use the same format. Node attribute file names often use the suffix ".noa", while edge attribute file names use the suffix ".eda". Cytoscape recognizes these suffixes when browsing for attribute files.

Node and edge attributes may be loaded at the command line using the –n and –e options or via the File → Import menu.

When expression data is loaded using an expression matrix, it is automatically loaded as node attribute data unless explicitly specified otherwise.

Node and edge attributes are attached to nodes and edges, and so are independent of networks. Attributes for a given node or edge will be applied to all copies of that node or edge in all loaded network files, regardless of whether the attribute file or network file is imported first.

Note: In order to import network attributes in Cytoscape 2.4, please go to File → Import → Attribute from Table (text/MS Excel)... or encode them in an XGMML network file (see Supported File Formats for more details).

Every attribute file has one header line that gives the name of the attribute, and optionally some additional meta-information about that attribute. The format is as follows:

attributeName (class=formal.class.of.value)

The first field is always the attribute name: it cannot contain spaces. If present, the class field defines the formal (package qualified) name of the class of the attribute values. For example, java.lang.String for Strings, java.lang.Double for floating point values, java.lang.Integer for integer values, etc. If the value is actually a list of values, the class should be the type of the objects in the list. If no class is specified in the header line, Cytoscape will attempt to guess the type from the first value. If the first value contains numbers in a floating point format, Cytoscape will assume java.lang.Double; if the first value contains only numbers with no decimal point, Cytoscape will assume java.lang.Integer; otherwise Cytoscape will assume java.lang.String. Note that the first value can lead Cytoscape astray: for example,

firstName = 1
secondName = 2.5

In this case, the first value will make Cytoscape think the values should be integers, when in fact they should be floating point numbers. It's safest to explicitly specify the value type to prevent confusion. A better format would be:

floatingPointAttribute (class=Double)
firstName = 1
secondName = 2.5


firstName = 1.0
secondName = 2.5

Every line past the first line identifies the name of an object (a node in a node attribute file or an edge in a edge attribute file) along with the String representation of the attribute value. The delimiter is always an equals sign; whitespace (spaces and/or tabs) before and after the equals sign is ignored. This means that your names and values can contain whitespace, but object names cannot contain an equals sign and no guarantees are made concerning leading or trailing whitespace. Object names must be the Node ID or Edge ID as seen in the left-most column of the attribute browser if the attribute is to map to anything. These names must be reproduced exactly, including case, or they will not match.

Edge names are all of the form:

sourceName (edgeType) targetName

Specifically, that is

As of Cytoscape 2.4, importing delimited text and MS Excel attribute data tables is now supported. Using this functionality, users can now easily import data that isn't formatted into Cytoscape node or edge attribute file formats (as described above).


Sample Attribute Table 1

Table 12. 

Object Key












The attribute table file should contain a primary key column and at least one attribute column. The maximum number of attribute columns is unlimited. The Alias column is an optional feature, as is using the first row of data as attribute names. Alternatively, you can specify each attribute name from the File → Import → Attribute from Table (text/MS Excel)... user interface.


When Cytoscape is started, the Attribute Browser appears in the bottom CytoPanel. This browser can be hidden and restored using the F5 key or the View → Show/Hide attribute browser menu option. Like other CytoPanels, the browser can be undocked by pressing the little icon in the browser’s top right corner.

To swap between displaying node, edge, and network attributes use the tabs on the bottom of the panel labelled "Node Attribute Browser", "Edge Attribute Browser", and "Network Attribute Browser". The attribute browser displays attributes belonging to selected nodes and/or edges and the currently selected network. To populate the browser with rows (as pictured above), simply select nodes and/or edges in a loaded network. By default, only the ID of nodes and edges is shown. To display more than just the ID, click the Select Attributes attachment:attributes_select_icon.png button and choose the attributes that are to be displayed (select various attributes by clicking on them, and then click elsewhere on the screen to close the attribute list). Each attribute chosen will result in one column in the attribute browser. Most attribute values can be edited by double-clicking an attribute cell; list values cannot be edited, and neither can the ID. Attribute rows in the browser can be sorted alphabetically by specific attribute by clicking on a column heading. A new attribute can be created using the Create New Attribute attachment:attributes_new_icon.png button, and must be one of four types – integer, string, real number (floating point), or boolean. Attributes can be deleted using the Delete Attributes attachment:attributes_delete_icon.png button. NOTE: Deleting attributes removes them from Cytoscape, not just the attribute browser! To remove attributes from the browser without deleting them, simply unselect the attribute using the Select Attributes attachment:attributes_select_icon.png button.

The right-click menu on the Attribute Browser has several functions, such as exporting attribute information to spreadsheet applications. For example, use the right-click menu to Select All and then Copy the data, and then paste it into a spreadsheet application. Each attribute browser panel also has a button for importing new attributes: attachment:attributes_import_icon.png .

The Node Attribute Browser panel has additional buttons for loading Gene Expression attribute matrices ( attachment:attributes_gene_expr_icon.png ) as node attributes.

In addition to normal node and edge attribute data, Cytoscape also supports importing gene expression data. Gene expression data are imported using a different file format than normal attributes; however, the resulting attributes are not treated differently by Cytoscape. Gene expression data (like attribute data) can be loaded at any time, but are (generally) only relevant once a network has been loaded.

Gene expression ratios or values are specified over one or more experiments using a text file. Ratios result from a comparison of two expression measurements (experiment vs. control). Some expression platforms, such as Affymetrix, directly measure expression values, without a comparison. The file consists of a header and a number of space- or tab-delimited fields, one line per gene, with the following format:

Identifier [CommonName] value1 value2 ... valueN [pval1 pval2 ... pvalN]

Brackets [ ] indicate fields that are optional.

The first field identifies which Cytoscape node the data refers to. In the simplest case, this is the gene name - exactly as it appears on the network generated by Cytoscape (case sensitive!). Alternatively, this can be some node attribute that identifies the node uniquely, such as a probeset identifier for commercial microarrays.

The next field is an optional common name. It is not used by Cytoscape, and is provided strictly for the user's convenience. With this common name field, the input format is the same as for commonly-used expression data anaysis packages such as SAM (

The next set of columns represent expression values, one per experiment. These can be either absolute expression values or fold change ratios. Each experiment is identified by its experiment name, given in the first line.

Optionally, significance measures such as P values may be provided. These values, generated by many microarray data analysis packages, indicate where the level of gene expression or the fold change appears to be greater than random chance. If you are using significance measures, then your expression file should contain them in a second set of columns after the expression values. The column names for the expression significance measures need to match those of the expression values exactly.

For example, here is an excerpt from the file galExpData.pvals in the Cytoscape sampleData directory:

GENE COMMON gal1RG gal4RG gal80R gal1RG gal4RG gal80R
YHR051W COX6 -0.034 0.111 -0.304 3.75720e-01 1.56240e-02 7.91340e-06
YHR124W NDT80 -0.090 0.007 -0.348 2.71460e-01 9.64330e-01 3.44760e-01
YKL181W PRS1 -0.167 -0.233 0.112 6.27120e-03 7.89400e-04 1.44060e-01
YGR072W UPF3 0.245 -0.471 0.787 4.10450e-04 7.51780e-04 1.37130e-05

This indicates that there is data for three experiments: gal1RG, gal4RG, and gal80R. These names appear two times in the header line: the first time gives the expression values, and the second gives the significance measures. For instance, the second line tells us that in Experiment gal1RG, the gene YHR051W has an expression value of -0.034 with significance measure 3.75720e-01.

Some variations on this basic format are recognized; see the formal file format specification below for more information. Expression data files commonly have the file extensions ".mrna" or ".pvals", and these file extensions are recognized by Cytoscape when browsing for data files.

For the sample network file sampleData/galFiltered.sif:

Option A.

