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Arighi C, Shamovsky V, Masci AM, Ruttenberg A, Smith B, Natale DA, Wu C, D'Eustachio P. Correction: Toll-Like Receptor Signaling in Vertebrates: Testing the Integration of Protein, Complex, and Pathway Data in the Protein Ontology Framework. PLoS One 2015; 10:e0131148. [PMID: 26086602 PMCID: PMC4472693 DOI: 10.1371/journal.pone.0131148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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D'Eustachio P. Pathway databases: making chemical and biological sense of the genomic data flood. ACTA ACUST UNITED AC 2013; 20:629-35. [PMID: 23706629 DOI: 10.1016/j.chembiol.2013.03.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Revised: 03/16/2013] [Accepted: 03/22/2013] [Indexed: 01/16/2023]
Abstract
Pathway databases are a means to systematically associate proteins with their functions and link them into networks that describe the reaction space of an organism. Here, the Reactome Knowledgebase provides a convenient example to illustrate strategies used to assemble such a reaction space based on manually curated experimental data, approaches to semiautomated extension of these manual annotations to infer annotations for a large fraction of a species' proteins, and the use of networks of functional annotations to infer pathway relationships among variant proteins that have been associated with disease risk through genome-wide surveys and resequencing studies of tumors.
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Natale DA, Arighi CN, Blake JA, Bult CJ, Christie KR, Cowart J, D'Eustachio P, Diehl AD, Drabkin HJ, Helfer O, Huang H, Masci AM, Ren J, Roberts NV, Ross K, Ruttenberg A, Shamovsky V, Smith B, Yerramalla MS, Zhang J, AlJanahi A, Çelen I, Gan C, Lv M, Schuster-Lezell E, Wu CH. Protein Ontology: a controlled structured network of protein entities. Nucleic Acids Res 2013; 42:D415-21. [PMID: 24270789 PMCID: PMC3964965 DOI: 10.1093/nar/gkt1173] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
The Protein Ontology (PRO; http://proconsortium.org) formally defines protein entities and explicitly represents their major forms and interrelations. Protein entities represented in PRO corresponding to single amino acid chains are categorized by level of specificity into family, gene, sequence and modification metaclasses, and there is a separate metaclass for protein complexes. All metaclasses also have organism-specific derivatives. PRO complements established sequence databases such as UniProtKB, and interoperates with other biomedical and biological ontologies such as the Gene Ontology (GO). PRO relates to UniProtKB in that PRO’s organism-specific classes of proteins encoded by a specific gene correspond to entities documented in UniProtKB entries. PRO relates to the GO in that PRO’s representations of organism-specific protein complexes are subclasses of the organism-agnostic protein complex terms in the GO Cellular Component Ontology. The past few years have seen growth and changes to the PRO, as well as new points of access to the data and new applications of PRO in immunology and proteomics. Here we describe some of these developments.
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Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, Gillespie M, Kamdar MR, Jassal B, Jupe S, Matthews L, May B, Palatnik S, Rothfels K, Shamovsky V, Song H, Williams M, Birney E, Hermjakob H, Stein L, D'Eustachio P. The Reactome pathway knowledgebase. Nucleic Acids Res 2013; 42:D472-7. [PMID: 24243840 PMCID: PMC3965010 DOI: 10.1093/nar/gkt1102] [Citation(s) in RCA: 1141] [Impact Index Per Article: 103.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Reactome (http://www.reactome.org) is a manually curated open-source open-data resource of human pathways and reactions. The current version 46 describes 7088 human proteins (34% of the predicted human proteome), participating in 6744 reactions based on data extracted from 15 107 research publications with PubMed links. The Reactome Web site and analysis tool set have been completely redesigned to increase speed, flexibility and user friendliness. The data model has been extended to support annotation of disease processes due to infectious agents and to mutation.
