D634 Nucleic Acids Research, 2008, Vol. 36, Database issue Java Universal Network/Graph (JUNG) framework (http://jung.sourceforge.net/index.html). BROWSING THE BACTERIOME Bacteriome.org provides a number of web-based forms for querying the interaction datasets and selecting one or more proteins for either a more detailed view of the gene annotations or for viewing within the context of its interactions with other proteins: (1) Text-based searches— these include keyword searches against annotations such as gene names, protein domains, gene ontology terms and swissprot descriptions (e.g. identify all the genes which have been annotated with the term ‘kinase’); (2) Sequence similarity searches—Bacteriome.org features a BLAST page that enables users to identify E. coli homologs to their sequence of interest (e.g. identify all the genes which possess sequence similarity to protein X); (3) Phylogenetic proﬁle searches—this allows the user to identify genes that have similar sequences in selected groups of organisms (e.g. identify all the genes which have homologs in all plants and protists); (4) Chromosomal location searches— this page allows the user to zoom in on a section of the E. coli genome and select genes on the basis of their local neighborhood (e.g. identify all genes that are within 50 kb of rpsH). (5) Browsing complexes/functional modules—ﬁnally, a Java applet is provided which allows the visualization of the predicted protein complexes/ functional modules from which users may select one or more complexes for a more detailed view. After performing a typical search (e.g. entering the term ‘kinase’ in the ‘Wild Search’ box on the left menu), the user is ﬁrst presented with a summary page detailing the number of proteins matching the search (Figure 1A). In addition to formatting options, the user may select one of the three interaction datasets for subsequent network visualization. The following results page then provides the user with a list of proteins and brief descriptions (Figure 1B) from which individual, groups or even the entire dataset of proteins may be selected for either a detailed view of each protein (providing access to functional data, gene ontology terms, protein domains, sequence data and so forth) or a view of the network in which the selected protein(s) operate. The network view features a purpose built interactive Java applet in which proteins are represented by nodes in a graph (Figure 1C). The applet provides the user with a range of diﬀerent layout settings and options for visualization of the network. These include the ability to navigate and zoom in on parts of the network, identifying nodes and visualizing the weights of interactions (which provide a measure of conﬁdence). Placing the mouse over individual nodes provides details of individual proteins while a select function allows users to obtain a more detailed view of one or more nodes. The initial view of the network colors each protein (node) according to its COG functional category (16) and also displays proteins that directly interact with the initially selected proteins (the size of each node represents the distance from the initially selected proteins). However, uniquely, the applet also features the ability to change the node representations to show either the domain architecture of each protein (Figure 1D) or the phylogenetic proﬁle of each protein (Figure 1E). Other features provided in the network view include the ability to alter the layer of neighbors presented in the network (e.g. nearest neighbors to the selected proteins, next nearest neighbors to the selected proteins) and the ability to choose which interaction dataset to visualize. Browsing the experimental protein complexes or the theoretical functional modules associated with the net- works takes the user directly to a network view of the complexes/modules in which each node (representing a complex/module) is visualized as a pie chart showing the proportion of proteins in the complex/module associated with particular COG functional categories (Figure 1F). Here, the size of each node indicates the number of protein constituents, details of which may be obtained through placing the mouse over the node in question. Again, users may select individual or groups of nodes for a more detailed report of the associated proteins (including the ability to visualize their local network). FUTURE DIRECTIONS We are continuing to generate new physical interaction data for E. coli and in the near future we hope to have completed interaction mapping for at least three quarters of E. coli proteins. These datasets together with updated predictions of protein complexes will be integrated in the Bacteriome resource as they are generated. We are also planning to host additional experimental and theoretical bacterial interaction datasets such as the yeast two-hybrid datasets for Helicobacter pylori (23) and Campylobacter jejuni (24). The inclusion of these datasets will necessitate the creation of corresponding knowledgebases providing detailed functional and structural annotations. These will be developed using the existing resource for E. coli (6) as a template. Aside from the interaction datasets, we are also seeking to extend the types of metadata that may be incorporated into the resource. These might include expression datasets (7) in which the expression pattern of a protein under a set of conditions could be visualized within a network setting using pie charts in an analogous fashion to that implemented by the GenePro plugin for Cytoscape (25,26). ACKNOWLEDGEMENTS This work was funded by the Canadian Institute of Health Research (CIHR). J.P. is supported by a New Investigators award from CIHR. Funding to pay the Open Access publication charges for the article was provided by CIHR. Conﬂict of interest statement. None declared.