Load a sample gene expression data set by going to File → Import → Attribute/Expression Matrix... . In the resulting window, in the field labeled "Please select an attribute or expression matrix file...", use the Select button to enter sampleData/galExpData.pvals. The identifiers used in this file are the same ones used in the network file sampleData/galFiltered.sif, so you do not need to touch the field labeled "Assign values to nodes using...". A few lines of this file are shown below:

GENE COMMON gal1RG gal4RG gal80R gal1RG gal4RG gal80R
YHR051W COX6 -0.034 0.111 -0.304 3.75720e-01 1.56240e-02 7.91340e-06
YHR124W NDT80 -0.090 0.007 -0.348 2.71460e-01 9.64330e-01 3.44760e-01
YKL181W PRS1 -0.167 -0.233 0.112 6.27120e-03 7.89400e-04 1.44060e-01

Option B.

Step 1. After loading the network, load the node attribute file sampleData/, using File → Import → Node attributes... . This file is shown in part below:

YHR051W = probeset2
YHR124W = probeset3
YKL181W = probeset4

Step 2. After loading the node attribute file, select the expression data file sampleData.galExpPvals.probeset.pvals, shown in part below:

GENE COMMON gal1RG gal4RG gal80R gal1RG gal4RG gal80R
probeset2 COX6 -0.034 0.111 -0.304 3.75720e-01 1.56240e-02 7.91340e-06
probeset3 NDT80 -0.090 0.007 -0.348 2.71460e-01 9.64330e-01 3.44760e-01
probeset4 PRS1 -0.167 -0.233 0.112 6.27120e-03 7.89400e-04 1.44060e-01

After selecting this file, in the field labeled "Assign values to nodes using...", select Probeset. You will see that this loads exactly the same expression data as in Case 1, but provides extra flexibility in case the node name cannot be used as an identifier.

In all expression data files, any whitespace (spaces and/or tabs) is considered a delimiter between adjacent fields. Every line of text is either the header line or contains all the measurements for a particular gene. No name conversion is applied to expression data files.

The names given in the first column of the expression data file should match exactly the names used elsewhere (i.e. in SIF or GML files).

The first line is a header line with one of the following three header formats:

<text> <text> cond1 cond2 ... cond1 cond2 ... [NumSigConds]

<text> <text> cond1 cond2 ...


The first format specifies that both expression ratios and significance values are included in the file. The first two text tokens (in angled brackets) contain names for each gene, such as the formal and common gene names. The condX token set specifies the names of the experimental conditions; these columns will contain ratio values. This list of condition names must then be duplicated exactly, each spelled the same way and in the same order. Optionally, a final column with the title NumSigConds may be present. If present, this column will contain integer values indicating the number of conditions in which each gene had a statistically significant change according to some threshold.

The second format is similar to the first except that the duplicate column names are omitted, and there is no NumSigConds field. This format specifies data with ratios but no significance values.

The third format specifies an MTX header, which is a commonly used format. Two tab characters precede the RATIOS token. This token is followed by a number of tabs equal to the number of conditions, followed by the LAMBDAS token. This format specifies both ratios and significance values.

Each line after the first is a data line with the following format:

FormalGeneName CommonGeneName ratio1 ratio2 ... [lambda1 lambda2 ...] [numSigConds]

The first two tokens are gene names. The names in the first column are the keys used for node name lookup; these names should be the same as the names used elsewhere in Cytoscape (i.e. in the SIF, GML, or XGMML files). Traditionally in the gene expression microarray community, who defined these file formats, the first token is expected to be the formal name of the gene (in systems where there is a formal naming scheme for genes), while the second is expected to be a synonym for the gene commonly used by biologists, although Cytoscape does not make use of the common name column. The next columns contain floating point values for the ratios, followed by columns with the significance values if specified by the header line. The final column, if specified by the header line, should contain an integer giving the number of significant conditions for that gene. Missing values are not allowed and will confuse the parser. For example, using two consecutive tabs to indicate a missing value will not work; the parser will regard both tabs as a single delimiter and be unable to parse the line correctly.

Optionally, the last line of the file may be a special footer line with the following format:

NumSigGenes int1 int2 ...

This line specified the number of genes that were significantly differentially expressed in each condition. The first text token must be spelled exactly as shown; the rest of the line should contain one integer value for each experimental condition.

Cytoscape 2.6.0 has a new feature called Web Service Client Manager. This is a framework to manage various kinds of web service clients in Cytoscape. By using web service clients, users can access remote datasources easily.

A web service is a standardized, platform-independent mechanism for machines to interact over the network. These days, many major biological databases publishes their system with web service API:

This enables developers to write a program to access these services. Cytoscape core developer team have developed several sample web service clients using this framework. Currently, Cytoscape supports the following web services:

  • IntAct: an open source database of protein interaction data, hosted at EMBL-EBI.

  • Pathway Commons: an open source portal, providing access to multiple integrated data sets, including: Reactome, IntAct, HPRD, HumanCyc, MINT, the MSKCC Cancer Cell Map, and the NCI/Nature Pathway Interaction database.

  • NCBI Entrez Gene: a public database of genes, including annotation, sequence and interactions.

  • Biomart: an open source biological database engine. Useful for ID/Name mapping.

All of these clients are available as Plugins and users can install them through Plugin Manager.

In the following sections, users learn how to import network from extrenal databases.

To get started, select: File → Import → Network from web services...


Some of the web service clients can import attributes from external databases. BioMart client is an example. You can install it from Plugin Manager.



Web services are useful when you combine the result from multiple data sources.


  • Import human orthologs from BioMart.


  • Show the othologs as the list of Ensemble Gene ID on the Data Panel. Copy them and use them as the query for IntAct.

  • Import Entrez Gene ID from BioMart. Use ensembl attribute for the mapping key.

  • Import annotations from NCBI. The resulting networks looks like the following:


The Layout menu has an array of features for organizing the network visually according to one of several algorithms, aligning and rotating groups of nodes, and adjusting the size of the network. Most of these features are available from plugins that are packaged with Cytoscape 2.3 and above. Cytoscape layouts have three different sources, which are reflected in the Layout menu.

Cytoscape Layouts are those layouts that have been written or integrated by Cytoscape developers. These layouts are fully integrated with Cytoscape. All Cytoscape Layouts have the option to operate on only the selected nodes, and all provide a Settings... panel to change the parameters of the algorithm. Most of the Cytoscape layouts also partition the graph before performing the layout. In addition, many of these layouts include the option to take either node or edge attributes into account. Some of these layouts are:

The force-directed layout is a new layout based on the "force-directed" paradigm. This layout is based on the algorithm implemented as part of the excellent prefuse toolkit provided by Jeff Heer. The algorithm is very fast and with the right parameters can provide a very pleasing layout. The Force Directed Layout will also accept a numeric edge attribute to use as a weight for the length of the spring, although this will often require more use of the Settings... dialog to achieve the best layout. This algorithm is available by selecting Layout → Cytoscape Layouts → Force-Directed Layout → (unweighted) or the edge attribute you want to use as a weight. A sample screen shot showing a portion of the galFiltered network provided in sample data is provided below:

  • attachment:force_layout.png

The simplest method to manually organize a network is to click on a node and drag it. If you select multiple nodes, all of the selected nodes will be moved together.

Selecting the Layout → Align/Distribute option will open the Align/Distribute/Stack window in CytoPanel 5. The Align buttons provide different options for either vertically or horizontally aligning selected nodes against a line. The differences are in what part of the node gets aligned, e.g. the center of the node, the top of the node, the left side of the node. The Distribute buttons evenly distribute selected nodes between the two most distant nodes along either the vertical or horizontal axis. The differences are again a function what part of the node is used as a reference point for the distribution. And the Stack buttons vertically or horizontally stack selected nodes with the full complement of alignment options. The table below provides a decription of what each button does.

Table 14. 




Description of Align Options




Vertical Align Top - The tops of the selected nodes are aligned with the top-most node.




Vertical Align Center - The centers of the selected nodes are aligned along a line defined by the midpoint between the top and bottom-most nodes.




Vertical Align Bottom - The bottoms of the selected nodes are aligned with the bottom-most node.




Horizontal Align Left - The left hand sides of the selected nodes are aligned with the left-most node.