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Monaco MK, Stein J, Naithani S, Wei S, Dharmawardhana P, Kumari S, Amarasinghe V, Youens-Clark K, Thomason J, Preece J, Pasternak S, Olson A, Jiao Y, Lu Z, Bolser D, Kerhornou A, Staines D, Walts B, Wu G, D'Eustachio P, Haw R, Croft D, Kersey PJ, Stein L, Jaiswal P, Ware D. Gramene 2013: comparative plant genomics resources. Nucleic Acids Res 2013; 42:D1193-9. [PMID: 24217918 PMCID: PMC3964986 DOI: 10.1093/nar/gkt1110] [Citation(s) in RCA: 120] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Gramene (http://www.gramene.org) is a curated online resource for comparative functional genomics in crops and model plant species, currently hosting 27 fully and 10 partially sequenced reference genomes in its build number 38. Its strength derives from the application of a phylogenetic framework for genome comparison and the use of ontologies to integrate structural and functional annotation data. Whole-genome alignments complemented by phylogenetic gene family trees help infer syntenic and orthologous relationships. Genetic variation data, sequences and genome mappings available for 10 species, including Arabidopsis, rice and maize, help infer putative variant effects on genes and transcripts. The pathways section also hosts 10 species-specific metabolic pathways databases developed in-house or by our collaborators using Pathway Tools software, which facilitates searches for pathway, reaction and metabolite annotations, and allows analyses of user-defined expression datasets. Recently, we released a Plant Reactome portal featuring 133 curated rice pathways. This portal will be expanded for Arabidopsis, maize and other plant species. We continue to provide genetic and QTL maps and marker datasets developed by crop researchers. The project provides a unique community platform to support scientific research in plant genomics including studies in evolution, genetics, plant breeding, molecular biology, biochemistry and systems biology.
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Gaudet P, Arighi C, Bastian F, Bateman A, Blake JA, Cherry MJ, D'Eustachio P, Finn R, Giglio M, Hirschman L, Kania R, Klimke W, Martin MJ, Karsch-Mizrachi I, Munoz-Torres M, Natale D, O'Donovan C, Ouellette F, Pruitt KD, Robinson-Rechavi M, Sansone SA, Schofield P, Sutton G, Van Auken K, Vasudevan S, Wu C, Young J, Mazumder R. Recent advances in biocuration: meeting report from the Fifth International Biocuration Conference. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2012; 2012:bas036. [PMID: 23110974 PMCID: PMC3483532 DOI: 10.1093/database/bas036] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The 5th International Biocuration Conference brought together over 300 scientists to exchange on their work, as well as discuss issues relevant to the International Society for Biocuration's (ISB) mission. Recurring themes this year included the creation and promotion of gold standards, the need for more ontologies, and more formal interactions with journals. The conference is an essential part of the ISB's goal to support exchanges among members of the biocuration community. Next year's conference will be held in Cambridge, UK, from 7 to 10 April 2013. In the meanwhile, the ISB website provides information about the society's activities (http://biocurator.org), as well as related events of interest.
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Haw R, Hermjakob H, D'Eustachio P, Stein L. Reactome pathway analysis to enrich biological discovery in proteomics data sets. Proteomics 2012; 11:3598-613. [PMID: 21751369 DOI: 10.1002/pmic.201100066] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Reactome (http://www.reactome.org) is an open-source, expert-authored, peer-reviewed, manually curated database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Pathway Browser is a Systems Biology Graphical Notation-like visualization system that supports manual navigation of pathways by zooming, scrolling and event highlighting, and that exploits PSI Common Query Interface web services to overlay pathways with molecular interaction data from the Reactome Functional Interaction Network and interaction databases such as IntAct, ChEMBL and BioGRID. Pathway and expression analysis tools employ web services to provide ID mapping, pathway assignment and over-representation analysis of user-supplied data sets. By applying Ensembl Compara to curated human proteins and reactions, Reactome generates pathway inferences for 20 other species. The Species Comparison tool provides a summary of results for each of these species as a table showing numbers of orthologous proteins found by pathway from which users can navigate to inferred details for specific proteins and reactions. Reactome's diverse pathway knowledge and suite of data analysis tools provide a platform for data mining, modeling and analysis of large-scale proteomics data sets. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 8).
