BACKGROUND: In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions. Qualitative labels are essential for the molecular biologist to experimentally verify predicted interactions. RESULTS: Of the individual protein-protein interaction prediction methods, some can predict physical interactions without producing other types of interactions. None of the methods can specifically predict metabolic interactions. We have constructed an 'omics evidence landscape' that combines all sources of evidence for protein interactions from various types of omics data for Saccharomyces cerevisiae. We explore this evidence landscape to identify areas with either only metabolic or only physical interactions, allowing us to specifically predict the nature of new interactions in these areas. We combine the datasets in ways that examine the whole evidence landscape and not only the highest scoring protein pairs in both datasets and find specific predictions. CONCLUSION: The combination of evidence types in the form of the evidence landscape allows for qualitative labels to be inferred and placed on the predicted protein interaction network of S. cerevisiae. These qualitative labels will help in the biological interpretation of gene networks and will direct experimental verification of the predicted interactions.

Information on protein-protein interactions is still mostly limited to a small number of model organisms, and originates from a wide variety of experimental and computational techniques. The database and online resource STRING generalizes access to protein interaction data, by integrating known and predicted interactions from a variety of sources. The underlying infrastructure includes a consistent body of completely sequenced genomes and exhaustive orthology classifications, based on which interaction evidence is transferred between organisms. Although primarily developed for protein interaction analysis, the resource has also been successfully applied to comparative genomics, phylogenetics and network studies, which are all facilitated by programmatic access to the database backend and the availability of compact download files. As of release 7, STRING has almost doubled to 373 distinct organisms, and contains more than 1.5 million proteins for which associations have been pre-computed. Novel features include AJAX-based web-navigation, inclusion of additional resources such as BioGRID, and detailed protein domain annotation. STRING is available at http://string.embl.de/

We have developed an automatable procedure for reconstructing the tree of life with branch lengths comparable across all three domains. The tree has its basis in a concatenation of 31 orthologs occurring in 191 species with sequenced genomes. It revealed interdomain discrepancies in taxonomic classification. Systematic detection and subsequent exclusion of products of horizontal gene transfer increased phylogenetic resolution, allowing us to confirm accepted relationships and resolve disputed and preliminary classifications. For example, we place the phylum Acidobacteria as a sister group of delta-Proteobacteria, support a Gram-positive origin of Bacteria, and suggest a thermophilic last universal common ancestor.

A full description of a protein's function requires knowledge of all partner proteins with which it specifically associates. From a functional perspective, 'association' can mean direct physical binding, but can also mean indirect interaction such as participation in the same metabolic pathway or cellular process. Currently, information about protein association is scattered over a wide variety of resources and model organisms. STRING aims to simplify access to this information by providing a comprehensive, yet quality-controlled collection of protein-protein associations for a large number of organisms. The associations are derived from high-throughput experimental data, from the mining of databases and literature, and from predictions based on genomic context analysis. STRING integrates and ranks these associations by benchmarking them against a common reference set, and presents evidence in a consistent and intuitive web interface. Importantly, the associations are extended beyond the organism in which they were originally described, by automatic transfer to orthologous protein pairs in other organisms, where applicable. STRING currently holds 730,000 proteins in 180 fully sequenced organisms, and is available at http://string.embl.de/.

In the genomics era, the interactions between proteins are at the center of attention. Genomic-context methods used to predict these interactions have been put on a quantitative basis, revealing that they are at least on an equal footing with genomics experimental data. A survey of experimentally confirmed predictions proves the applicability of these methods, and new concepts to predict protein interactions in eukaryotes have been described. Finally, the interaction networks that can be obtained by combining the predicted pair-wise interactions have enough internal structure to detect higher levels of organization, such as 'functional modules'.

In all genome-sequencing projects completed to date, a considerable number of 'gaps' have been found in the biochemical pathways of the respective species. In many instances, missing enzymes are displaced by analogs, functionally equivalent proteins that have evolved independently and lack sequence and structural similarity. Here we fill such gaps by analyzing anticorrelating occurrences of genes across species. Our approach, applied to the thiamin biosynthesis pathway comprising approximately 15 catalytic steps, predicts seven instances in which known enzymes have been displaced by analogous proteins. So far we have verified four predictions by genetic complementation, including three proteins for which there was no previous experimental evidence of a role in the thiamin biosynthesis pathway. For one hypothetical protein, biochemical characterization confirmed the predicted thiamin phosphate synthase (ThiE) activity. The results demonstrate the ability of our computational approach to predict specific functions without taking into account sequence similarity.