Horizontal Align Center - The centers of the selected nodes are aligned along a line defined by the midpoint between the left and right-most nodes.




Horizontal Align Right - The right hand sides of the selected nodes are aligned with the right-most node.

Table 15. 




Description of Distribute Options




Vertical Distribute Top - The tops of the selected nodes are distributed evenly between the top-most and bottom-most nodes, which should stay stationary.




Vertical Distribute Center - The centers of the selected nodes are distributed evenly between the top-most and bottom-most nodes, which should stay stationary.




Vertical Distribute Bottom - The bottoms of the selected nodes are distributed evenly between the top-most and bottom-most nodes, which should stay stationary.




Horizontal Distribute Left - The left hand sides of the selected nodes are distributed evenly between the left-most and right-most nodes, which should stay stationary.




Horizontal Distribute Center - The centers of the selected nodes are distributed evenly between the left-most and right-most nodes, which should stay stationary.




Horizontal Distribute Right - The right hand sides of the selected nodes are distributed evenly between the left-most and right-most nodes, which should stay stationary.

One of Cytoscape's strengths in network visualization is the ability to allow users to encode any attribute of their data (name, type, degree, weight, expression data, etc.) as a visual property (such as color, size, transparency, or font type). A set of these encoded or mapped attributes is called a Visual Style and can be created or edited using the Cytoscape VizMapper. With the VizMapper, the visual appearance of your network is easily customized. For example, you can:

Cytoscape 2.6.0 and later has additional sample Visual Styles. You can try those samples to understand how Visual Styles change appearence of a network. The following is a list of network views based on sample styles applied to galFiltered.sif network :

attachment:default_style.png attachment:metro_style.png attachment:solid_style.png attachment:ripple_style.png attachment:skeleton_style.png attachment:universe_style.png

The VizMapper can be accessed by going to View → Open VizMapper or by clicking on the VizMapper icon attachment:VizMapIcon.png. Also, starting in Cytoscape 2.5, direct access to the VizMapper is provided via a tab on the Control Panel at the left-hand side of the screen (formerly known as CytoPanel 1).

The Cytoscape distribution includes several predefined visual styles to get you started. To demonstrate these styles, try out the following example:

Step 1. Load some sample data

Step 2. Switch between different Visual Styles

You can change visual styles by making a selection from the Current Visual Style dropdown list (found at the top of the VizMapper Main Panel).

For example, if you select Sample1, a new visual style will be applied to your network, and you will see a white background and round blue nodes. Additionally, if you zoom in closer, you can see that protein-DNA interactions (specified with the label "pd") are drawn with dashed red edges, whereas protein-protein interactions (specified with the label "pp") are drawn with a solid light blue edge (see sample screenshot below).

Finally, if you select Solid, you can see the graphics below:

This Visual Style does not have mappings except node/edge labels, but you can modify the network graphics by editing Default View.

Additional sample styles are available as sampleStyles.props file in the SampleData directory. You can import the sample file from File → Import → Vizmap Property File.

The Cytoscape VizMapper uses three core concepts:

Cytoscape allows a wide variety of visual attributes to be controlled. These are summarized in the tables below.

Table 18. 

Visual Attributes Associated with Nodes

Node Color

Node Opacity

Node Border Color

Node Border Opacity

Node Border Line Style. Solid and dashed lines are supported.

Node Border Line Width

Node Shape. The following options are available:


Node Size: the width and height of each node.

Node Label: the text label for each node.

Node Label Color

Node Label Opacity

Node Label Position: the position of the label relative to the node.

Node Font: node label font and size.

Table 19. 

Visual Attributes Associated with Edges

Edge Color

Edge Opacity

Edge Line Style. Solid or dashed lines are supported.

Edge Line Width

Edge Source and Target Arrow Shape: The following options are available:


Edge Source and Target Arrow Color

Edge Source and Target Arrow Opacity

Edge Label: the text label for each edge.

Edge Label Color

Edge Label Opacity

Edge Font: edge label font and size.

Table 20. 

Global Visual Properties

Background Color

Selected Node Color

Selected Edge Color

For each visual attribute, you can specify a default value or define a dynamic visual mapping. Cytoscape currently supports three different types of visual mappers:

  1. Passthrough Mapper

    • The values of network attributes are passed directly through to visual attributes. A passthrough mapper is only used to specify node/edge labels. For example, a passthrough mapper can label all nodes with their common gene names.
  2. Discrete Mapper

    • Discrete network attributes are mapped to discrete visual attributes. For example, a discrete mapper can map all protein-protein interactions to the color blue.
  3. Continuous Mapper

    • Continuous graph attributes are mapped to visual attributes. Depending on the visual attribute, there are three kinds of continuous mappers:
      1. Continuous-to-Continuous Mapper: for example, you can map a continuous numerical value to a node size.

      2. Color Gradient Mapper: This is a special case of continuous-to-continuous mapping. Continuous numerical values are mapped to a color gradient.

      3. Continuous-to-Discrete Mapper: for example, all values below 0 are mapped to square nodes, and all values above 0 are mapped to circular nodes.

    • However, note that there is no way to smoothly morph between circular nodes and square nodes.

The table below shows visual mapper support for each visual property.


Table 21. 




Mapping is not supported for the specified visual property.


Mapping is fully supported for the specified visual property.


Mapping is partially supported for the specified visual property. Support for “continuous to continuous” mapping is not supported.

Node Visual Mappings

Table 22. 

Node Visual Properties

Passthrough Mapper

Discrete Mapper

Continuous Mapper



Node Color




Node Opacity





Node Border Color





Node Border Opacity





Node Label Color





Node Label Opacity






Node Size




Node Font Size





Node Line Width






Node Border Type




Node Shape





Node Label





Node Tooltip





Node Font Family





Edge Visual Mappings

The following tutorials demonstrate some of the basic VizMapper features. Each tutorial is independent of the others.

The goal of this tutorial is to learn how to create a new Visual Style and set some default values.

Step 1. Load a sample network. From the main menu, select File → Import → Network (Multiple file types), and select sampleData/galFiltered.sif.

Step 2. Open the VizMapper. Select the View → Open VizMapper menu option, or select the VizMapper icon in the main button bar, or click on the VizMapper tab in the Control Panel at the left of the screen. You will now see a VizMapper Main Panel, as shown below.

Step 3. Create a new visual style. Click the Options attachment:VizMapOptionIcon.png button, and select Create new visual style... Then enter a name for your new visual style when prompted. You will see an empty visual style in the VizMapper Main Panel, as shown below.

Since no mapping is set up yet, all visual attributes are listed in the Unused Properties category. From this panel, you can create node/edge mappings for all visual properties.

Step 4. Edit default values. Open the Default Appearance Editor by clicking on the Defaults graphics window (shown below) in the VizMapper Main Panel.

Step 5. Change the default node shape. To set the default node shape to triangles, click "Node Shape" in the Default Visual Properties list. A list of available node shapes will be shown. Click on the Triangle icon and then click the Apply button. The Default Appearance Editor will be automatically updated. You can edit other default values by clicking on visual attribute names on the list. In the example shown below, the node shape is set to Triangle, while the node color is set to blue.

Step 6. Apply your settings. When you finish editing, click the Apply button at the bottom of the editor. Your new Visual Style will be applied to the current network, as shown below.

The following tutorial demonstrates how to create a new visual style using a discrete mapper. The goal is to draw protein-DNA interactions as dashed blue lines, and protein-protein interactions as solid red lines.

Step 1. Load a sample network. From the main menu, select File → Import → Network (Multiple file types), and select sampleData/galFiltered.sif.

Step 2. Open the VizMapper. Select the View → Open VizMapper menu option, or select the VizMapper icon in the main button bar, or click on the VizMapper tab in the Control Panel at the left of the screen.

Step 3. Create a new visual style. Click the Options attachment:VizMapOptionIcon.png button, and select Create new visual style... Name your new style “Tutorial VS2”.

