Summary

After the Cytoscape retreat held in San Diego in 2005, it became obvious that Cytoscape needs to support metanodes. A metanode is a graph node that contains a subgraph. There are many different ways of modeling and visualizing metanodes, depending on the biological application in question.

The objective is to:

• Clearly identify the biological applications for metanodes
• Clearly define what type of metanode visualization and modeling is needed for each one of these applications
• Come up with a project plan to implement these different ways of visualizing/modeling metanodes

This RFC is a forum for discussing each one of these points.

Iliana Avila developed a Cytoscape plugin that implements one possible way of modeling and visualizing metanodes. This plugin can be used as a concept plugin, so that everyone involved has a clearer idea of what is a metanode, and how it can be used. It is by no means the final implementation of metanodes. See "Concept Plugin" section in this Wiki to learn how to obtain plugin.

["Comment summary"]

Biological Applications and their MetaNode Needs

Please add your biological application, and a possible metanode solution.

1. Biomodules

• Biological application: Group proteins in a graph of protein-protein interactions that have a collective function in the cell ([http://www.genome.org/cgi/content/abstract/14/3/380]) in order to discern higher levels of organization in the biological network

• Metanode solution: A group of proteins can be visualized by a single node that has visual and topological characteristics that reflect the underlying group of proteins. For example, the size of the node is proportional to the number of proteins it represents, its connections to other proteins reflect connections from its inside proteins to other proteins, its color represents the average expression levels of its members for a certain condition, etc. See the image in [http://labs.systemsbiology.net/galitski/projs/biomodules/index.html]. Round nodes are metanodes.

2. Protein Complexes - Pico/GenMAPP (note by Cline/Pasteur)

• Biological application: Group proteins in a pathway that are known to form complexes in order to simplify visualization and store known associations in the data model.

• Metanode solution: Ideally, there could be two views of protein complexes. (1) A collapsed view, similar to that used in Biomodules above, but with a default size (not scaled by number of members) and a label that is unique to the metanode (i.e., PKA complex). (2) A stacked view, where all the children nodes are visible and simply stacked (like gene boxes in GenMAPP).

• Extensions: Note that the solution should also fit for protein domains since the particular boundaries between protein domains in a single chain and between proteins in a complex is rather arbitrary, a matter of evolutionary fate. The solution should also extend to the grouping of paralogs and splice variants.

• Further note (Cline/Pasteur): these extensions become especially interesting, now that there are high-throughput platforms to measure separate expression levels of genomic features. Biologically, the likelihood of a given interaction will depend on the isoforms produced in the cell, and whether or not the protein features involved in the interactions are expressed. One Metanode implentation is for each child node to represent a different component of the gene or protein - an exon, or a protein domains. Where the right data is available, interaction would be tied to the components involved in the interaction - much as is done now in the Domain Network plugin. A second Metanode implementation would have each child node represent a protein isoform of the parent. Again, where the right data is available, interactions would be associated with the isoforms that can interact. Here, some thought should go into how to handle the edges. If one metanode in an interaction has N child nodes, representing N protein isoforms, and the second has M modes representing M isoforms, having up to N*M different edges represents a lot of complexity.

3. Intragenic Features - Pico/GenMAPP

• Biological application: Associate features such as exon structure, promoter regions and SNP positions with proteins in a pathway. These features are quickly becoming the preferred level of abstraction for microarray analysis and other high-throughput methods. We must be able to translate these massive datasets into biological context (i.e., pathways) in an efficient manner.

• Metanode solution: By associating these feature-level nodes with a protein node in a parent-child relationship, we could efficient map these data types to the biology at the pathway level. These associations might be best viewed as collapsed nodes colored by specified algorithms that consider the data type (e.g., a splicing analysis on all exon data mapped to the whole gene). And instead of expanding the node on the same network, perhaps we could restrict the expansion to a new network (like a small pop up window) that displays the feature-level nodes and direct data mapping, e.g., expression level for each exon associated with the protein. See [attachment:HighResWindow.jpg attached image] for an illustration of the pop-up window.

• Note: This type of metanode seems to be qualitatively different and might require separate terminology. Then, again, maybe not?

["Comment biological applications"]

Implementation Strategy

Concept Plugin

CVS

Login anonymously into CVS and checkout csplugins:

1. cvs -d :pserver:anonymous@bordeaux.ucsd.edu:/cvsdir5 login

2. cvs -d :pserver:anonymous@bordeaux.ucsd.edu:/cvsdir5 co cytoscape

3. cvs -d :pserver:anonymous@bordeaux.ucsd.edu:/cvsdir5 logout

The plugin is located in: /csplugins/isb/iavila/metaNodeViewer. Edit the build.xml file if necessary to point to the correct Cytoscape path (works with latest Cytoscape version). Type: ant run

["Comment concept plugin"]