To explore a biologically relevant subset, we can obtain a sub-network using only genes in the experimental data:.
This selects all entries that have upper case letters or numbers. Note that all of the gene expression data has gene symbols that match that regular expression, but if the gene only exists in the BioGRID interaction network, that field will be blank as in Figure 2. This should select approximately 5, nodes, as indicated at the bottom of the Select panel. Now with the selected nodes, we would like to create a sub-network from the original BioGRID network:.
Depending on the default settings, a network view may be created. This may take some time. The first one is the original network and the second one is the sub-network filtered by the experiment. Otherwise, just click on that network to select it.
This may take some time, too. The result should look similar to Figure 3. A unique strength of Cytoscape is its rich collection of Apps that can perform various analyses. We will now use some of these Apps to analyze our data.
Ensure you have the specific App installed as described in Support Protocol 2 before you attempt the protocol. One common task in biological network analysis is to identify clusters or modules of biological molecules that share similar properties.
For instance, a cluster of genes whose expression changes similarly to external stimuli may have related function and participate in the same biological processes. The clusterMaker App Morris et al.
For instance, we can choose to find clusters in a gene network based on expression profiles using hierarchical or K-means clustering, or identify densely intra-connected sub-networks using Markov clustering or community clustering Su et al. A clustered heatmap should now be shown similar to Figure 4.
Some well-defined clustering patterns can be identified. The first cluster has a single protein HSP12 , but the next cluster contains genes. The corresponding nodes in a cluster can be selected by clicking on the horizontal lines in the dendrogram as shown in Figure 4. These genes are all characterized by a tendency to have elevated expression at the time of the temperature change and significantly decreased expression after 15 minutes.
Some of the other genes also show a tendency towards decreased expression after 30 minutes. When we obtain gene clusters from a network, a natural follow-up question is how do these clusters map to known gene function? We will now use BiNGO to identify enriched functions in the previously identified clusters.
Select a name for the cluster e. The results are displayed both as a table ordered by p-value of term enrichment and a network of ontology terms where the node color represents the p-value of the over-represented terms as in Figure 6.
As might be expected, the top scoring p-values are all related to ribosome biogenesis and RNA processing.
As the cell is shocked, the first step is to ramp-up its ability to make proteins to respond to the new conditions. In this protocol, we import data from the human disease network constructed by Goh et al.
Goh et al. A human disease network HDN was constructed by connecting diseases that share the same gene mutations, and a disease gene network DGN was constructed similarly via associated diseases.
Some of the resulting functional modules were interpreted quantitatively using microarray and protein-protein interaction networks. We will now explore the network from this paper. We want to skip the comment lines. There are three interaction sources: R and S indicate two literature sources, and L indicates literature curation. If any of the columns are shown as blue, click on the column header to make them grey.
If all settings are correct, the import dialog should look similar to Figure 7. When a network is imported from a file, it has no layout information — by default Cytoscape lays out the network as a grid. We will demonstrate different layouts. Depending on your computer, this step may take a few minutes.
Your view should look similar to Figure 8. Click on the arrow on the right to reveal possible options. Choose a color for each of the interaction data sources for L, R, and S. Your view should look similar to Figure 9. Cytoscape sometimes hide labels, node graphics and other information to improve visualization speed. You can see that even though this is a very dense network, protein interactions from the same source tend to cluster with each other.
Identifying densely connected nodes e. Each of the clusters can be exported as a sub-network. You should now see a network like Figure In this case MCODE identified a functionally homogenous cluster from a large interaction network purely based on how densely interconnected the nodes were. You can grow or shrink the discovered local clusters by adjusting the Size Threshold slider.
Drag the slider several notches and the cluster will expand. You can create a new sub-network and check the functional term association using BiNGO following similar procedures in protocol 3. Although Cytoscape is often used to visualize gene, protein and metabolic networks, it can be used to visualize other biomedical networks as well. In the following example, we will illustrate how to visualize the human disease network. Your import dialog should look like Figure Your network view should be similar to Figure Note that there are many duplicate edges.