Step 4. Choose a visual attribute. Double click the Edge Color entry listed in Unused Properties. Edge Color will now appear at the top of the list, under the Edge Visual Mapping category (as shown below).

Step 5. Choose a network attribute. Click on the cell to the right of the Edge Color entry and select "interaction" from the dropdown list that appears.

Step 6. Choose a mapping type. Set the Discrete Mapper option as the Mapping Type. All available attribute values for "interaction" will be displayed, as shown below.

Step 7. Set the mapping relationship. Click the empty cell next to "pd" (protein-DNA interactions). On the right side of the cell, ... and X buttons will appear. Click on the ... button. A popup window will appear; select blue, and the change will immediately appear on the network window.

Repeat step 7 for "pp" (protein-protein interactions), but select red as the edge color. Then repeat steps 4 through 7 for the Edge Line Style attribute. You can select the correct line style (dashed or solid) from the dropdown list.

Now your network should show "pd" interactions as dashed blue lines and "pp" interactions as solid red lines. A sample screenshot is provided below.


The following tutorial demonstrates how to create a new visual style using a continuous mapper. The goal is to superimpose gene expression data onto a network and display gene expression values along a color gradient.

Step 1. Load a sample network. From the main menu, select File → Import → Network (Multiple file types), and select sampleData/galFiltered.sif.

Step 2. Load sample expression data. From the main menu, select File → Import → Attribute/Expression Matrix, and select sampleData/galExpData/pvals.

Step 3. Open the VizMapper. Select the View → Open VizMapper menu option, or select the VizMapper icon in the main button bar, or click on the VizMapper tab in the Control Panel at the left of the screen.

Step 4. Create a new visual style. Click the Options attachment:VizMapOptionIcon.png button, and select Create new visual style... Name your new style “Tutorial VS3”.

Step 5. Choose a visual attribute. Double click the Node Color entry listed in Unused Properties. Node Color will now appear at the top of the list, under the Node Visual Mapping category.

Step 6. Choose a network attribute. Click on the cell to the right of the Node Color entry and select "gal1RGexp" from the dropdown list that appears.

Step 7. Choose a mapping type. Set the Continuous Mapping option as the Mapping Type. This automatically creates a default mapping

Step 8. Define the points where colors will change. Double-click on the black-and-white gradient rectangle next to Graphical View to open the Color Gradient Mapper. Click and drag one point to -1, or type the value in the Range Setting box. Set the second point to 2.

Step 9. Define the colors between points. Double-click on the leftmost triangle (facing left) and a color palette will appear. Choose a shade of yellow and click OK. Double-click on the triangle at -1 and set the color white. For triangle at 2, set its color to red. Set the rightmost triangle to black.

The color gradients will immediately appear in the network window. All nodes with a gal1RGexp value less than –1 will be set to yellow, and all nodes with a gal1RGExp value greater than 2 will be black. Additionally, all values between –1 and 2 will be painted with a white/red color gradient. A sample screenshot is below.


The following tutorial demonstrates new features in Cytoscape 2.5. The new VizMapper user interface has some utilities to help users editing discrete mappings. The goal of this section is learning how to set and adjust values for discrete mappings automatically.

  1. Load a sample network: From the main menu, select File → Import → Network, and select sampleData/galFiltered.sif.

  2. Apply layout to the network: From the main menu, select Layout → Cytoscape Layouts → Degree Sorted Circle Layout. This layout algorithm sort nodes in a circle by degree of the nodes. Degrees will be stored as node attribute names Degree after you applied this algorithm.

  3. Click the VizMap attachment:VizMapIcon.png button on the tool bar.

  4. Click Defaults panel on the VizMapper main panel. Default Apearence Editor pops up (see below.)

  5. Edit the following visual properties and press Apply. Since you changed opacity of the node, you can see the nodes bihind the front node (see below.)

    • Node Oppacity - 100
    • Edge Color - White
    • Background Color - Black


  6. Cretate a Discrete Node Color Mapping. Select Degree as controlling attribute.

  7. Select Node Color, then right click to show popup menu. Select Generate discrete values → Rainbow 1. It generates different colors for different attribute values as shown below.


  8. Cretate a Discrete Node Size Mapping. Select Degree as controlling attribute.

  9. Select Node Size and right click to show popup menu. Select Generate Discrete Values → Series (Numbers Only). Type 30 for the first value and click OK. Enter 15 for increment.

  10. Apply Force-Directed layout. Final view of the window looks like the following.


From version 2.5, several utility functions are available for Discrete Mappings. You can use those functions by right clicking anywhere on the Visual Mapping Browser (shown below.)


There are three kinds of Continuous Mapping Editors. Each of them are associated with a specific visual attributes:

Table 24. 

Editor Type

Supported Data Type

Visual Attributes

Color Gradient Editor


node/edge/border/label colors

Continuous-Continuous Editor



Continuous-Discrete Editor

All others


All Cytoscape Visual Style settings are initially loaded from a a default file called vizmap.props that cannot be altered by users. When users make changes to the visual properties, a vizmap.props file is saved in the session file. This means that assuming you save your session, you will not lose your visual properties. No other vizmap.props files are saved during normal operation.

Cytoscape has a feature that allows users to override visualizations created by the VizMapper for individual nodes and edges. This feature is available by right-clicking on a node or edge and then clicking on the Visual Mapping Bypass menu.

Each visual property of the node or edge is displayed. When a property is overridden, a checkmark appears next to the property and a [Reset <Property Name>] menu option appears directly below it. By clicking this Reset option, the bypass will be removed and the attribute will be displayed as defined by the VizMapper. At the bottom of the menu a Reset All option appears. When clicked, this will remove all bypasses for the specified node or edge. In the example above, you can see the the selected node size, color, and shape have been overridden. This is apparent in the appearance of the node itself and by the check marks in the popup menu.

It is important to realize that the the Visual Mapping Bypass only works for individual nodes and edges and not for all nodes or edges of a specific type. Using the bypass function is not particularly resource intensive, meaning you can use it as much as you like. However, if you ever find yourself repeating the same bypasses, then you should consider using the VizMapper instead.

Bypass is accomplished using special attributes with names like node.fillColor and node.shape. These are normal Cytoscape attributes and can be seen and edited in the Attribute Browser. The value of the attribute is a string representation of a property. For example, color is represented by 3 integers representing the RGB (red, green, blue) value of the color. Different types of properties have different string representations. When in doubt, just use the right click menu to create valid attribute values.

Because bypass values are specified using normal attributes, these attributes will persist between sessions only as long as you save your session! If you don't save your session, you will lose whatever bypass values you set.

Cytoscape includes a Quick Find feature, which enables you to quickly find nodes and edges.


Using Quick Find is very simple. Here is how it works:



By default, you should see a filter icon on the toolbar: attachment:filter_icon.png . If you click on it, the filters tab on the Control Panel will be selected. You can also access the filters by clicking directly on the tab or by using the Select menu pull-down and choose “Use Filters” menu item. The filters panel initially looks like this:


1. Filter Definition

To create a new filter, click the option button and select “Create new filter” from the list provided. Enter a name for the new filter.



Definition of simple filter

When a new filter is created, it is empty initially. It can be defined by choosing attributes (one at a time) in the Attribute/Filter comboBox and clicking the Add button. Note that in the comboBox each attribute has a prefix, either node. or edge., denoting what type of attribute it is. After add button is clicked, a widget, depended on the item selected, will be added to the definition panel. If the attribute type is text-based (or String), the widget will be a indexed-text-box widget, if numeric attribute, the widget will be a range slider; both are shown below.


For each widget, the name of the attribute it represents is on the left. A NOT checkbox gives the option to get the negation of selection for the widget. There is a trash-can icon on the right. Clicking on the trash-can icon will delete the widget. In this way, the filter definition can be modified after it is defined.

Note that if more than one widget is added on the filter definition panel, the relationship between them is “AND” by default. This relationship can be changed to “OR” by selecting the OR relation in Advanced Panel.

The Advanced panel can be opened by clicking on the plus (+) sign.