Functional links between proteins can often be inferred from genomic associations between the genes that encode them: groups of genes that are required for the same function tend to show similar species coverage, are often located in close proximity on the genome (in prokaryotes), and tend to be involved in gene-fusion events. The database STRING is a precomputed global resource for the exploration and analysis of these associations. Since the three types of evidence differ conceptually, and the number of predicted interactions is very large, it is essential to be able to assess and compare the significance of individual predictions. Thus, STRING contains a unique scoring-framework based on benchmarks of the different types of associations against a common reference set, integrated in a single confidence score per prediction. The graphical representation of the network of inferred, weighted protein interactions provides a high-level view of functional linkage, facilitating the analysis of modularity in biological processes. STRING is updated continuously, and currently contains 261 033 orthologs in 89 fully sequenced genomes. The database predicts functional interactions at an expected level of accuracy of at least 80% for more than half of the genes; it is online at http://www.bork.embl-heidelberg.de/STRING/.

With the increasing availability of genome sequences, new methods are being proposed that exploit information from complete genomes to classify species in a phylogeny. Here we present SHOT, a web server for the classification of genomes on the basis of shared gene content or the conservation of gene order that reflects the dominant, phylogenetic signal in these genomic properties. In general, the genome trees are consistent with classical gene-based phylogenies, although some interesting exceptions indicate massive horizontal gene transfer. SHOT is a useful tool for analysing the tree of life from a genomic point of view. It is available at http://www.Bork.EMBL-Heidelberg.de/SHOT.

Bifidobacteria are Gram-positive prokaryotes that naturally colonize the human gastrointestinal tract (GIT) and vagina. Although not numerically dominant in the complex intestinal microflora, they are considered as key commensals that promote a healthy GIT. We determined the 2.26-Mb genome sequence of an infant-derived strain of Bifidobacterium longum, and identified 1,730 possible coding sequences organized in a 60%-GC circular chromosome. Bioinformatic analysis revealed several physiological traits that could partially explain the successful adaptation of this bacteria to the colon. An unexpectedly large number of the predicted proteins appeared to be specialized for catabolism of a variety of oligosaccharides, some possibly released by rare or novel glycosyl hydrolases acting on "nondigestible" plant polymers or host-derived glycoproteins and glycoconjugates. This ability to scavenge from a large variety of nutrients likely contributes to the competitiveness and persistence of bifidobacteria in the colon. Many genes for oligosaccharide metabolism were found in self-regulated modules that appear to have arisen in part from gene duplication or horizontal acquisition. Complete pathways for all amino acids, nucleotides, and some key vitamins were identified; however, routes for Asp and Cys were atypical. More importantly, genome analysis provided insights into the reciprocal interactions of bifidobacteria with their hosts. We identified polypeptides that showed homology to most major proteins needed for production of glycoprotein-binding fimbriae, structures that could possibly be important for adhesion and persistence in the GIT. We also found a eukaryotic-type serine protease inhibitor (serpin) possibly involved in the reported immunomodulatory activity of bifidobacteria.

By combining the pairwise interactions between proteins, as predicted by the conserved co-occurrence of their genes in operons, we obtain protein interaction networks. Here we study the properties of such networks to identify functional modules: sets of proteins that together are involved in a biological process. The complete network contains 3,033 orthologous groups of proteins in 38 genomes. It consists of one giant component, containing 1,611 orthologous groups, and of 516 small disjointed clusters that, on average, contain only 2.7 orthologous groups. These small clusters have a homogeneous functional composition and thus represent functional modules in themselves. Analysis of the giant component reveals that it is a scale-free, small-world network with a high degree of local clustering (C = 0.6). It consists of locally highly connected subclusters that are connected to each other by linker proteins. The linker proteins tend to have multiple functions, or are involved in multiple processes and have an above average probability of being essential. By splitting up the giant component at these linker proteins, we identify 265 subclusters that tend to have a homogeneous functional composition. The rare functional inhomogeneities in our subclusters reflect the mixing of different types of (molecular) functions in a single cellular process, exemplified by subclusters containing both metabolic enzymes as well as the transcription factors that regulate them. Comparative genome analysis, thus, allows identification of a level of functional interaction between that of pairwise interactions, and of the complete genome.

In the course of evolution, genomes are shaped by processes like gene loss, gene duplication, horizontal gene transfer, and gene genesis (the de novo origin of genes). Here we reconstruct the gene content of ancestral Archaea and Proteobacteria and quantify the processes connecting them to their present day representatives based on the distribution of genes in completely sequenced genomes. We estimate that the ancestor of the Proteobacteria contained around 2500 genes, and the ancestor of the Archaea around 2050 genes. Although it is necessary to invoke horizontal gene transfer to explain the content of present day genomes, gene loss, gene genesis, and simple vertical inheritance are quantitatively the most dominant processes in shaping the genome. Together they result in a turnover of gene content such that even the lineage leading from the ancestor of the Proteobacteria to the relatively large genome of Escherichia coli has lost at least 950 genes. Gene loss, unlike the other processes, correlates fairly well with time. This clock-like behavior suggests that gene loss is under negative selection, while the processes that add genes are under positive selection.

