Ulysses - an application for the projection of molecular interactions across species
© Kemmer et al.; licensee BioMed Central Ltd. 2005
Received: 23 February 2005
Accepted: 8 November 2005
Published: 2 December 2005
We developed Ulysses as a user-oriented system that uses a process called Interolog Analysis for the parallel analysis and display of protein interactions detected in various species. Ulysses was designed to perform such Interolog Analysis by the projection of model organism interaction data onto homologous human proteins, and thus serves as an accelerator for the analysis of uncharacterized human proteins. The relevance of projections was assessed and validated against published reference collections. All source code is freely available, and the Ulysses system can be accessed via a web interface http://www.cisreg.ca/ulysses.
The catalogue of human protein-encoding genes is largely enumerated , but the task of discerning the functions of these genes remain a formidable challenge. A significant fraction of protein-encoding genes are entirely novel; the cellular roles of the proteins remain a mystery. As model organism genome sequences have been available for several years, a modest compendium of functional genomics data has emerged for these organisms. To capitalize on these data for the functional annotation of human genes, one can project model organism gene properties onto homologous human genes . Although the properties of homologous genes are often predicted based on recorded annotations of genes with similar sequences, such mappings only begin to capitalize on available data.
The increasing body of genomics data allows functions to be predicted using 'Guilt by Association' (GBA) methods. In GBA, the function of a gene is inferred from the functions of genes with which it interacts (for example, protein contact) or parallels (for example, co-expression). Observation of mutually consistent interactions in multiple species improves the predictive performance of GBA methods, a process named Interolog Analysis [2, 3]. Early demonstrations of the utility of Interolog Analysis, although limited to the analysis of model organism data, offer promise for the accelerated annotation of human genes.
Prediction of human gene function based on Interolog Analysis requires an underlying set of bioinformatics resources and algorithms to make unified data accessible to the community. First, functional genomics data must be accessible through reference databases. Second, the relationships between homologous genes must be mapped by a suitable comparison procedure. Finally, the relationships must be rendered accessible to the broad community through an intuitive interface. A system incorporating these three components would be a powerful tool for laboratory investigators seeking to capitalize on existing genomics data.
Despite substantial success in sequencing genomes, large-scale functional studies have been reported for only a few common model organisms. Key reports have addressed protein-protein interactions in Saccharomyces cerevisiae [4–6], Drosophila melanogaster [7–9], and Caenorhabditis elegans . In addition to these screens, functional studies have linked genes by tackling such topics as: patterns of co-expression , genetic interactions , and sub-cellular co-localization . The diverse data from the functional studies have been rendered publicly accessible in species-specific repositories [14–16]. Large databases that have emerged to consolidate the diverse functional genomics data include leading examples like the Biomolecular Interaction Network Database (BIND) , DIP , and MINT .
To manage the combination of interaction data and genome annotation, data warehouses have emerged such as EnsMart , SeqHound , and Atlas . All three examples store heterogeneous biological data in a relational schema, allowing for rapid retrieval using Structured Query Language (SQL) via an integrated application programming interface (API), or via a web graphical user interface.
In order to draw conclusions about human genes from model organism data, it is essential to possess a map enumerating gene homology relationships among species. The fundamental assumption is that direct gene orthologs (genes separated only by speciation) typically occupy the same functional niche . Leading systems such as COGs [24, 25] and Inparanoid  continue to unravel the complex evolutionary relationships between genes. As shown by these efforts, the stringent demands for orthology mapping are challenging, so it is often more feasible to group homologs. The National Center for Biotechnology Information's (NCBI) HomoloGene  provides such a high-throughput map suitable for incorporation into larger analyses that address many organisms. The establishment of evolutionary relationships between genes remains a topic of active investigation.
Biological interpretation of integrated data is greatly aided by tools for visualization of properties. Multiple platforms for the visualization and manipulation of protein interaction networks [28–32] provide users with interfaces to complex interaction data. Interolog Analysis has emerged as a powerful means to predict the function of genes [2, 33–36]. Existing Interolog Analysis tools, like the Interolog database  and STRING , convey information about protein associations across species using databases, homology maps, and simple visualization methods. These visualization tools, however, are restricted to single views that fail to convey the evidence from each species.