There are three rows in the advanced panel:

1. The first row, labeled Save. The two checkboxes (Global and Session) will determine where the filter is saved. By default, filters are saved in individual sessions. If the Global checkbox is checked, the filter will be saved in the global properties file. Note also that the prefix of the filter name in the Current Filter dropdown list reflects where it is saved.

2. The second row, Relation, will determine what Boolean operation (AND or OR) will be applied to each individual widget.

3. Negation checkbox. If this checkbox is checked, the result of the filter will be negated.

Definition of complex filter

In the pull-down list of Attribute/Filter comboBox, there are two sections, the top one is the list of attributes, the bottom one is the list of previously defined filter. Those previously dfined filters can also be used as components of other filters. By combinational use of AND, OR, NOT and pre-defined filters, filters with arbitary complexity can be defined.

3. Apply the filter

If a network is small (number of nodes or edges less than 1000), the filter will be applied dynamically when a widget is added or any value is adjusted. If the network is large, then Apply button should be clicked to apply the filter.

The threshold (1000 by default) can be changed by defining a Cytoscape property value dynamicFilterThreshold.

4. Other filters

In the option menu pulldown, there are menu items “Create new topology filter”, “Create new NodeInteraction filter” and “Create new EdgeInteraction filter”.

Topology Filter

Topology filter will select nodes based on the properties of its near-by nodes (neighbors). To create a topology filter, choose the menu item “Create new topology filter” from the option menu. See below,


Interaction filter

Interaction filters are used to select nodes/edges based on the properties of their neighboring edges/nodes. See below for a node interaction filter.


The old filters is the one existed in previous versions of Cytoscape. Several types of filters are available.

Basic filters allow the selection of multiple nodes or edges according to singe attribute data:

Compound filters allow selection based on the application of pre-existing filters:

Example filters are shipped with the plugin to get started.

To use the old filters, go to Select → Use Old Filters. You will see a filters window which initially looks like the following:


If the first filter is selected, then the window looks as shown:


There are three panels in the Filters window:

Using Cytoscape's Editor, you can build and modify networks interactively by dragging and dropping nodes and edges from a palette onto the main network view window. The palette contains a set of shapes (for nodes) and arrows (for edges). The shapes on the palette are defined by the current Visual Style, with Node Shape and Node Color mapping into the shape and color of a node, and Edge Target Arrow mapping into the target arrow of an edge. An example of an editor, with the palette contained in CytoPanel 1, is shown below.


To edit an existing network, just select the Editor tab in CytoPanel 1. To start editing a new network, create a new network by going to File → New → Network → Empty Network.

To add a node to a network, drag and drop a node shape from the palette onto the canvas. To connect two nodes with an edge, drag and drop an arrow shape onto a node on the canvas. This node becomes the source node of the edge. Move the cursor and a rubber-banded line will follow the cursor. As the cursor passes over another node, that node is highlighted and the rubber-banded line will snap to a connection point on that second node. Click the mouse while over this node and the connection will be established.


You can abort the drawing of the edge by clicking on an empty spot on the palette.

Note that if you change the Visual Style, the palette used by the current network view will also change to be consistent with the mappings in the new Visual Style.

There is also an Edit → Connect Selected Nodes option that, when chosen, creates a clique amongst the selected nodes.

The editor provides accelerators for adding nodes and edges. Control-clicking at a position on the canvas creates a node at that position. The NODE_TYPE attribute of the node will be the same as the NODE_TYPE of the node most recently added, defaulting back to the original visual style. In this manner, you can use control-clicking as a kind of "stamp" to add multiple nodes of the same type to the network. Control-clicking on a node on the canvas starts an edge with its source at that node. Move the cursor and a rubber-banded line will follow the cursor. As the cursor passes over another node, that node is highlighted and the rubber-banded line will snap to a connection point on that second node. Control-click the mouse again and the connection will be established. The EDGE_TYPE attribute of the edge will be the same as the EDGE_TYPE of the edge most recently added, defaulting back to the original visual style. You can abort the drawing of the edge by control-clicking on an empty spot on the palette.

You can delete nodes and edges by selecting a number of nodes and edges, then selecting Edit → Delete Selected Nodes and Edges. You can recover any nodes and edges deleted from a network by going to Edit → Undo. However, note that this will restore all nodes and edges that were previously deleted from the network, not just those deleted by the most recent delete operation.

To install new features, go to the Plugin Manager at Plugins → Manage Plugins. On the left side of the window that pops up, you will see plugin folders labeled Currently Installed and Available for Install. Double-clicking on these will show sub-folders, and then the plugins themselves. To find out more about a specific plugin, click on its name to display some basic information on the right-hand side of the window.

The Currently Installed folder contains a number of default plugins that are fully integrated in every copy of Cytoscape, as well as any additional installed plugins. In contrast, the Available for Install folder displays plugins that may be installed. To install and use these plugins, click on the file name and then click on the Install button at the bottom of the window. A license agreement may appear, in which case you must accept it in order to download the plugin. You will then see a progress bar as the plugin is automatically downloaded and installed into your current version of Cytoscape. The progress bar will disappear when the download is complete. Other plugins can then be downloaded, or the manager can be closed by clicking the Close button.

Once a new plugin is added, it can immediately be used. Closing and restarting Cytoscape is not required. For example, installing the MCODE plugin will automatically result in new menu options such as Plugins → MCODE → Start MCODE.

CytoPanels are floatable/dockable panels designed to cut down on the number of pop-up windows within Cytoscape and to create a more unified user experience. These panels used to be called CytoPanel 1, 2, and 3. From 2.5, they are named based on their functions. The following screenshot shows the file yeastHighQuality.sif and GO annotations loaded into Cytoscape, performed Force-Directed layout, enable Align and Distribute tools, and then run MCODE plugin for the data sets. In Control Panel (at the left-hand side of the screen), the Network Manager, Network Overview, VizMapper, Filters, and Cytoscape Editor have been loaded. On the bottom of the panel, there is another CytoPanel called Tool Panel. In the Data Panel, the Attribute Browser has been loaded. In addition, Result of the analysis by MCODE plugin is shown in Result Panel (at the right-hand side).


The user can then choose to resize, hide or float CytoPanels. For example, in the screenshot below, the user has chosen to float all panels and toolbar:


In Cytoscape 2.3, a new network rendering engine is being introduced. The goal of the rendering engine is to be able to display large networks (>10,000 nodes), yet retain interactive speed. To accomplish this goal, a technique involving level of detail (LOD) is being used. Based on the number of objects (nodes and edges) being rendered, an appropriate level of detail is chosen. For example, by default, node labels (if present) are only rendered when less than 100 nodes are visible because drawing text is a relatively expensive operation. This can create some unusual behavior. If the screen currently contains 98 nodes, node labels will be displayed. If you pan across the network, such that now 101 nodes are displayed, the node labels will disappear. As another example, if the sum of rendered edges and rendered nodes is greater than or equal to a default value of 2000, a very coarse level of detail is chosen, where edges are always straight lines, nodes are always rectangles, and no antialiasing is done. The default values used to determine these thresholds can be changed by setting properties under Edit → Preferences → Properties... .

Low LOD vs High LOD

Table 26. 

Large Network with Low LOD

Large Network with High LOD





With low LOD values, all nodes are displayed as square and anti-alias is turned off. With high LOD values, anti-alias is turned on and nodes are displayed as actual shape user specified in the Visual Style.

NOTE: The greater these thresholds become, the slower performance will become. If you work with small networks (a few hundred nodes), this shouldn't be a problem, but for large networks it will produce noticeable slowing. The various thresholds are described below.

Annotations in Cytoscape are stored as a set of ontologies (e.g. the Gene Ontology, or GO). An ontology consists of a set of controlled vocabulary terms that annotate the objects. For example, using the Gene Ontology, the Saccharomyces Cerevisiae CDC55 gene has a biological process described as “protein biosynthesis”, to which GO has assigned the number 6412 (a GO ID).

