Abstract
BackgroundA wide variety of biological data can be modeled as network structures, including experimental results (e.g. protein-protein interactions), computational predictions (e.g. functional interaction networks), or curated structures (e.g. the Gene Ontology). While several tools exist for visualizing large graphs at a global level or small graphs in detail, previous systems have generally not allowed interactive analysis of dense networks containing thousands of vertices at a level of detail useful for biologists. Investigators often wish to explore specific portions of such networks from a detailed, gene-specific perspective, and balancing this requirement with the networks' large size, complex structure, and rich metadata is a substantial computational challenge.ResultsGraphle is an online interface to large collections of arbitrary undirected, weighted graphs, each possibly containing tens of thousands of vertices (e.g. genes) and hundreds of millions of edges (e.g. interactions). These are stored on a centralized server and accessed efficiently through an interactive Java applet. The Graphle applet allows a user to examine specific portions of a graph, retrieving the relevant neighborhood around a set of query vertices (genes). This neighborhood can then be refined and modified interactively, and the results can be saved either as publication-quality images or as raw data for further analysis. The Graphle web site currently includes several hundred biological networks representing predicted functional relationships from three heterogeneous data integration systems: S. cerevisiae data from bioPIXIE, E. coli data using MEFIT, and H. sapiens data from HEFalMp.ConclusionsGraphle serves as a search and visualization engine for biological networks, which can be managed locally (simplifying collaborative data sharing) and investigated remotely. The Graphle framework is freely downloadable and easily installed on new servers, allowing any lab to quickly set up a Graphle site from which their own biological network data can be shared online.
Highlights
A wide variety of biological data can be modeled as network structures, including experimental results, computational predictions, or curated structures
BMC Bioinformatics 2009, 10:417 http://www.biomedcentral.com/1471-2105/10/417 tein-protein interaction networks (PPIs, [6,7]) to microarray correlations [8,9] have all been represented as graphs; computational predictions including regulatory networks [10,11] and functional relationships [12,13] are generally presented as network structures as well
Genomic data is often large, but not so large that it falls into the class of "huge" network visualization; tools for exploring such tremendous networks typically hide the details that are vital for understanding biological networks
Summary
Graphle provides a web-based system for interactively browsing large biological networks. These genes are sent to the server, which uses a configurable graph query algorithm [13,26], described above) to select the subgraph of the requested network most relevant to the query genes (Figure 2A) This subgraph is of sufficiently small size (~50 fully connected vertices and the associated edge weights) to be sent to the client in full; the client provides a configurable visualization of the subgraph for the user. Exporting graph images and data Graphle provides the opportunity for users to export the current subgraph as an image or as raw textual data (e.g. for further analysis, Figure 2F) Data exported in this manner is provided as a simple edge list linking unique vertex identifiers (i.e. gene names) with the weight of the edge joining them (the semantics of which are dependent on http://www.biomedcentral.com/1471-2105/10/417 the source of the underlying graph). The currently visible, filtered subgraph can be exported as an image of quality suitable for publication
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