Interaction networks are built using proteinIDs as nodes (see sections ‘PIANA and protein identifiers’ and ‘Proteinprotein interactions integration’). When translating the nodes of the network to external protein identifiers (process referred as ‘unifying the network’), there are two possibilities: 1) one proteinID corresponds to a single external identifier and 2) different proteinIDs correspond to the same identifier, and thus, nodes and interactions are merged. Therefore, the same PIANA proteinID network will correspond to different unified networks, depending on the external identifier. Statistics in this article have been obtained after unifying the networks by NCBI geneID. Although geneIDs only cover 42% of proteinIDs, the cardinality proteinID:externalIdentifier is the highest (Table 1), and therefore geneID is the best suited identifier type for obtaining an unbiased view of the integrated protein interaction network. Protein sequences of unknown geneID were unified using UniProt accessions.

We used predictions of protein-protein interactions obtained by four different methods: (i) Gene fusion, in which two proteins are predicted to interact if their corresponding genes appear fused in another genome (Enright et al., 1999); (ii) Phylogenetic profiles, in which similarity of phylogenetic profiles is interpreted as being indicative of two proteins need to be simultaneously present to perform a given function together (Pellegrini et al., 1999); (iii) Distant conservation of sequence patterns and structure relationships, in which structural similarities among domains of known interacting proteins and conservation of pairs of sequence patches involved in protein–protein interfaces are used to predict putative protein interaction pairs (Espadaler et al., 2005b); and (iv) Structural interologs, in which interactions are transferred between proteins with the same structural domains (Aragues et al., 2006). Interactions for the two first methods were retrieved from STRING (von Mering et al., 2007) by querying the database for interactions with a score higher than 0.7 for that particular methodology. Interactions for (iii) were obtained from the work of Espadaler et al. (Espadaler et al., 2005b). Interactions for (iv) were predicted by transferring experimental interactions in PIANA between proteins with a domain within the same SCOP family.

PIANA (Protein Interactions And Network Analysis) (Aragues et al., 2006) is a software framework capable of (i) integrating multiple sources of information into a single relational database (see database design on additional file 1); (ii) creating and analyzing protein interaction networks; and (iii) mapping multiple types of biological data onto protein interaction networks. PIANA code and documentation are freely available under an open source license for local installation and modification http://sbi.imim.es/piana. The data warehousing approach and software architecture of PIANA are shown in Figure 1 (see additional file X for details). The PIANA database is accessed by the Graph library through a database interface, which is also used by the PIANA library to create, manage and analyze proteinprotein interaction networks. The whole process can be controlled from a user interface module.

PIANA handles an extensive set of protein identifiers types: UniProt entries and accessions; gene symbols; NCBI gi, geneID, Unigene and accession numbers; ENSEMBL; RefSeq; PDB; and FastA formatted sequences. PIANA internally identifies proteins with proteinIDs (integers). Each proteinID is linked to a pair [aminoacid sequence, taxonomy id], so there is a unique identifier for each protein sequence for a given organism. This allows PIANA to use the <sequence, species> of the protein as an inter-lingua between the external identifiers provided by the main repositories of genes and proteins. Therefore, one external protein identifier (e.g. UniProt entry THRB_HUMAN) can be associated to one or more proteinIDs (e.g. 11483), which are in turn linked to other external identifiers that are also used to represent that protein (e.g., gene symbol ‘f2’ and Unigene‘Hs.410092’). Consequently, along the different processes involved in inputting/outputting PIANA, external identifiers are ‘translated’ to proteinIDs, the desired operations are performed, and finally, if needed, proteinIDs are returned into the external identifier expected by the user (Figure 2). This strategy reduces the ambiguity and processing problems to the minimum: there is no need for continuously translating between distinct types of protein identifiers, since all information has been previously stored by assigning it to specific proteinIDs. Furthermore, codifying interactions in terms of proteinIDs allows PIANA to capture a larger number of interactions than platforms based on third party protein identifiers.

Moreover, PIANA uses a number of techniques to assure the quality and completeness of the identifiers used as input/output: 1) inferring correspondences between identifiers and sequences even in the case that no external database explicitly contained the cross-reference: if one database identifies sequence A with identifier id1 and another database uses identifier id2 to sequence A, PIANA infers that id1 is equivalent to id2; 2) uniqueness of output protein identifiers: if two proteinIDs are linked to the same external identifier, those proteins are considered to be the same, and hence, merged into a single network node; 3) avoiding gene name ambiguities: thanks to integrating the species of the protein into the internal identifier, gene names are not confounded even if the same symbol is used for several species; and 4) using representative protein identifiers: (i) PIANA will use the identifier labeled as ‘preferred’ by the source database (eg. official gene symbol) unless the user says the contrary; and (ii) any input identifiers given by the user are prioritized over other identifiers in the PIANA database.