GO 8150 biological_process
 GO 7582 physiological processes
   GO 8152 metabolism
    GO 44238 primary metabolism
      GO 19538 protein metabolism
        GO 6412 protein biosynthesis

Graphical View of GO Term 6412: protein biosynthesis


Cytoscape can use this ontology DAG (Directed Acyclic Graph) to annotate objects in networks. The Ontology Server (originally called "BioDataServer") is a Cytoscape feature which allows you to load, navigate, and assign annotation terms to nodes and edges in a network. Cytoscape 2.4 now has an enhanced GUI for loading ontology and associated annotation, enabling you to load both local and remote files.

The standard file formats used in Cytoscape Ontology Server are OBO and Gene Association. The GO website details these file formats:

An OBO file is the ontology DAG itself. This file defines the relationships between ontology terms. Cytoscape 2.4 and onwards can load all ontology files written in OBO format. The full listing of ontology files are available from the Open Biomedical Ontologies (OBO) website:

Sample OBO File - gene_ontology.obo:

format-version: 1.2
date: 27:11:2006 17:12
saved-by: midori
auto-generated-by: OBO-Edit 1.002
subsetdef: goslim_generic "Generic GO slim"
subsetdef: goslim_goa "GOA and proteome slim"
subsetdef: goslim_plant "Plant GO slim"
subsetdef: goslim_yeast "Yeast GO slim"
subsetdef: gosubset_prok "Prokaryotic GO subset"
default-namespace: gene_ontology
remark: cvs version: $Revision: 5.49 $

id: GO:0000001
name: mitochondrion inheritance
namespace: biological_process
def: "The distribution of mitochondria, including the mitochondrial genome, into daughter cells after mitosis or meiosis, mediated by interactions between mitochondria and the cytoskeleton." [GOC:mcc, PMID:10873824, PMID:11389764]
synonym: "mitochondrial inheritance" EXACT []
is_a: GO:0048308 ! organelle inheritance
is_a: GO:0048311 ! mitochondrion distribution

id: GO:0000002
name: mitochondrial genome maintenance
namespace: biological_process
def: "The maintenance of the structure and integrity of the mitochondrial genome." [GOC:ai]
is_a: GO:0007005 ! mitochondrion organization and biogenesis

Cytoscape provides a list of ontologies available in OBO format. If an Internet connection is available, Cytoscape will import ontology and annotation files directly from the remote source. The table below lists the included ontologies.

Table 28. 

Ontology Name


Gene Ontology Full

This data source contains a full-size GO DAG, which contains all GO terms. This OBO file is written in version 1.2 format.

Generic GO slim

A subset of general GO Terms, including higer-level terms only.

Yeast GO slim

A subset of GO Terms for annotating Yeast data sets maintained by SGD.

Molecule role (INOH Protein name/family name ontology)

A structured controlled vocabulary of concrete and abstract (generic) protein names. This ontology is a INOH pathway annotation ontology, one of a set of ontologies intended to be used in pathway data annotation to ease data integration. This ontology is used to annotate protein names, protein family names, and generic/concrete protein names in the INOH pathway data. INOH is part of the BioPAX working group.

Event (INOH pathway ontology)

A structured controlled vocabulary of pathway-centric biological processes. This ontology is a INOH pathway annotation ontology, one of a set of ontologies intended to be used in pathway data annotation to ease data integration. This ontology is used to annotate biological processes, pathways, and sub-pathways in the INOH pathway data. INOH is part of the BioPAX working group.

Protein-protein interaction

A structured controlled vocabulary for the annotation of experiments concerned with protein-protein interactions.

Pathway Ontology

The Pathway Ontology is a controlled vocabulary for pathways that provides standard terms for the annotation of gene products.


PATO is an ontology of phenotypic qualities, intended for use in a number of applications, primarily phenotype annotation. For more information, please visit the PATO wiki (

Mouse pathology

The Mouse Pathology Ontology (MPATH) is an ontology for mutant mouse pathology. This is Version 1.

Human disease

This ontology is a comprehensive hierarchical controlled vocabulary for human disease representation. For more information, please visit the Disease Ontology website (

Although Cytoscape can import all kinds of ontologies in OBO format, annotation files are associated with specific ontologies. Therefore, you need to provide the correct ontology-specific annotation file to annotate nodes/edges/networks in Cytoscape. For example, while you can annotate human network data using the GO Full ontology with human Gene Association files, you cannot use a combination of the human Disease Ontology file and human Gene Association files, because the Gene Association file is only compatible with GO.

If you have a network file and an attribute file, they should have a common key to map attributes onto network data. If those two do not have a common key, you need to do an extra step to add a shared key. The following is a quick tutorial to learn how to use Gene Name Mapping files.

From Cytoscape 2.6.0, you can import various kinds of ID sets from BioMart ( BioMart web service client is available as a set of plugins. You can install BioMartClient and BioMartUserInterface plugins from Plugin Manager window.


  1. Select: File → Import → Import attributes from Biomart...

  2. Select a data source. For ID mapping, select one of the Ensemble Genes data set. You need to choose correct species for your network.

  3. Select Attribute. If you want to import new ID sets matching current node IDs, select ID.

  4. Select Data Type. This should be the type of ID set selected in Attribute list. For example, if you select ID for Attribute and your network uses Entrez Gene ID for its node ID, you need to select EntrezGene ID(s) for Data Type.

  5. Select new ID sets from the list. Because BioMart server does not accept query to import lots of annotations at once, you can select only 3-5 attributes for each import.

  6. Press Import.

  1. Download name mapping files. Mapping files are available at: In this tutorial, we are going to use, which is a file set without prefixes for each gene names. Unzip the archive.

  2. Load sample network file. Open network import dialog from File-->Import-->Network (multiple file types)... Then click URL radio button and import Human Protein-Protein: Rual et al. (Subnetwork for tutorial).

  3. Open attribute table import dialog from File-->Import-->Attribute from Table.

  4. Select human.dic_cyto.txt as the input file.

  5. Check "Show Text File Import Options and click Transfer first line as attribute names checkbox.


    Uncheck "Show Text File Import Options

    and check Mapping Options. </listitem>
  6. Select EntrezGene as Primary Key.

  7. Right-click on EntrezGene column name and set the type to String.

  8. Do the same for HGNC.

  9. Right-click on Other Aliases and select List as the data type.

  10. Check Other Aliases as Alias (under "Alias?" checkboxes).

  11. Now the Table Import dialog looks like the following screenshot:
    • attachment:importdialog1.png

  12. Press Import. The network has new names in the text file as attributes.

    • attachment:nameMapping1.png

At this point, nodes have multiple names including HGNC, UniProt, and EntrezGene ID. You can import other attribute files using these keys. These imported names (IDs) are useful when you import GO Annotation.


Cytoscape 2.4 provides a graphical user interface to import both ontology and annotation files at the same time.

For convenience, Cytoscape has a list of URLs for commonly used ontology data and a complete set of Gene Association files. To import Gene Ontology and Gene Association files for the loaded networks, please follow these steps:

Important: All data sources in the preset list are remote URLs, meaning a network connection is required!

Network Data


Ontology Data


Annotation Data


Mapping Result


If you want to map ontology terms onto network objects, you need to create a custom annotation file. The annotation file should contain at least 2 columns: a primary key and an ontology term ID. The primary key is the value used for mapping between the annotation file and network. Usually, the node/edge ID is used as the primary key, but you may choose any of the available attributes. The Ontology term ID is the key used for mapping between the annotation file and the ontology DAG. Using these data sources, you can annotate network objects in Cytoscape.

Suppose you have a small network:

node_1 pp node_2
node_3 pp node_1
node_2 pp node_3

and you want to annotate this network with Ontology A, which is an ontology DAG available in OBO format. In this case, you need an annotation table file that looks like this:

node_1  OA_0000232
node_2  OA_0000441
node_3  OA_0000702

where OA_*** represents an ontology term ID. This example is a file with the minimum necessary number of columns; however, you can include additional columns that will appear as additional node attributes.

