and protein–protein interaction (PPI) data will allow the identiﬁcation of not only which proteins interact, but also their overall functional organization, domain associations and evolutionary relationships. Here we introduce a new database resource focusing on the collation of these datasets from E. coli to provide a detailed view of a model bacterial interactome (Bacteriome.org). Unlike other excellent resources which collate interaction data for a range of diﬀerent organisms, for example, STRING (4), BioGRID (9) and ProLinks (2), our focus is to collate and exploit the unique properties of these complementary datasets to provide an integrated and detailed view of structural, functional and evolutionary relationships within the E. coli inter- actome. Two types of interaction networks are presented: an ‘experimental’ dataset that builds on a previously published high throughput protein–protein interaction screen (3); and a ‘theoretical’ dataset of predicted functional interactions constructed from the Bayesian integration of functional genomic and proteomic datasets (1). In addition to web forms allowing the interrogation and navigation of the datasets, a specialized Java applet has been created for the visualization of associated metadata such as functional categories of proteins, complex membership, protein domains and phylogenetic proﬁles, within the context of the interaction networks. The database is open to browsing without restriction. Links are provided to allow users to freely download the interaction datasets. CONSTRUCTION OF THE RESOURCE The Bacteriome resource currently provides access to three recently derived interaction datasets for E. coli—one theoretical and two experimental (unpublished data). Detailed information on their construction and analysis is outside the scope of the current article, but is available online and will be presented in additional publications. The ﬁrst consists of a set of 3989 functional interactions predicted between 1927 proteins. These predictions were generated from the integration of a variety of experi- mental and computationally derived functional genomic and proteomic datasets. Sources for the experimental datasets include large- and small-scale PPI’s obtained from the database of interacting proteins (DIP) (10) which includes a recently published high throughput study of E. coli PPI’s (1), and co-expression data from a recent comparative study of gene expression proﬁles (11). Sources for the theoretical datasets include operon, gene neighborhood, gene fusion and phylogenetic proﬁle data obtained from the Prolinks database (2); a set of interactions previously predicted from literature data (12) and a set of interactions previously predicted using the ‘interolog’ approach (13). Predictions of functional linkages between pairs of proteins were obtained using a similar naı¨ ve Bayes approach previously applied to yeast (14). In this scheme, weights are assigned to reﬂect the relative conﬁdence associated with each dataset. These are Nucleic Acids Research, 2008, Vol. 36, Database issue D633 derived as log likelihood scores measuring the likelihood that pairs of genes are functionally linked within a given pathway (as deﬁned by the EcoCyc database (5)) given the evidence. Benchmarks based on: the Kyoto Encyclopaedia of Genes and Genomes (KEGG) (15); Clusters of Orthologous Genes (COG) (16); and Gene Ontology annotations (17) gave similar results. The combination of weights for an interaction identiﬁed across diﬀerent datasets was then used to quantify the evidence that a given interaction is real. We used data from small-scale pull-down experiments obtained from DIP as our ‘gold standard’ set of functional linkages for determining the cutoﬀ score for inclusion of functional linkages in the ﬁnal theoretical interaction dataset. Further details including an analysis of the performance of this method are provided on the website. The two experimental datasets represent physical interactions obtained from a high throughput screen using our previously described TAP-TAG technology (3). These include a ‘core’ dataset of 4863 interactions between 1100 proteins and an ‘extended’ dataset of 9860 interactions between 2131 proteins. For each interaction a puriﬁcation enrichment (PE) score is derived which takes into account the bait_prey, prey_bait and prey_prey relationships of the interaction. Individual scores were calculated for each component based on a probabilistic discriminant function as described previously (18). The primary aﬃnity puriﬁcation scores (obtained through MS-LCMS and MALDI) and the PE scores were both used to evaluate the overall conﬁdence of the interaction. Conﬁdence was calculated through a logistic regression model using a weighted sum to integrate the scores (see website for further details). The two datasets were obtained using diﬀerent cutoﬀ values of their conﬁdence scores. For the core dataset we used a conﬁdence score cutoﬀ of 0.7 while for the extended dataset, we used a slightly lower conﬁdence score cutoﬀ of 0.5. For each interaction dataset, clusters of proteins representing functional modules (for the theoretical dataset) or protein complexes (for the experimental datasets) were predicted on the basis of their common interactions using the MCL algorithm as previously described (19). Phylogenetic proﬁles [representing the presence or absence of a sequence across a set of genomes (20,21)], were generated via a series of BLAST analyses (22) across 199 selected genomes (19 eukaryotes, 165 bacteria and 15 archaea). The Bacteriome resource is implemented using postgreSQL (http://www.postgresql.org). The previously constructed E. coli knowledgebase (6) was downloaded as a set of ﬂat ﬁles and used to build the initial resource. The additional datasets (interactions, phylogenetic proﬁles and predictions of protein complexes/functional modules) were imported as sets of additional tables. Users are able to browse the data via a series of php-based web pages. In addition, we have created a specialized Java applet to allow visualization and navigation of the protein networks. The applet was written using the open source