The diseases are linked by shared gene mutations and each link is documented twice in this file. If the view is not automatically refreshed — pan or zoom the network view and the duplicate edges will disappear. You can check the imported disease attributes as in Figure To search for diseases with keywords, you can type the search terms in the top right corner of the toolbar. Each disease class is then automatically assigned a random color as node fill.
This value indicates how many gene mutations different diseases share. Set the start width to 1, and set the increment to 3.
The network should look like Figure Disorders that are connected with thick edges indicate more shared number of genetic mutations. Now Disease name will be displayed in each disorder instead of disease IDs. Double click on the mapping bar, and drag vertical ticks to make the node size range from 30 — mapped to degree 1 — Your network view should look similar to Figure The two selected nodes are Leukemia and Deafness classes, respectively.
Diseases in the same class tend to be placed near each other and form clusters that share similar gene mutations. The protocols provided here can stand alone as methods for analyzing biological networks and also serve as a starting point for more in-depth analysis using various Cytoscape analysis and visualization apps.
The two basic protocols have focused on protein-protein interaction networks, but Cytoscape has been used to explore structural networks Morris et al. If the starting point is a list of genes and no source network is available, apps like the AgilentLiteratureSearch App Vailaya et al.
Basic Protocol 1 demonstrates the annotation of a protein-protein interaction network with expression data, although the approach could be used to annotate networks with a wide variety of additional data. The hierarchical clusters derived from the expression data provide just one approach to exploring this data set.
For instance, critical genes and proteins tend to be hubs nodes connected to many other nodes or part of the shortest path through the network between two other nodes Yu et al.
In addition to the traditional hierarchical clustering and heat map visualization, clusterMaker2 may be used to create a co-expression network where the edges between nodes represent expression profile similarities.
To explore the concept of modules in more detail, Cytoscape apps such as jActiveModules Ideker et al. Unlike the co-expression network approach mentioned above, jActiveModules takes the network topology into account. This can highlight subnetworks where the genes in that subnetwork experience similar expression patterns.
A plausible biological explanation for co-expression of genes or proteins is functional relatedness. This is especially true in prokaryotes, where functionally-related genes may be organized into the same operons in the genome. Genes involved in a complex can exhibit just-in-time assembly, where one highly regulated critical gene controls the overall activity of the entire complex de Lichtenberg et al.
This helps identify functions enriched in a set of genes, including sets of genes that are co-expressed. Basic Protocol 2 demonstrates the workflow of using Cytoscape to visualize and annotate large biomedical networks. One important and useful Cytoscape feature is its Style Manager formerly called VizMapper , which allows researchers to translate a variety of attribute data, such as gene expression profiles, functional gene groups and pathways, and protein-protein interaction types, to intuitive graphic representations that facilitate exploratory knowledge discovery.
These clusters can be immediately visualized in the Network view, which is especially helpful for visualizing and understanding the local topologies and functional features in very large networks such as the Human Disease Network having thousands of nodes and edges.
In our protocol, we imported disease network and additional annotation data containing disease categories, and utilize such information to aid network visualization. Such data can also be imported directly from many external sources.
Using Cytoscape, users can also integrate their own experimental data with existing network data and functional annotations. The term biological network usually refers to two types of data: those human-curated from the literature and those that are experimentally derived.
The former is built on curated and verified knowledge such as those stored in pathway and protein interaction databases. The latter is derived from experiments, such as protein interaction screens or gene expression correlations. Combining these two data sources and other functional annotations enables researchers to support their experiment and identify new patterns from the data.
Visual exploration tools are required for this, especially if the data are large. The omics era has brought many opportunities and challenges for network analysis. The sharp decline in the cost of high-throughput technology has made it possible to efficiently measure tens of thousands of molecular profiles at once, for hundreds of different sample groups and experimental conditions. Such rich repositories of experimental data, along with the human curated annotations from the literature, enable researchers to quickly identify novel connections between their observations and existing knowledge, thereby enabling testing of new hypotheses.