Since PIANA works internally with identifiers linked to the sequence of proteins (i.e. proteinID), the output identifier that is used for proteins depends not only on the type of identifier chosen by the user (e.g. UniProt) but also on the specific results that are being outputted. The reason is that one proteinID can be associated to several external identifiers (i.e. one sequence is associated to three gene names) and consequently, one of those external identifiers has to be chosen above the others. The algorithm used to chose among external identifiers depends on the input identifiers given by the user (they are prioritized over other identifiers) and the number of external databases that linked that sequence to the identifiers. Therefore, one proteinID will not always be represented in the output by the same external identifier.

Our internal protein identifiers do not distinguish between identical paralogs. We believe this distinction is not needed, since most repositories of interactions do not reach that level of specificity. Finally, proteinIDs are not intended to be new external protein identifiers, their only purpose is to be used for integration. Therefore, the way the integration is performed remains transparent to the user, whose only concern is to decide on the type of identifiers for input and output.

Sequence and taxonomy data was obtained from (The Uniprot Consortium, 2007), NCBI GenBank (Benson et al., 2007) and NCBI Blast nr (Maglott et al., 2007) databases (see additional file 2 for the complete list of protein sequence repositories used). Unexpectedly, UniProt Swiss-Prot (i.e. curated sequences) and UniProt TrEMBL (i.e. predicted sequences) have a significant overlap (additional file 3). Moreover, the overlap between TrEMBL and GenBank is lower than anticipated. Cross-references between external identifiers and proteinIDs were obtained from multiple thirdparty repositories (see additional file 2). Table 1 shows the coverage provided by the main protein external identifiers for all proteinIDs (i.e. pair [protein sequence, taxonomy]) in the PIANA database.

Each interaction described in a third-party database is‘translated’ to one or more interactions between proteinIDs. For example, if the external database contains an interaction between proteins A and B, with A corresponding to two proteinIDs (e.g 1 and 2) and B to one proteinID (e.g. 3), two interactions (1-3 and 2-3) will be inserted into the PIANA database. Both interactions will be described in the PIANA database as coming from that specific external database and labeled with the method used to detect the interaction between A and B. For example, HPRD describes an interaction between Entrez Gene 217 (mitochondrial ALDH) and Entrez Gene 3336 (heat shock protein). According to the correspondences in the PIANA database, Entrez Gene 217 corresponds to 13 different proteinIDs, and Entrez Gene 3336 corresponds to 12 proteinIDs. Therefore, PIANA will internally store the interaction between those two proteins as 156 different interactions. This methodology allows PIANA to give full control to the user: 1) interactions can be retrieved from any type of identifier; 2) a network can be created for a given external database (e.g. use only interactions from IntAct) and/or a specific method (e.g. do not use interactions detected in two hybrids assays) and/or a species (e.g. only interested in human interactions); 3) PIANA outputs can be set to use any type of protein identifier and therefore, interactions between proteinIDs are transformed to non-redundant interactions between protein identifiers (Methods). Consequently, describing interactions in terms of protein sequences instead of external identifiers provides a true integration of all known interactions into a single network, while keeping record of the source databases and detection methods associated with the interaction. Currently, PIANA can integrate interactions from DIP (Salwinski et al., 2004), MIPS (Pagel et al., 2005), MINT (Chatr-aryamontri et al., 2007), IntAct (Kerrien et al., 2007), BIND (Alfarano et al., 2005), BioGrid (Stark et al., 2006), HPRD (Peri et al., 2003), STRING(von Mering et al., 2007), interactions predicted by distant conservation of sequence patterns and structure relationships (Espadaler et al., 2005b), interactions transferred between proteins based on orthology (Yu et al., 2004) and, in general, any interaction data that is in tabulated or PSI-MI (Hermjakob, 2006) formats. See additional file 2 for the detailed description of interaction repositories that have been used in this work. Furthermore, data does not to have to be integrated indiscriminately without differentiating high-throughput versus small-scale experiments and literature annotation. Therefore, PIANA allows users to define subsets of interactions based on the source repository and detection methods employed. For example, a subset of reliable interactions can be extracted by requiring them to be in at least two different repositories.

The experimental interactions in the PIANA integrated database have been obtained from 7 different repositories, belong to 736 different species, and were detected using 106 different experimental methods. As shown on Table 2, the species with the largest number of experimental interactions are yeast (111,535 interactions) and human (110,457 interactions). Most interactions were found in just one database and were detected by just one method (Figure 3). The high correlation between the number of methods and databases is explained by the fact that most interactions appear in just one external repository, and these repositories usually label interactions with a single detection method. We calculated the overlap between 7 repositories with experimental information in terms of interactions (Table 3A) and proteins (Table 3B). BioGrid (Stark et al., 2006) is the repository with the highest number of interactions (216,370) and with the highest number of unique interactions (163,700). The two repositories that show the greatest overlap are MINT and IntAct (61% of interactions and 82% of proteins in MINT are also in IntAct) while the lowest overlap was between HPRD and DIP (only 4% of interactions and 9% of proteins in HPRD are also in DIP). Most low overlaps in terms of interactions are explained by the low overlap in terms of proteins. Therefore, data integration is required in order to obtain an interaction network that covers most proteins and interactions.

