locus in a genome by a single protein (the longest iso- form). Thus, and because STRING aggregates data and predictions stemming from a wide spectrum of cell types and environmental conditions, it aims to represent the union of all possible protein–protein links. From this union, the actual network for any given spatio-temporal snapshot of the cell can in principle be deduced by projec- tion, for example by removing proteins known to be not expressed or not active under the conditions studied (17). In keeping with the above deﬁnitions, STRING imports protein association knowledge not only from databases of physical interactions, but also from databases of curated biological pathway knowledge. Apart form the resources already included in the previous release [MINT (10), HPRD (9), BIND (12), DIP (11), BioGRID (8), KEGG (13) and Reactome (14)], a number of resources have been newly included [IntAct (7), EcoCyc (15), NCI-Nature Pathway Interaction Database and Gene Ontology (GO) protein complexes]. For the full STRING release, this set of previously known and well-described interactions is then complemented by interactions that are predicted computationally, speciﬁcally for STRING, using a number of prediction algorithms (18,19). First, we con- duct systematic searches for genes that are found in close proximity within prokaryotic chromosomes, which is a good indicator for functional linkage. Second, we search for instances where genes have joined to encode a single fusion protein, which is indicative of functional linkage even in organisms where the two proteins have not fused. Third, we search for gene families that share above-random similarities in their evolutionary histories (i.e. they have similar ‘phylogenetic proﬁles’). This, again, predicts that they contribute to similar functional processes in the cell. Fourth, we conduct searches for genes that display a similar transcriptional response across a variety of conditions (co-expression). Individ- ually, the above predictors may not always have the speciﬁcity of direct experimental interaction assays; how- ever, when used in concert and integrated probabilisti- cally, the performance even of relatively weak predictors can rival that of experimental data (20). Lastly, two further sources of interactions in STRING are actually providing the majority of associations; these are text-mining and interaction transfer between organ- isms. For the former, we parse a large body of scientiﬁc texts [SGD (21), OMIM (22), The Interactive Fly, and all abstracts from PubMed]. We search for statistically relevant co-occurrences of gene names, and also extract a subset of semantically speciﬁed interactions using Natural Language Processing (23). For the transfer of interactions between organisms, we estimate whether a pair of interacting proteins found conserved in another organism justiﬁes the transfer of the interaction to that other organism (24). The transferred interactions, as well as all predicted or imported interactions, are benchmarked and scored against a common reference of functional part- nership [we currently use the joint membership of proteins in biological pathways, as annotated at KEGG (13), as our gold-standard]. Together, the above sources of interactions, includ- ing predictions and transfers, result in a uniquely high Nucleic Acids Research, 2009, Vol. 37, Database issue D413 coverage of the interaction networks stored in STRING (Figure 1), particularly for well-studied model organisms. Since the previous release, STRING has almost doubled the number of supported organisms, which now stands at 630. The number of stored interactions has increased as well, to a total of more than 50 million. Since the various subtypes of the interaction evidence are stored separately in the database, they can be disabled at will—giving users the ability to adjust the scope and speciﬁcity of STRING towards their particular application. EXTENDED DEFINITION OF CONSERVED GENOMIC NEIGHBORHOOD When working with prokaryotes, scientists have long used conserved genomic neighborhood arrangements of genes to infer functional linkage, assuming that such arrangements reﬂect polycistronic transcription units (operons). STRING has followed this principle, compiling and benchmarking protein–protein associations based on close, co-directional neighborhood of genes on the genome. As of version 8, this has been extended to cover also neighboring genes that are counter-directional in a head-to-head orientation (‘divergent transcription’). Such divergently oriented gene pairs have been shown to be indicative of functional linkage as well (25), albeit with somewhat lower conﬁdence. Often, one of the two genes is a transcriptional regulator, targeting the neighboring gene (25). STRING now uses this type of arrangement in its neighborhood algorithm as well (benchmarked sepa- rately, Figure 2). In addition, STRING is now more error tolerant when assembling conserved neighborhoods, ignoring short, partially overlapping genes on the anti- sense strand that are likely to be spurious predictions. INTEGRATION OF PROTEIN STRUCTURES For each update, STRING now parses all entries of the PDB database of protein structures (26). The use of pro- tein structures is two-fold: ﬁrst, to inform the user that a given protein—or a close homolog thereof—indeed has 3D structure information. In this case, a small preview of a representative structure is shown in the network, and the user can follow it to view the full structure and to proceed to the PDB website. Second, protein structures serve as interaction evidence themselves, when more than one distinct peptide chain is found in the structure. In this case, a stable and reliable protein–protein interaction is assumed. NEW PROGRAMMING INTERFACE To facilitate the integration of STRING into network tools like Cytoscape (27) and workﬂow engines like Taverna (28), we have created an application program- ming interface (API) that allows access to the interaction network in computer-readable formats (Figure 3). Addi- tionally, speciﬁc API functions allow retrieval of individ- ual records from our database, for example to map a protein via its name onto a STRING entry. We further