Some ontologies will be used to annotate edges or networks. For example, the Protein-protein interaction ontology is a controlled set of terms for annotating interactions between proteins, so ontology terms should be mapped onto edges (see example below).

node_1 (pp) node_2  MI:0445
node_3 (pp) node_1  MI:0046
node_2 (pp) node_3  MI:0346


The basic operation of the Ontology and Annotation Import function is the same as that of the Attribute Table Import. The main difference is that you need to specify an additional key for mapping:


By selecting a column from the "Key Column in Annotation File" dropdown list, you can specify the key for mapping between ontology terms and the annotation file.

  • Note: When you load Gene Association files, Cytoscape uses a special loader program designed only for Gene Association files. Because of this program, all attributes will be named automatically. Also, ontology IDs will be converted into term names and NCBI taxonomy ID will be converted into actual species name. However, for custom annotation files, those processes will not be applied. All ontology terms will be mapped as term IDs.

LinkOut provides a mechanism to link nodes and edges to external web resources within Cytoscape. Right-clicking on a node or edge in Cytoscape view opens a popup menu with a list of web links.

The external links are specified in a linkout.props file which is included in the cytoscape.jar file. The defaults include a number of links such as Entrez, SGD, iHOP, and Google, as well as a number of species-specific links. In addition to the default links, users can customize the LinkOut menu by adding (or removing) links by editing the linkout properties (found under Edit → Preferences → Properties...).

External links are listed as ‘key’-‘value’ pairs in the linkout.props file where key specifies the name of the link and value is the search URL. The LinkOut menus are organized in a hierarchical structure that is specified in the key. Linkout key terms specific for nodes start with the keyword nodelinkouturl, for edges this is edgelinkouturl.

For example, the following entry:

nodelinkouturl.Model Organism DB.SGD (yeast)=

places the SGD link under the yeast submenu. This link will appear in Cytoscape as:


In a similar fashion one can added new submenus.

The %ID% string in the URL is a place-holder for the node label. When the popup menu is generated this marker is substituted with the node label. In the above example, the generated SGD link for the YIM1 protein is:\=YIM1

If you want to query based on a different attribute you currently need to specify a different Node Label using the VizMapper.

For edges the mechanism is much the same; however here the placeholders %ID1% and %ID2% reflect the source and target node label respectively.

Currently there is no mechanism to check whether the constructed URL query is correct and if the node label is meaningful. Similarly, there is no ID mapping between various identifiers. For example, a link to NCBI Entrez from a network that uses ensembl gene identifiers as node labels will produce a link to Entrez using ensembl ID, which results in an incorrect link. It is the user's responsibility to ensure that the node label that is used as the search term in the URL link will result in a meaningful link.

From Cytoscape 2.6.0, you can use LinkOut from Attribute Browser. Basic functionality is the same, and the only difference is the parameter passed to the LinkOut is value in the selected cell.


Cytoscape is built with a number of open source third-party Java libraries. The Cytoscape team gratefully acknowledges the following libraries:

This product includes software developed by the Apache Software Foundation (

This product includes software developed by the JDOM Project (

One-step installation of the Cytoscape software is accomplished using the InstallAnywhere product from ZeroG Software, Inc. (

Handlers for the following format still exist in Cytoscape as legacy code, however we strongly recommend using the new formats (OBO + Gene Association) described in the previous section, since they are easier to download directly from the Gene Ontology project and use directly. Currently, users have no access to an import interface for this old format.

The annotation server requires that the gene annotations and associated ontology of controlled vocabulary terms follow a simple format. This simple format was chosen because it is efficient to parse and easy to use.

The flat file formats are explained below:

By example (the Gene Ontology - GO):

(curator=GO) (type=all)
0003673 = Gene_Ontology
0003674 = molecular_function [partof: 0003673 ]
0008435 = anticoagulant [isa: 0003674 ]
0016172 = antifreeze [isa: 0003674 ]
0016173 = ice nucleation inhibitor [isa: 0016172 ]
0016209 = antioxidant [isa: 0003674 ]
0045174 = glutathione dehydrogenase (ascorbate) [isa: 0009491 0015038 0016209 0016672 ]
0004362 = glutathione reductase (NADPH) [isa: 0015038 0015933 0016209 0016654 ]
0017019 = myosin phosphatase catalyst [partof: 0017018 ]

A second example (KEGG pathway ontology):

(curator=KEGG) (type=Metabolic Pathways)
90001 = Metabolism
80001 = Carbohydrate Metabolism [isa: 90001 ]
80003 = Lipid Metabolism [isa: 90001 ]
80002 = Energy Metabolism [isa: 90001 ]
80004 = Nucleotide Metabolism [isa: 90001 ]
80005 = Amino Acid Metabolism [isa: 90001 ]
80006 = Metabolism of Other Amino Acids [isa: 90001 ]
80007 = Metabolism of Complex Carbohydrates [isa: 90001 ]

The format has these required features:

The Gene Ontology (GO) project is a valuable source of annotation for the genes of many organisms. In this section we will explain how to:

Go to the GO XML FTP ( page. Download the latest go-YYYYMM-termdb.xml.gz file.

GO maintains a list of association files for many organisms; these files associate genes with GO terms. The next step is to get the file for the organism(s) you are interested in, and parse it into the form Cytoscape needs. A list of files may be seen at The rightmost column contains links to tab-delimited files of gene associations, by species. Choose the species you are interested in, and click 'Download'.

Let's use "GO Annotations @ EBI: Human" as an example. After you have downloaded and saved the file, look at the first few lines:

SPTR    O00115  DRN2_HUMAN              GO:0003677      PUBMED:9714827  TAS             F       Deoxyribonuclease II precursor  IPI00010348     protein taxon:9606              SPTR
SPTR    O00115  DRN2_HUMAN              GO:0004519      GOA:spkw        IEA             F       Deoxyribonuclease II precursor  IPI00010348     protein taxon:9606        20020425      SPTR
SPTR    O00115  DRN2_HUMAN              GO:0004531      PUBMED:9714827  TAS             F       Deoxyribonuclease II precursor  IPI00010348     protein taxon:9606              SPTR

Note that line wrapping has occurred here, so each line of the actual file is wrapped to two lines. The goal is to create from these lines the following lines:

(species=Homo sapiens) (type=Molecular Function) (curator=GO)
IPI00010348 = 0003677
IPI00010348 = 0004519
IPI00010348 = 0004531


(species=Homo sapiens) (type=Biological Process) (curator=GO)
NP_001366 = 0006259
NP_001366 = 0006915
NP_005289 = 0007186
NP_647593 = 0006899

The first sample contains molecular function annotations for proteins, and each protein is identified by its IPI number. IPI is the International Protein Index, which maintains cross references to the main databases for human, mouse and rat proteomes. The second sample contains biological process annotation, and each protein is identified by its NP (RefSeq) number. These two naming systems, IPI and RefSeq, are two of many that you can use to define canonical names when you run Cytoscape. For budding yeast, it is much easier: the yeast community always uses standard ORF names, and so Cytoscape uses these as canonical names. For human proteins and genes, there is no single standard.

The solution (for those working with human genes or proteins) is, once you have downloaded the annotations file, to:

  1. Decide which naming system you want to use.
  2. Download This cross-reference file, when used strategically, allows you to create Cytoscape-compatible annotation files in which the canonical name is the one most meaningful to you.

  3. Examine xrefs.goa to figure out which column contains the names you wish to use.

  4. Make a very slight modification to the python script described below, and then
  5. Run that script, supplying both xrefs.goa and that annotation file as arguments.