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Ndegwa N, Côté RG, Ovelleiro D, D'Eustachio P, Hermjakob H, Vizcaíno JA, Croft D. Critical amino acid residues in proteins: a BioMart integration of Reactome protein annotations with PRIDE mass spectrometry data and COSMIC somatic mutations. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2011; 2011:bar047. [PMID: 22025670 PMCID: PMC3199918 DOI: 10.1093/database/bar047] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The reversible phosphorylation of serine, threonine and tyrosine hydroxyl groups is an especially prominent form of post-translational modification (PTM) of proteins. It plays critical roles in the regulation of diverse processes, and mutations that directly or indirectly affect these phosphorylation events have been associated with many cancers and other pathologies. Here, we describe the development of a new BioMart tool that gathers data from three different biological resources to provide the user with an integrated view of phosphorylation events associated with a human protein of interest, the complexes of which the protein (modified or not) is a part, the reactions in which the protein and its complexes participate and the somatic mutations that might be expected to perturb those functions. The three resources used are the Reactome, PRIDE and COSMIC databases. The Reactome knowledgebase contains annotations of phosphorylated human proteins linked to the reactions in which they are phosphorylated and dephosphorylated, to the complexes of which they are parts and to the reactions in which the phosphorylated proteins participate as substrates, catalysts and regulators. The PRIDE database holds extensive mass spectrometry data from which protein phosphorylation patterns can be inferred, and the COSMIC database holds records of somatic mutations found in human cancer cells. This tool supports both flexible, user-specified queries and standard (‘canned’) queries to retrieve frequently used combinations of data for user-specified proteins and reactions. We demonstrate using the Wnt signaling pathway and the human c-SRC protein how the tool can be used to place somatic mutation data into a functional perspective by changing critical residues involved in pathway modulation, and where available, check for mass spectrometry evidence in PRIDE supporting identification of the critical residue. Database URL:http://www.reactome.org/cgi-bin/mart
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Haw RA, Croft D, Yung CK, Ndegwa N, D'Eustachio P, Hermjakob H, Stein LD. The Reactome BioMart. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2011; 2011:bar031. [PMID: 22012987 PMCID: PMC3197281 DOI: 10.1093/database/bar031] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Reactome is an open source, expert-authored, manually curated and peer-reviewed database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Reactome BioMart provides biologists and bioinformaticians with a single web interface for performing simple or elaborate queries of the Reactome database, aggregating data from different sources and providing an opportunity to integrate experimental and computational results with information relating to biological pathways. Database URL:http://www.reactome.org
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Bult CJ, Drabkin HJ, Evsikov A, Natale D, Arighi C, Roberts N, Ruttenberg A, D'Eustachio P, Smith B, Blake JA, Wu C. The representation of protein complexes in the Protein Ontology (PRO). BMC Bioinformatics 2011; 12:371. [PMID: 21929785 PMCID: PMC3189193 DOI: 10.1186/1471-2105-12-371] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2011] [Accepted: 09/19/2011] [Indexed: 11/26/2022] Open
Abstract
Background Representing species-specific proteins and protein complexes in ontologies that are both human- and machine-readable facilitates the retrieval, analysis, and interpretation of genome-scale data sets. Although existing protin-centric informatics resources provide the biomedical research community with well-curated compendia of protein sequence and structure, these resources lack formal ontological representations of the relationships among the proteins themselves. The Protein Ontology (PRO) Consortium is filling this informatics resource gap by developing ontological representations and relationships among proteins and their variants and modified forms. Because proteins are often functional only as members of stable protein complexes, the PRO Consortium, in collaboration with existing protein and pathway databases, has launched a new initiative to implement logical and consistent representation of protein complexes. Description We describe here how the PRO Consortium is meeting the challenge of representing species-specific protein complexes, how protein complex representation in PRO supports annotation of protein complexes and comparative biology, and how PRO is being integrated into existing community bioinformatics resources. The PRO resource is accessible at http://pir.georgetown.edu/pro/. Conclusion PRO is a unique database resource for species-specific protein complexes. PRO facilitates robust annotation of variations in composition and function contexts for protein complexes within and between species.
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D'Eustachio P. Reactome knowledgebase of human biological pathways and processes. Methods Mol Biol 2011; 694:49-61. [PMID: 21082427 DOI: 10.1007/978-1-60761-977-2_4] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The Reactome Knowledgebase is an online, manually curated resource that provides an integrated view of the molecular details of human biological processes that range from metabolism to DNA replication and repair to signaling cascades. Its data model allows these diverse processes to be represented in a consistent way to facilitate usage as online text and as a resource for data mining, modeling, and analysis of large-scale expression data sets over the full range of human biological processes.