We were interested in analyzing the distribution of interactions in terms of the detection method employed. We examined the overlaps between different detection methods in terms of interactions (Table 4A) and proteins (Table 4B). We observed that high-throughput methods account for most of the known interactions (126,136 for affinity methods and 103,334 for yeast two hybrid assays). The overlap between the interactions detected by the different methods is low, even in cases where the overlap at the protein level is high. For example, while 51% of proteins with interactions from affinity methods also had interactions detected by yeast twohybrid methods, only 9% of interactions from yeast twohybrid were also detected by affinity methods. Therefore, in order to maximize the number of known interactions for a protein, multiple experimental detection methods should be employed.

Well-documented observations about protein interaction networks are confirmed when analyzing the integrated experimental interaction networks of different species. Moreover, the integrated network shows the modular functional organization of the proteome reported by previous works (Gavin et al., 2006). In particular, proteins tend to interact with proteins of the same Gene Ontology (GO) (Harris et al., 2004) biological process (Table 5). Furthermore, 95% of the interacting proteins in the integrated network have the same cellular component according to GO. In addition, the following properties were observed for the yeast protein interaction network (Table 6): (i) yeast hubs (proteins with 5 or more interactions) are more likely to be essential (Giaever et al., 2002) than non-hubs (22% of hubs are essential versus only 5% of non-hubs), although this might be a reflection of hubs usually having multiple interfaces (Kim et al., 2006); (ii) approximately 59% of the interactions have the same cell localization according to (Lee et al., 2002); (iii) approximately 60% of the interactions reported are found coexpressed during the yeast cell cycle according to (Cho et al., 1998).

Recently, it has been shown that the number of common interaction partners between two proteins can be used to annotate proteins (Brun et al., 2003; Samanta et al., 2003). We have studied the use of this heuristic to predict molecular functions and biological processes as defined by GO (Harris et al., 2004), by calculating the percentage of shared GO terms between proteins with common interaction partners (Figure 4). As expected, we observe that the interactions of a protein in the integrated network can be used to predict its function and the biological processes in which it intervenes. For example, proteins with 10-20 interaction partners in common share 90% of their GO biological process terms. Moreover, the accuracy of the predictions based on the integrated network is similar to that obtained when solely using the subset of interactions from DIP (Salwinski et al., 2004), while the number of annotated proteins is much higher (additional file 4).

We were interested in assessing protein interaction predictions and evaluating the similarities between the predicted interaction network and the experimental interaction network. In particular, we studied 4 different types of predictions (Methods): (i) Gene fusion events (Enright et al., 1999) as predicted by STRING(von Mering et al., 2007); (ii) Phylogenetic profiles (Pellegrini et al., 1999) as predicted by STRING (von Mering et al., 2007); (iii) Distant conservation of sequence patterns and structure relationships as described by Espadeler et al. (Espadaler et al., 2005b); and (iv) Structural interologs predicted by PIANA (Aragues et al., 2006). We calculated the overlap between the different experimental and prediction methods in terms of interactions (Table 7A) and proteins (Table 7B), observing a high overlap between prediction methods based on genomes analyses (i.e. gene fusion events and phylogenetic profiles) and a very low overlap between all other prediction methods. This minimal overlap between interaction predictions is explained by the different types of input data used by each method and the type of proteins for which the methods are capable of predicting interactions. For example, the method based on structural interologs predicts interactions for proteins with known 3D structure, while STRING predictions from gene fusion events were mainly applied to prokaryotes. Most proteins with known 3D structure are eukaryotes (Berman et al., 2000), and therefore, the two methods rarely predict similar interactions. Moreover, there is low overlap between predicted interactions and those obtained by experimental high throughput methods, both in terms of interactions and proteins. These results indicate that different methods identify interactions for different proteins. For example, there are many species for which no yeast two-hybrid experiments have been carried out, while many predictions can be ‘transferred’ to those species on the basis of genomes analysis, resulting in a low overlap at the interaction and protein level between the two methods.

We evaluated whether interacting proteins according to different prediction methods tended to share biological process, molecular function and cellular component according to GO (Table 8). We observed that the method that better captures functional relationships between proteins is the one based on gene fusion events (Methods): 85% of the predicted interacting pairs belong to the same biological process. Moreover, all prediction methods detected a sensible number of colocalized proteins. For example, 87% of interacting proteins according to the prediction method based on structural interologs had the same cellular location.