In addition to the traditional genomics, transcriptomics and proteomics, accurate metabolomics, phenomics, lipidomics are also becoming more accessible. Together, these data offer different snapshots of a target organism. Even though robust and scalable computational and statistical methods have been developed to mine new signals, it is often difficult for the researcher to explore such data without high-performance, versatile and interactive visualization software.
Cytoscape was originally designed as a simple tool to visualize networks with hundreds, or maybe a few thousands, of nodes.
Thanks to the continuous community support, it has expanded its capabilities and scope to handle bigger, more complex data and evolved into a sophisticated platform that can be used for many network analysis purposes. New features include:. Our protocols above demonstrate common workflows, though many other workflows are possible. Also, new Apps are regularly posted to the Cytoscape App store, many enabling new workflows.
A good way to find popular Apps is to rank all Apps by popularity Number of downloads on the App store website. Cytoscape has run out of memory to load the network, or the operating system is swapping RAM to the hard disk because your Cytoscape. Add more memory, then register the new memory with Cytoscape per the Note on Memory Consumption section of the Cytoscape user manual.
When starting Cytoscape on Windows, you receive messages indicating that the JMV could not be found, is defective, or the maximum heap size is too large. You may have inadvertently installed 32 bit Java, which is the default download from java. If you have installed a 64 bit Cytoscape, the 32 bit Java is inappropriate. Uninstall 32 bit Java and install 64 bit Java instead.
When you restart Cytoscape, you should see its splash screen. When starting Cytoscape, the splash screen appears and nothing more happens, or it shows the names of Cytoscape modules being loaded, but then freezes before showing a Cytoscape window. Cytoscape and its code cache may have become unsynchronized, possibly as a result of installing a newer or older Cytoscape.
Alternatively, a new Cytoscape installation could be taking extra time up to 3 minutes to build its code cache. After loading a large network, the network name appears in the Network tab, but there is no window showing the network. For large networks, Cytoscape shortens the overall load time by not drawing the network view window.
Right click on the network in the Network tab, and choose the Create View menu item. The network window will appear within a few seconds. You can create a more manageable subnetwork by using the procedure in Step 14 of Basic Protocol 1. Expression or attribute data files are not properly integrated with the loaded network. The gene identifier columns that synchronize the two files do not match exactly, or the files may not be in the correct format. Cytoscape development is a large community effort.
We thank all of the core Cytoscape developers and App developers who have enriched the Cytoscape user experience with their ideas. National Center for Biotechnology Information , U.
Curr Protoc Bioinformatics. Author manuscript; available in PMC Sep 8. Gang Su , 1 John H. Morris , 2 Barry Demchak , 3 and Gary D. Bader 4. John H. Gary D. Author information Copyright and License information Disclaimer. Copyright notice. The publisher's final edited version of this article is available at Curr Protoc Bioinformatics.
See other articles in PMC that cite the published article. Abstract Cytoscape is one of the most popular open-source software tools for the visual exploration of biomedical networks composed of protein, gene and other types of interactions. Introduction A network model or graph in mathematics represents associations between entities in a system. Macintosh OSX Linux Ubuntu The Cytoscape desktop and the welcome screen should now appear. Support Protocol 2: Search and Install Apps Cytoscape Apps are optional extensions to the Cytoscape software that provide specific additional features.
A dialog should pop out showing the progress. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as predicting gene function and constructing pathways.
Cytoscape provides core functionality to load, visualize, search, filter, and save networks, and hundreds of Apps extend this functionality to address specific research needs. The latest generation of Cytoscape version 3. This protocol aims to jump-start new users with specific protocols for basic Cytoscape functions, such as installing Cytoscape and Cytoscape Apps, loading data, visualizing and navigating the networks, visualizing network associated data attributes , and identifying clusters.
It also highlights new features that benefit experienced users.
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