Model organism data to predict human protein interactions
The available pool of curated annotations of protein-protein interactions in reference databases is sparse, only a small subset of the interactome (the complete collection of all functionally relevant protein-protein interactions) is present. The Human Protein Reference Database (HPRD)  is the largest curated collection of documented human protein interactions. To assess the relevance of observed interactions between model organism proteins for the prediction of human interactions, we determined the overlap between protein interactions in the HPRD reference dataset and homologous interactions from model organisms represented in BIND . Reflecting the sparse coverage of the interactome, only 80 such interactions were found. The sparse coverage of bona fide protein-protein interactions is problematic to evaluating the performance of predictive methods. Previous studies have assessed the quality of interaction data on the basis of protein interactor pairs sharing the same annotated GO-terms [33, 35, 40, 41] or pathway assignments . While such measures are often supportive of the predictive performance of methods, we believe such criteria suffer from a focus on the strongest and most easily observed interactions.
Yeast protein interactions reported in BIND confirmed by co-localization
Independently confirmed interactions
Composition of localization bins
Data resources for performance evaluation
Randomly generated human (HPRD)
Model organisms (BIND)
Pairs mapped to cellular compartments
Cross-classification of interaction and localization - single projections
Cross-classification of interaction and localization - double projections
Yeast two-hybrid/complex purification
Negative control data
Because a curated reference collection of non-interacting human proteins is lacking and because pairs of proteins residing in different sub-cellular compartments are less likely to interact , we assessed the noise in the interaction data by the frequency with which HomoloGene interactors were annotated with incompatible localizations. We evaluated proteins localizing to the nucleus, the cytoplasm, and the extra-cellular space. We considered all model organism protein interactions for which both interactors mapped to a HomoloGene containing a human protein with annotated localization in the HPRD database. We found that 'true' interactions, that is, interactions between two model organism proteins annotated with the same compartment, accounted for 91% and inconsistencies were observed in 9% of the cases. As proteins can exist in different compartments at different times, and the curated HPRD annotations are restricted to the available literature, the inconsistencies should be viewed as an upper-bound of the false classification rate. It is noteworthy that there were no inconsistencies for the double linkage interactions.
Network expansion and detection (multi-protein interactions)
KEGG  and PINdb  are curated annotation databases describing biological pathways and complexes. To demonstrate the capacity of Ulysses to detect new components of these known pathways and complexes, we identified candidates based on the following double linkage criteria: the candidate interacted with two or more pathway members in one organism; or the candidate interacted with homologous proteins of pathway members in two or more species. Based on these criteria and after mapping all pathway and complex components to HomoloGene, 14 HomoloGenes were newly associated with 11 pathways and complexes previously described in KEGG and PINdb (Additional data file 1). Several of these candidates have been previously linked to the pathways or processes in the scientific literature, but have not yet been annotated as such in the reference databases.
Human protein interaction predictions supported by redundant observations for homologous proteins in model organisms
HomoloGene ID 1
Gene symbol 1
HomoloGene ID 2
Gene symbol 2
Grouping of overlaps in these high confidence interactions revealed previously characterized networks, including highly conserved pathways and complexes.
We recovered elements of the spliceosome, including seven core small nuclear ribonucleoprotein particle (snRNP) components (LSM1, 2, 4, 5, 7, 8, SNRPD2), four U2 and U3 snRNP-specific proteins (SF3A3, IMP3, IMP4, MPHOSPH10), a splicing factor (PRPF19), as well as a protein usually associated with the PRPF19 complex (CRNKL1) known to interact with the spliceosome .
Two clusters were observed composed of proteins required for DNA replication and repair, as well as replication-dependent structural proteins. One cluster contained all five subunits (RFC1, 2, 3, 4, 5) of an accessory factor for DNA replication, replication factor C (RF-C). The other cluster contained four nucleosomal proteins, three members of the H2A histone family (H2AFE, H2AFJ, H2AFN), which were all connected to the nucleosome assembly protein 1-like 1 (NAP1L1).
We also identified a network of 19 interconnected proteasome subunits. We found five core alpha (PSMA1, 2, 3, 5, 7) and four core beta subunits (PSMB3, 4, 5, 7) from the 20S proteasome, as well as nine subunits from the 19S regulatory complex. We located the proteasome regulatory particle subunit PSMD6 interacting with PSMD3, a non-ATPase subunit of the 19S regulatory complex.