Here are a few sample lines from xrefs.goa:

SP      O00115  IPI00010348             ENSP00000222219;        NP_001366;              BAA28623;AAC77366;AAC35751;AAC39852;BAB55598;AAB51172;AAH10419; 2960,DNASE2     1777,DNASE2
SP      O00116  IPI00010349             ENSP00000324567;ENSP00000264167;        NP_003650;              CAA70591;       327,AGPS        8540,AGPS
SP      O00124  IPI00010353             ENSP00000265616;ENSP00000322580;        NP_005662;              BAA18958;BAA18959;AAH20694;             7993,D8S2298E

Note that line wrapping has occurred here – each line in this example starts with the letters SP. See the README file for more information (

Finally, run the script to create your three annotation files for human proteins:

  • bioproc.anno (GO biological process annotation)

  • molfunc.anno (GO molecular function annotation)

  • cellcomp.anno (GO cellular component annotation)

using the supplied python script. It may be necessary to modify this script slightly if RefSeq identifiers are not used as canonical names or if you are using a more recent version of Python.

python gene_association.goa_human xrefs.goa

(See below for Python script listing)

These scripts, as described above, require Python version 2.2 or later.

Script 1 -

#  translate a GO XML ontology file into a simpler
#  Cytoscape flat file
# RCS: $Revision: 1.3 $   $Date: 2003/05/18 00:38:43 $
import re, pre, sys
def flatFilePrint (id, name, isaIDs, partofIDs):
  isa = ''
  if (len (isaIDs) > 0):
    isa = '[isa: '
    for isaID in isaIDs:
      isa += isaID
      isa += ' '
    isa += ']'
  partof = ''
  if (len (partofIDs) > 0):
    partof = '[partof: '
    for partofID in partofIDs:
      partof += partofID
      partof += ' '
    partof += ']'
  result = '~np~%~/np~s = ~np~%~/np~s ~np~%~/np~s ~np~%~/np~s' ~np~%~/np~ (id, name, isa, partof)
  result = result.strip ()
  if (result == 'isa = isa' or result == 'partof = partof'):
    print >> sys.stderr, 'meaningless term: ~np~%~/np~s' ~np~%~/np~ result
    print result
if (len (sys.argv) != 2):
  print 'usage:  ~np~%~/np~s <someFile.xml>' ~np~%~/np~ sys.argv [0]
  sys.exit ();
inputFilename = sys.argv [1];
print >> sys.stderr,  'reading ~np~%~/np~s...' ~np~%~/np~ inputFilename
text = open (inputFilename).read ()
print >> sys.stderr,  'read ~np~%~/np~d characters' ~np~%~/np~ len (text)
regex = '<go:term .*?>(.*?)</go:term>';
cregex = pre.compile (regex, re.DOTALL)   # . matches newlines
m = pre.findall (cregex, text)
print >> sys.stderr, 'number of go terms: ~np~%~/np~d' ~np~%~/np~ len (m)
regex2 = '<go:accession>GO:(.*?)</go:accession>.*?<go:name>(.*?)</go:name>'
cregex2 = re.compile (regex2, re.DOTALL)
regex3 = '<go:isa\s*rdf:resource="*?)"\s*/>'
cregex3 = re.compile (regex3, re.DOTALL)
regex4 = '<go:part-of\s*rdf:resource="*?)"\s*/>'
cregex4 = re.compile (regex4, re.DOTALL)
goodElements = 0
badElements = 0
print '(curator=GO) (type=all)'
for term in m:
  m2 = (cregex2, term)
  if (m2):
    goodElements += 1;
    id = (1)
    name = (2)
    isaIDs = []
    m3 = re.findall (cregex3, term);
    for ref in m3:
      isaIDs.append (ref)
    m4 = re.findall (cregex4, term);
    partofIDs = []
    for ref in m4:
      partofIDs.append (ref)
    flatFilePrint (id, name, isaIDs, partofIDs)
    badElements += 1;
    print >> sys.stderr, 'no match to m2...'
    print >> sys.stderr, "---------------\n~np~%~/np~s\n------------------" ~np~%~/np~ term
print >> sys.stderr,  'goodElements ~np~%~/np~d' ~np~%~/np~ goodElements
print >> sys.stderr, 'badElements ~np~%~/np~d' ~np~%~/np~ badElements

Script 2 -

import sys
def fixCanonicalName (rawName):
# for instance, trim 'YBR085W|ANC3' to 'YBR085W'
  bar = rawName.find ('|')
  if (bar < 0):
    return rawName
  return rawName [:bar]
def fixGoID (rawID):
  bar = rawID.find (':') + 1
  return rawID [bar:]
def readGoaXrefFile (filename):
  lines = open (filename).read().split ('\n')
  result = {}
  for line in lines:
    if (len (line) < 10):
    tokens = line.split ('\t')
    ipi = tokens [2]
    np = tokens [5]
    semicolon = np.find (';')
    if (semicolon >= 0):
      np = np [:semicolon]
    if (len (ipi) > 0 and len (np) > 0):
      result [ipi] = np
  return result
if (len (sys.argv) != 3):
  print 'error!  parse   <gene_associations file from GO> <goa xrefs file> '
  sys.exit ()
associationFilename = sys.argv [1];
xrefsFilename = sys.argv [2]
species = 'Homo sapiens'
ipiToNPHash = readGoaXrefFile (xrefsFilename)
tester = 'IPI00099416'
print 'hash size: ~np~%~/np~d' ~np~%~/np~ len (ipiToNPHash)
print 'test map: ~np~%~/np~s -> NP_054861: ~np~%~/np~s ' ~np~%~/np~ (tester, ipiToNPHash [tester])
bioproc = open ('bioproc.txt', 'w')
molfunc = open ('molfunc.txt', 'w')
cellcomp = open ('cellcomp.txt', 'w')
bioproc.write ('(species=~np~%~/np~s) (type=Biological Process) (curator=GO)\n' ~np~%~/np~ species)
molfunc.write ('(species=~np~%~/np~s) (type=Molecular Function) (curator=GO)\n' ~np~%~/np~ species);
cellcomp.write ('(species=~np~%~/np~s) (type=Cellular Component) (curator=GO)\n' ~np~%~/np~ species);
sys.stderr.write ('found ~np~%~/np~d lines\n' ~np~%~/np~ len (lines))

for line in lines:
  if (line.find ('!') == 0 or len (line) < 2):
  tokens = line.split ('\t')
  goOntology = tokens [8]
  goIDraw = tokens [4]
  goID = goIDraw.split (':')[1]
  ipiName = fixCanonicalName (tokens [10])
  if (len (ipiName) < 1):

  if (not ipiToNPHash.has_key (ipiName)):
  refseqName = ipiToNPHash [ipiName]
  printName = refseqName
  #printName = ipiName
  if (ipiName == tester):
    print '~np~%~/np~s (~np~%~/np~s) has go term ~np~%~/np~s' ~np~%~/np~ (tester, printName, goID)
  if (goOntology == 'C'):
    cellcomp.write ('~np~%~/np~s = ~np~%~/np~s\n' ~np~%~/np~ (printName, goID))
  elif (goOntology == 'P'):
    bioproc.write ('~np~%~/np~s = ~np~%~/np~s\n' ~np~%~/np~ (printName, goID))
  elif (goOntology == 'F'):
    molfunc.write ('~np~%~/np~s = ~np~%~/np~s\n' ~np~%~/np~ (printName, goID))



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from Notes on memory consumption, Cytoscape User Manual

For users interested in loading large networks, the amount of memory needed by Cytoscape will increase. Memory usage depends on both number of network objects (nodes+edges) and the number of attributes. Here are some rough suggestions for memory allocation:

Suggested Memory Size Without View

Table 29. 

Number of Objects (nodes + edges)

Suggested Memory Size

0 - 70,000

512M (default)

70,000 - 150,000


Suggested Memory Size With View

There are a number of ways to change Cytoscape's memory allocation, depending on your preferred method of opening the application. All of them will change Cytoscape's default memory parameters except starting from the command line.

Option A: Command line startup (note: this does not permanently change Cytoscape's default 512M setting)

If you are opening Cytoscape from the command line using the command

then you can increase the value of –Xmx to the desired amount of memory. For example:

Option B: Using cytoscape.bat (Windows systems)

Option C: Using (UNIX, Linux, and Mac OS X systems)

Option D: Using the Cytoscape icon (Mac OS X systems)

Option E: Using the Cytoscape icon (Windows systems)