We raise some issues in detecting the conservation (or absence thereof) of co-regulation using gene order; how we think the variations in the cellular network in various species can be studied; and how to determine and interpret the higher order structure in networks of functional relations.

Comprehensive protein protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions.

Comparisons of the gene order in closely related genomes reveal a major role for inversions in the genome shuffling process. In contrast to prokaryotes, where the inversions are predominantly large, half of the inversions between Saccharomyces cerevisiae and Candida albicans appear to be small, often encompassing only a single gene. Overall the genome rearrangement rate appears higher in eukaryotes than in prokaryotes, and the current genome data do not indicate that functional constraints on the co-expression of neighboring genes have a large role in conserving eukaryotic gene order. Nevertheless, qualitatively interesting examples of conservation of gene order in eukaryotes can be observed.

Much has been learned about the cellular pathology of Friedreich's ataxia, a recessive neurodegenerative disease resulting from insufficient expression of the mitochondrial protein frataxin. However, the biochemical function of frataxin has remained obscure, hampering attempts at therapeutic intervention. To predict functional interactions of frataxin with other proteins we investigated whether its gene specifically co-occurs with any other genes in sequenced genomes. In 56 available genomes we identified two genes with identical phylogenetic distributions to the frataxin/cyaY gene: hscA and hscB/JAC1. These genes have not only emerged in the same evolutionary lineage as the frataxin gene, they have also been lost at least twice with it, and they have been horizontally transferred with it in the evolution of the mitochondria. The proteins encoded by hscA and hscB, the chaperone HSP66 and the co-chaperone HSP20, have been shown to be required for the synthesis of 2Fe-2S clusters on ferredoxin in proteobacteria. JAC1, an ortholog of hscB, and SSQ1, a paralog of hscA, have been shown to be required for iron-sulfur cluster assembly in mitochondria of Saccharomyces cerevisiae. Combining data on the co-occurrence of genes in genomes with experimental and predicted cellular localization data of their proteins supports the hypothesis that frataxin is directly involved in iron-sulfur cluster protein assembly. They indicate that frataxin is specifically involved in the same sub-process as HSP20/Jac1p.

Different gene context methods are reviewed and summarized and an example of implementing several of these strategies into a publicly accessible web server is given.

Four years after the original sequence submission, we have re-annotated the genome of Mycoplasma pneumoniae to incorporate novel data. The total number of ORFss has been increased from 677 to 688 (10 new proteins were predicted in intergenic regions, two further were newly identified by mass spectrometry and one protein ORF was dismissed) and the number of RNAs from 39 to 42 genes. For 19 of the now 35 tRNAs and for six other functional RNAs the exact genome positions were re-annotated and two new tRNA(Leu) and a small 200 nt RNA were identified. Sixteen protein reading frames were extended and eight shortened. For each ORF a consistent annotation vocabulary has been introduced. Annotation reasoning, annotation categories and comparisons to other published data on M.pneumoniae functional assignments are given. Experimental evidence includes 2-dimensional gel electrophoresis in combination with mass spectrometry as well as gene expression data from this study. Compared to the original annotation, we increased the number of proteins with predicted functional features from 349 to 458. The increase includes 36 new predictions and 73 protein assignments confirmed by the published literature. Furthermore, there are 23 reductions and 30 additions with respect to the previous annotation. mRNA expression data support transcription of 184 of the functionally unassigned reading frames.

Various new methods have been proposed to predict functional interactions between proteins based on the genomic context of their genes. The types of genomic context that they use are Type I: the fusion of genes; Type II: the conservation of gene-order or co-occurrence of genes in potential operons; and Type III: the co-occurrence of genes across genomes (phylogenetic profiles). Here we compare these types for their coverage, their correlations with various types of functional interaction, and their overlap with homology-based function assignment. We apply the methods to Mycoplasma genitalium, the standard benchmarking genome in computational and experimental genomics. Quantitatively, conservation of gene order is the technique with the highest coverage, applying to 37% of the genes. By combining gene order conservation with gene fusion (6%), the co-occurrence of genes in operons in absence of gene order conservation (8%), and the co-occurrence of genes across genomes (11%), significant context information can be obtained for 50% of the genes (the categories overlap). Qualitatively, we observe that the functional interactions between genes are stronger as the requirements for physical neighborhood on the genome are more stringent, while the fraction of potential false positives decreases. Moreover, only in cases in which gene order is conserved in a substantial fraction of the genomes, in this case six out of twenty-five, does a single type of functional interaction (physical interaction) clearly dominate (>80%). In other cases, complementary function information from homology searches, which is available for most of the genes with significant genomic context, is essential to predict the type of interaction. Using a combination of genomic context and homology searches, new functional features can be predicted for 10% of M. genitalium genes.