These examples of functional networks among protein members of well conserved cellular complexes and pathways validate our approach to detect biologically meaningful protein interactions in human by overlaying and projecting interaction data originating from diverse model organisms.
To date, the limiting factor for network discovery is the sparse protein interaction data. As more association data are generated for the core model organisms, the Ulysses Interolog analysis system will facilitate greater inference of network members.
Ulysses web interface for analysis and visualization of networks
Utility and comparison to other systems
Here we described an exploratory Interolog Analysis framework for the inference of protein function. We demonstrate, by overlaying protein interaction data sets, dramatic improvements in the specificity of projected 'dual-linkage' interactions compared to those based on a single study. Through a novel interface, we provide a means for the broad community of researchers to use Interolog Analysis for the directed study of specific pathways or processes.
Ulysses represents a significant advance in the graphical display of protein interaction data for comparative genomics. Visualization tools for the study of protein and genetic networks have been available for many years, including Cytoscape , Osprey , and ProViz . These useful tools have enabled researchers to display networks for a single species or data set. Each of these tools requires submission of a pre-computed table of results, whereas Ulysses both performs the data analysis and renders a visual display. To our knowledge, only two software tools provide interfaces for comparative analysis of protein interactions (Interolog Analysis). POINT  displays pairwise network diagrams; however, positions of homologous proteins are not preserved between panes, making visual interpretation exceedingly difficult. The mature STRING system  features an excellent underlying data collection. The STRING visual interface for comparative analysis, however, is restricted to a composite plot - there is no parallel display for individual species. Although the underlying data in STRING is robust, only the most advanced users of the system can extract the information provided intuitively in the Ulysses interface. Thus Ulysses is unique in its capacity for parallel display of interaction data from multiple species for comparative analysis and biological interpretation.
A limiting factor for inference of new protein clusters and extension of known clusters is the sparse existing coverage of interactions in genomics data. Even though proteome-scale analyses have been conducted for several organisms [4, 7, 10], the lack of overlapping interactions limits the impact of the analysis of interactions shared by homologs. In this study, we found that interactions observed in multiple studies (for homologous proteins) are highly reliable (Table 5). As more extensively overlapping interaction data sets emerge, Interolog Analysis will allow for expanded functional annotation of human genes. Individual uncharacterized genes will be linked to known cellular pathways and complexes, and we anticipate the discovery of new functional units. To this end, we strongly encourage protein interaction screens of additional organisms and deeper coverage of the primary model organisms, as the depth of data is critical to increasing the utility of Interolog Analysis.
The homology mapping obtained from HomoloGene was convenient for the Ulysses system. Because homology mapping across organisms remains an issue of debate, however, future releases of Ulysses will offer an option to choose between different resources, possibly including well established systems [24, 26, 27].
Even though the small size of the present body of functional genomics data does not allow for extended de novo discovery of cellular networks, detection of known complexes and pathways demonstrate Ulysses' capacity to successfully identify biological networks. Ulysses is available without restriction as an internet-based resource or as downloadable code for developers . The novel interface partitions data into discrete planes, offering an intuitive means of performing Interolog Analysis.
Materials and methods
All data were stored within the Atlas database system [22, 50]. The Atlas data warehouse provides a framework for integrating data from diverse systems within a unified environment. All data sets were imported from indicated databases using the SQL interface or Java API. All software and scripts used to extract data from the Atlas system are available by request.
Model organism protein interaction datasets
BIND - Y2H
BIND - complex
HomoloGene  is an NCBI resource providing computationally identified homologs to human protein reference sequences derived from the RefSeq collection . We used data from HomoloGene freeze July 2004, which included 26,797 HomoloGene groups and 108,734 unique genes. The HomoloGene dataset was seeded by a non-redundant human RefSeq protein sequence collection and compared using protein-protein BLAST  to RefSeq protein sequences from model organisms. After mapping the protein sequences back to their respective genomes, both distance (Ka/Ks ratios ) and synteny were assessed to identify false pairings.
Ortholog mapping for model organisms
For proteins from each of the three included model organisms (worm, fly, and yeast), unique GenBank protein geninfo (gi) numbers were extracted from BIND. These identifiers were mapped to corresponding identifiers in the RefSeq collection and the RefSeq IDs were used to select homology sets in HomoloGene. For BIND sequences without a mapping to a RefSeq sequence, BLAST analysis was performed against a database of all RefSeq sequences represented in the HomoloGene system. Parameters were set to an e-value cutoff of 10-20, and sequences were only included in the set if the matching portion included the entirety (100%) of the query sequence. At the time of publication, homology mappings through HomoloGene were updated as of September 2005.
Reference data sets and evaluation criteria
The HPRD is a collection of hand-curated reports on human proteins extracted from the scientific literature . The HPRD collection (HPRD freeze July 2004: 13,469 proteins, 26,893 protein interactions) was uploaded into the Atlas database, and protein identifiers were mapped to corresponding HomoloGene and RefSeq identifiers. The HPRD annotations include reported sub-cellular locations for each protein.
Interaction data set from model organisms
A total of 32,930 binary and protein complex interactions were obtained from BIND for which both interactors had been successfully mapped to HomoloGene homology groups. These interactions constitute the observed data and were assessed relative to the HPRD reference set.
Sampling from HPRD
We generated 60,000 random pairings of all interactors (proteins) present in HPRD bearing a localization label. After eliminating redundancy, we used this set to determine the sub-cellular co-localization. Statistical significance was evaluated using the Fisher exact test.
Visualization and web interface
The Ulysses visualization system dynamically generates images for display in a web browser. The visualization problem was divided into two tasks: graph network layout and image rendering. The open source JUNG (Java Universal Network/Graph) Framework  was used for modeling the network structure, based on interaction data extracted from the Atlas database via the Atlas API. Image rendering and web page generation were performed by a Java framework composed of the following components: JavaServer Pages (JSPs), standard Java libraries included with J2SE 1.5.0 , and the Java Advanced Imaging (JAI) libraries . JSPs were used to unite the various components. The visualization application is deployed using the Tomcat web application server . The network layout is defined using all reported HomoloGene sets in all organisms, and the species-specific images are constructed by limiting the display to proteins participating in interactions within the species. This process allows for the positions of homologous genes to be maintained across species.
Additional data files
The following additional data are available with the online version of this paper. Additional data file 1 is a table showing new HomoloGene associations with known pathways and complexes described in KEGG and PINdb. Additional data file 2 lists the human protein interaction predictions supported by redundant observations for homologous proteins in model organisms.
The authors would like to thank Dr Christer Höög for insightful discussions. B.F.F.O. acknowledges the University of British Columbia for support of this project. W.W.W. acknowledges support from the Canadian Institutes of Health Research and the Michael Smith Foundation for Health Research. This work was supported by funding from Merck (to the Centre for Molecular Medicine and Therapeutics) and the Pfizer Corporation (D.K.). J.B. is supported by a predoctoral scholarship from the Canadian Institutes of Health Research. We thank Stefanie Butland for critical reviews of this manuscript, and Miroslav Hatas and Jonathan Falkowski for systems and software installation, and continuing maintenance of the Ulysses server.