Recently, a number of techniques have been proposed that use completely sequenced genomes for the function prediction of individual proteins encoded therein. They use the fusion of genes, their conserved location in operons or merely their co-occurrence in genomes to predict the existence of functional interactions between the proteins they encode. This type of information complements functional features that are predicted by classical homology-based search techniques.

Operons, co-transcribed and co-regulated contiguous sets of genes, are poorly conserved over short periods of evolutionary time. The gene order, gene content and regulatory mechanisms of operons can be very different, even in closely related species. Here, we present several lines of evidence which suggest that, although an operon and its individual genes and regulatory structures are rearranged when comparing the genomes of different species, this rearrangement is a conservative process. Genomic rearrangements invariably maintain individual genes in very specific functional and regulatory contexts. We call this conserved context an uber-operon.

The repeated occurrence of genes in each other's neighbourhood on genomes has been shown to indicate a functional association between the proteins they encode. Here we introduce STRING (search tool for recurring instances of neighbouring genes), a tool to retrieve and display the genes a query gene repeatedly occurs with in clusters on the genome. The tool performs iterative searches and visualises the results in their genomic context. By finding the genomically associated genes for a query, it delineates a set of potentially functionally associated genes. The usefulness of STRING is illustrated with an example that suggests a functional context for an RNA methylase with unknown specificity.

Comparative analysis of metabolic pathways in different genomes yields important information on their evolution, on pharmacological targets and on biotechnological applications. In this study on glycolysis, three alternative ways of comparing biochemical pathways are combined: (1) analysis and comparison of biochemical data, (2) pathway analysis based on the concept of elementary modes, and (3) a comparative genome analysis of 17 completely sequenced genomes. The analysis reveals a surprising plasticity of the glycolytic pathway. Isoenzymes in different species are identified and compared; deviations from the textbook standard are detailed. Several potential pharmacological targets and by-passes (such as the Entner-Doudoroff pathway) to glycolysis are examined and compared in the different species. Archaean, bacterial and parasite specific adaptations are identified and described.

Doolittle argued that to construct phylogenies, an organism should be regarded as either less or more than the sum of its genes (1). The argument is based on the observation that gene phylogenies are rarely consistent with one another because, among others, of lateral gene transfer (LGT). Creating phylogenies from sequence data in which an organism is described exactly as the sum of its genes is not, however, the only approach (2). Rather than creating phylogenies based on sequence identity for separate genes, this alternative creates a distance-based phylogeny at the genome level by comparing the fraction of genes shared between genomes (2). The resulting phylogeny (Fig. 1) of completely sequenced genomes (for an overview, see http://www.tigr.org/tdb/tdb.html) is remarkably similar to the phylogenies that are based on 16S ribosomal RNA (3). Not only is the trichotomy between Eukarya, Bacteria, and Archaea present, but within each of these taxa the clusters generally recognized as being monophyletic and for which multiple genomes are available, all have high bootstrap values (the Proteobacteria and their branching order, the Spirochaetales, the low (G+C) Gram-positive bacteria, and the Euryarchaeota). This method does not resolve the major branchings of the Bacteria, but neither do the phylogenies based on sequences that do not show LGT resolve this part of Bacterial phylogeny with a high degree of confidence.

Species phylogenies derived from comparisons of single genes are rarely consistent with each other, due to horizontal gene transfer, unrecognized paralogy and highly variable rates of evolution. The advent of completely sequenced genomes allows the construction of a phylogeny that is less sensitive to such inconsistencies and more representative of whole-genomes than are single-gene trees. Here, we present a distance-based phylogeny constructed on the basis of gene content, rather than on sequence identity, of 13 completely sequenced genomes of unicellular species. The similarity between two species is defined as the number of genes that they have in common divided by their total number of genes. In this type of phylogenetic analysis, evolutionary distance can be interpreted in terms of evolutionary events such as the acquisition and loss of genes, whereas the underlying properties (the gene content) can be interpreted in terms of function. As such, it takes a position intermediate to phylogenies based on single genes and phylogenies based on phenotypic characteristics. Although our comprehensive genome phylogeny is independent of phylogenies based on the level of sequence identity of individual genes, it correlates with the standard reference of prokarytic phylogeny based on sequence similarity of 16s rRNA. Thus, shared gene content between genomes is quantitatively determined by phylogeny, rather than by phenotype, and horizontal gene transfer has only a limited role in determining the gene content of genomes.

A systematic comparison of nine bacterial and archaeal genomes reveals a low level of gene-order (and operon architecture) conservation. Nevertheless, a number of gene pairs are conserved. The proteins encoded by conserved gene pairs appear to interact physically. This observation can therefore be used to predict functions of, and interactions between, prokaryotic gene products.