- Southan C: Has the yo-yo stopped? An assessment of human protein-coding gene number. Proteomics. 2004, 4: 1712-1726. 10.1002/pmic.200300700.PubMedView ArticleGoogle Scholar
- Matthews LR, Vaglio P, Reboul J, Ge H, Davis BP, Garrels J, Vincent S, Vidal M: Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or "interologs". Genome Res. 2001, 11: 2120-2126. 10.1101/gr.205301.PubMedPubMed CentralView ArticleGoogle Scholar
- Yu H, Luscombe NM, Lu HX, Zhu X, Xia Y, Han JD, Bertin N, Chung S, Vidal M, Gerstein M: Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. Genome Res. 2004, 14: 1107-1118. 10.1101/gr.1774904.PubMedPubMed CentralView ArticleGoogle Scholar
- Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, et al: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature. 2000, 403: 623-627. 10.1038/35001009.PubMedView ArticleGoogle Scholar
- Gavin AC, Bosche M, Krause R, Grandi P, Marzioch M, Bauer A, Schultz J, Rick JM, Michon AM, Cruciat CM, et al: Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 2002, 415: 141-147. 10.1038/415141a.PubMedView ArticleGoogle Scholar
- Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams SL, Millar A, Taylor P, Bennett K, Boutilier K, et al: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature. 2002, 415: 180-183. 10.1038/415180a.PubMedView ArticleGoogle Scholar
- Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao YL, Ooi CE, Godwin B, Vitols E, et al: A protein interaction map of Drosophila melanogaster. Science. 2003, 302: 1727-1736. 10.1126/science.1090289.PubMedView ArticleGoogle Scholar
- Formstecher E, Aresta S, Collura V, Hamburger A, Meil A, Trehin A, Reverdy C, Betin V, Maire S, Brun C, et al: Protein interaction mapping: a Drosophila case study. Genome Res. 2005, 15: 376-384. 10.1101/gr.2659105.PubMedPubMed CentralView ArticleGoogle Scholar
- Stanyon CA, Liu G, Mangiola BA, Patel N, Giot L, Kuang B, Zhang H, Zhong J, Finley RL: A Drosophila protein-interaction map centered on cell-cycle regulators. Genome Biol. 2004, 5: R96-10.1186/gb-2004-5-12-r96.PubMedPubMed CentralView ArticleGoogle Scholar
- Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, Vidalain PO, Han JD, Chesneau A, Hao T, et al: A map of the interactome network of the metazoan C. elegans. Science. 2004, 303: 540-543. 10.1126/science.1091403.PubMedPubMed CentralView ArticleGoogle Scholar
- Stuart JM, Segal E, Koller D, Kim SK: A gene coexpression network for global discovery of conserved genetic modules. Science. 2003, 21: 21-Google Scholar
- Tong AH, Lesage G, Bader GD, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M, et al: Global mapping of the yeast genetic interaction network. Science. 2004, 303: 808-813. 10.1126/science.1091317.PubMedView ArticleGoogle Scholar
- Huh WK, Falvo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, O'Shea EK: Global analysis of protein localization in budding yeast. Nature. 2003, 425: 686-691. 10.1038/nature02026.PubMedView ArticleGoogle Scholar
- The FlyBase database of the Drosophila genome projects and community literature. Nucleic Acids Res. 2003, 31: 172-175. 10.1093/nar/gkg094.Google Scholar
- Harris TW, Chen N, Cunningham F, Tello-Ruiz M, Antoshechkin I, Bastiani C, Bieri T, Blasiar D, Bradnam K, Chan J, et al: WormBase: a multi-species resource for nematode biology and genomics. Nucleic Acids Res. 2004, D411-417. 10.1093/nar/gkh066.Google Scholar
- Christie KR, Weng S, Balakrishnan R, Costanzo MC, Dolinski K, Dwight SS, Engel SR, Feierbach B, Fisk DG, Hirschman JE, et al: Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. Nucleic Acids Res. 2004, D311-314. 10.1093/nar/gkh033.Google Scholar
- Alfarano C, Andrade CE, Anthony K, Bahroos N, Bajec M, Bantoft K, Betel D, Bobechko B, Boutilier K, Burgess E, et al: The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res. 2005, D418-424.Google Scholar
- Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D: DIP: the database of interacting proteins. Nucleic Acids Res. 2000, 28: 289-291. 10.1093/nar/28.1.289.PubMedPubMed CentralView ArticleGoogle Scholar
- Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G: MINT: a Molecular INTeraction database. FEBS Lett. 2002, 513: 135-140. 10.1016/S0014-5793(01)03293-8.PubMedView ArticleGoogle Scholar
- Kasprzyk A, Keefe D, Smedley D, London D, Spooner W, Melsopp C, Hammond M, Rocca-Serra P, Cox T, Birney E: EnsMart: a generic system for fast and flexible access to biological data. Genome Res. 2004, 14: 160-169. 10.1101/gr.1645104.PubMedPubMed CentralView ArticleGoogle Scholar
- Michalickova K, Bader GD, Dumontier M, Lieu H, Betel D, Isserlin R, Hogue CW: SeqHound: biological sequence and structure database as a platform for bioinformatics research. BMC Bioinformatics. 2002, 3: 32-10.1186/1471-2105-3-32.PubMedPubMed CentralView ArticleGoogle Scholar
- Shah SP, Huang Y, Xu T, Yuen MMS, Ling J, Ouellette BFF: Atlas - A data warehouse for integrative bioinformatics. BMC Bioinformatics. 2005, 6: 34-10.1186/1471-2105-6-34.PubMedPubMed CentralView ArticleGoogle Scholar
- Gabaldon T, Huynen MA: Prediction of protein function and pathways in the genome era. Cell Mol Life Sci. 2004, 61: 930-944. 10.1007/s00018-003-3387-y.PubMedView ArticleGoogle Scholar
- Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, et al: The COG database: an updated version includes eukaryotes. BMC Bioinformatics. 2003, 4: 41-10.1186/1471-2105-4-41.PubMedPubMed CentralView ArticleGoogle Scholar
- Tatusov RL, Galperin MY, Natale DA, Koonin EV: The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000, 28: 33-36. 10.1093/nar/28.1.33.PubMedPubMed CentralView ArticleGoogle Scholar
- O'Brien KP, Remm M, Sonnhammer EL: Inparanoid: a comprehensive database of eukaryotic orthologs. Nucleic Acids Res. 2005, 33: D476-480. 10.1093/nar/gki107.PubMedPubMed CentralView ArticleGoogle Scholar
- Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, Church DM, DiCuccio M, Edgar R, Federhen S, Helmberg W, et al: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2005, D39-45.Google Scholar
- Iragne F, Nikolski M, Mathieu B, Auber D, Sherman D: ProViz: protein interaction visualization and exploration. Bioinformatics. 2005, 21: 272-274. 10.1093/bioinformatics/bth494.PubMedView ArticleGoogle Scholar
- Hanisch D, Sohler F, Zimmer R: ToPNet-an application for interactive analysis of expression data and biological networks. Bioinformatics. 2004, 20: 1470-1471. 10.1093/bioinformatics/bth096.PubMedView ArticleGoogle Scholar
- Suzuki H, Saito R, Kanamori M, Kai C, Schonbach C, Nagashima T, Hosaka J, Hayashizaki Y: The mammalian protein-protein interaction database and its viewing system that is linked to the main FANTOM2 viewer. Genome Res. 2003, 13: 1534-1541. 10.1101/gr.956303.PubMedPubMed CentralView ArticleGoogle Scholar
- Breitkreutz BJ, Stark C, Tyers M: Osprey: a network visualization system. Genome Biol. 2003, 4: R22-10.1186/gb-2003-4-3-r22.PubMedPubMed CentralView ArticleGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13: 2498-2504. 10.1101/gr.1239303.PubMedPubMed CentralView ArticleGoogle Scholar
- Lehner B, Fraser AG: A first-draft human protein-interaction map. Genome Biol. 2004, 5: R63-10.1186/gb-2004-5-9-r63.PubMedPubMed CentralView ArticleGoogle Scholar
- Sharan R, Suthram S, Kelley RM, Kuhn T, McCuine S, Uetz P, Sittler T, Karp RM, Ideker T: Conserved patterns of protein interaction in multiple species. Proc Natl Acad Sci USA. 2005, 102: 1974-1979. 10.1073/pnas.0409522102.PubMedPubMed CentralView ArticleGoogle Scholar
- Brown KR, Jurisica I: Online predicted human interaction database. Bioinformatics. 2005, 21: 2076-2082. 10.1093/bioinformatics/bti273.PubMedView ArticleGoogle Scholar
- Huang TW, Tien AC, Huang WS, Lee YC, Peng CL, Tseng HH, Kao CY, Huang CY: POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome. Bioinformatics. 2004, 20: 3273-3276. 10.1093/bioinformatics/bth366.PubMedView ArticleGoogle Scholar
- von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M, Jouffre N, Huynen MA, Bork P: STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res. 2005, D433-437.Google Scholar
- Ulysses. [http://www.cisreg.ca/ulysses]
- Peri S, Navarro JD, Amanchy R, Kristiansen TZ, Jonnalagadda CK, Surendranath V, Niranjan V, Muthusamy B, Gandhi TK, Gronborg M, et al: Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res. 2003, 13: 2363-2371. 10.1101/gr.1680803.PubMedPubMed CentralView ArticleGoogle Scholar
- Deng M, Tu Z, Sun F, Chen T: Mapping Gene Ontology to proteins based on protein-protein interaction data. Bioinformatics. 2004, 20: 895-902. 10.1093/bioinformatics/btg500.PubMedView ArticleGoogle Scholar
- Lin N, Wu B, Jansen R, Gerstein M, Zhao H: Information assessment on predicting protein-protein interactions. BMC Bioinformatics. 2004, 5: 154-10.1186/1471-2105-5-154.PubMedPubMed CentralView ArticleGoogle Scholar
- Walhout AJ, Sordella R, Lu X, Hartley JL, Temple GF, Brasch MA, Thierry-Mieg N, Vidal M: Protein interaction mapping in C. elegans using proteins involved in vulval development. Science. 2000, 287: 116-122. 10.1126/science.287.5450.116.PubMedView ArticleGoogle Scholar
- von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P: Comparative assessment of large-scale data sets of protein-protein interactions. Nature. 2002, 417: 399-403. 10.1038/nature750.PubMedView ArticleGoogle Scholar
- Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, et al: The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 2004, 32: D258-261. 10.1093/nar/gkh066.PubMedView ArticleGoogle Scholar
- Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, Chung S, Emili A, Snyder M, Greenblatt JF, Gerstein M: A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science. 2003, 302: 449-453. 10.1126/science.1087361.PubMedView ArticleGoogle Scholar
- Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M: The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004, 32: D277-280. 10.1093/nar/gkh063.PubMedPubMed CentralView ArticleGoogle Scholar
- Luc PV, Tempst P: PINdb: a database of nuclear protein complexes from human and yeast. Bioinformatics. 2004, 20: 1413-1415. 10.1093/bioinformatics/bth114.PubMedView ArticleGoogle Scholar
- Jurica MS, Moore MJ: Pre-mRNA splicing: awash in a sea of proteins. Mol Cell. 2003, 12: 5-14. 10.1016/S1097-2765(03)00270-3.PubMedView ArticleGoogle Scholar
- Lenhard B, Hayes WS, Wasserman WW: GeneLynx: a gene-centric portal to the human genome. Genome Res. 2001, 11: 2151-2157. 10.1101/gr.199801.PubMedPubMed CentralView ArticleGoogle Scholar
- Atlas Integrated Database System. [http://bioinformatics.ubc.ca/atlas]
- Bader GD, Betel D, Hogue CW: BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 2003, 31: 248-250. 10.1093/nar/gkg056.PubMedPubMed CentralView ArticleGoogle Scholar
- HomoloGene. [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=homologene]
- Wheeler DL, Church DM, Edgar R, Federhen S, Helmberg W, Madden TL, Pontius JU, Schuler GD, Schriml LM, Sequeira E, et al: Database resources of the National Center for Biotechnology Information: update. Nucleic Acids Res. 2004, D35-40. 10.1093/nar/gkh073.Google Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215: 403-410. 10.1006/jmbi.1990.9999.PubMedView ArticleGoogle Scholar
- Hurst LD: The Ka/Ks ratio: diagnosing the form of sequence evolution. Trends Genet. 2002, 18: 486-10.1016/S0168-9525(02)02722-1.PubMedView ArticleGoogle Scholar
- Java Universal Network/Graph Framework. [http://jung.sourceforge.net]
- Java Technology. [http://java.sun.com]
- Java Advanced Imaging (JAI) API. [http://java.sun.com/products/java-media/jai/]
- Apache Tomcat. [http://jakarta.apache.org/tomcat/]
- Trotta CR, Lund E, Kahan L, Johnson AW, Dahlberg JE: Coordinated nuclear export of 60S ribosomal subunits and NMD3 in vertebrates. EMBO J. 2003, 22: 2841-2851. 10.1093/emboj/cdg249.PubMedPubMed CentralView ArticleGoogle Scholar
- Gadal O, Strauss D, Kessl J, Trumpower B, Tollervey D, Hurt E: Nuclear export of 60s ribosomal subunits depends on Xpo1p and requires a nuclear export sequence-containing factor, Nmd3p, that associates with the large subunit protein Rpl10p. Mol Cell Biol. 2001, 21: 3405-3415. 10.1128/MCB.21.10.3405-3415.2001.PubMedPubMed CentralView ArticleGoogle Scholar
- Ho JH, Kallstrom G, Johnson AW: Nmd3p is a Crm1p-dependent adapter protein for nuclear export of the large ribosomal subunit. J Cell Biol. 2000, 151: 1057-1066. 10.1083/jcb.151.5.1057.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.