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Demir E, Cary MP, Paley S, Fukuda K, Lemer C, Vastrik I, Wu G, D'Eustachio P, Schaefer C, Luciano J, Schacherer F, Martinez-Flores I, Hu Z, Jimenez-Jacinto V, Joshi-Tope G, Kandasamy K, Lopez-Fuentes AC, Mi H, Pichler E, Rodchenkov I, Splendiani A, Tkachev S, Zucker J, Gopinath G, Rajasimha H, Ramakrishnan R, Shah I, Syed M, Anwar N, Babur Ö, Blinov M, Brauner E, Corwin D, Donaldson S, Gibbons F, Goldberg R, Hornbeck P, Luna A, Murray-Rust P, Neumann E, Reubenacker O, Samwald M, van Iersel M, Wimalaratne S, Allen K, Braun B, Whirl-Carrillo M, Cheung KH, Dahlquist K, Finney A, Gillespie M, Glass E, Gong L, Haw R, Honig M, Hubaut O, Kane D, Krupa S, Kutmon M, Leonard J, Marks D, Merberg D, Petri V, Pico A, Ravenscroft D, Ren L, Shah N, Sunshine M, Tang R, Whaley R, Letovksy S, Buetow KH, Rzhetsky A, Schachter V, Sobral BS, Dogrusoz U, McWeeney S, Aladjem M, Birney E, Collado-Vides J, Goto S, Hucka M, Novère NL, Maltsev N, Pandey A, Thomas P, Wingender E, Karp PD, Sander C, Bader GD. Erratum: Corrigendum: The BioPAX community standard for pathway data sharing. Nat Biotechnol 2010. [DOI: 10.1038/nbt1210-1308c] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Croft D, O'Kelly G, Wu G, Haw R, Gillespie M, Matthews L, Caudy M, Garapati P, Gopinath G, Jassal B, Jupe S, Kalatskaya I, Mahajan S, May B, Ndegwa N, Schmidt E, Shamovsky V, Yung C, Birney E, Hermjakob H, D'Eustachio P, Stein L. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 2010; 39:D691-7. [PMID: 21067998 PMCID: PMC3013646 DOI: 10.1093/nar/gkq1018] [Citation(s) in RCA: 1092] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Reactome (http://www.reactome.org) is a collaboration among groups at the Ontario Institute for Cancer Research, Cold Spring Harbor Laboratory, New York University School of Medicine and The European Bioinformatics Institute, to develop an open source curated bioinformatics database of human pathways and reactions. Recently, we developed a new web site with improved tools for pathway browsing and data analysis. The Pathway Browser is an Systems Biology Graphical Notation (SBGN)-based visualization system that supports zooming, scrolling and event highlighting. It exploits PSIQUIC web services to overlay our curated pathways with molecular interaction data from the Reactome Functional Interaction Network and external interaction databases such as IntAct, BioGRID, ChEMBL, iRefIndex, MINT and STRING. Our Pathway and Expression Analysis tools enable ID mapping, pathway assignment and overrepresentation analysis of user-supplied data sets. To support pathway annotation and analysis in other species, we continue to make orthology-based inferences of pathways in non-human species, applying Ensembl Compara to identify orthologs of curated human proteins in each of 20 other species. The resulting inferred pathway sets can be browsed and analyzed with our Species Comparison tool. Collaborations are also underway to create manually curated data sets on the Reactome framework for chicken, Drosophila and rice.
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Natale DA, Arighi CN, Barker WC, Blake JA, Bult CJ, Caudy M, Drabkin HJ, D'Eustachio P, Evsikov AV, Huang H, Nchoutmboube J, Roberts NV, Smith B, Zhang J, Wu CH. The Protein Ontology: a structured representation of protein forms and complexes. Nucleic Acids Res 2010; 39:D539-45. [PMID: 20935045 PMCID: PMC3013777 DOI: 10.1093/nar/gkq907] [Citation(s) in RCA: 87] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
The Protein Ontology (PRO) provides a formal, logically-based classification of specific protein classes including structured representations of protein isoforms, variants and modified forms. Initially focused on proteins found in human, mouse and Escherichia coli, PRO now includes representations of protein complexes. The PRO Consortium works in concert with the developers of other biomedical ontologies and protein knowledge bases to provide the ability to formally organize and integrate representations of precise protein forms so as to enhance accessibility to results of protein research. PRO (http://pir.georgetown.edu/pro) is part of the Open Biomedical Ontology Foundry.
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Demir E, Cary MP, Paley S, Fukuda K, Lemer C, Vastrik I, Wu G, D'Eustachio P, Schaefer C, Luciano J, Schacherer F, Martinez-Flores I, Hu Z, Jimenez-Jacinto V, Joshi-Tope G, Kandasamy K, Lopez-Fuentes AC, Mi H, Pichler E, Rodchenkov I, Splendiani A, Tkachev S, Zucker J, Gopinath G, Rajasimha H, Ramakrishnan R, Shah I, Syed M, Anwar N, Babur O, Blinov M, Brauner E, Corwin D, Donaldson S, Gibbons F, Goldberg R, Hornbeck P, Luna A, Murray-Rust P, Neumann E, Ruebenacker O, Reubenacker O, Samwald M, van Iersel M, Wimalaratne S, Allen K, Braun B, Whirl-Carrillo M, Cheung KH, Dahlquist K, Finney A, Gillespie M, Glass E, Gong L, Haw R, Honig M, Hubaut O, Kane D, Krupa S, Kutmon M, Leonard J, Marks D, Merberg D, Petri V, Pico A, Ravenscroft D, Ren L, Shah N, Sunshine M, Tang R, Whaley R, Letovksy S, Buetow KH, Rzhetsky A, Schachter V, Sobral BS, Dogrusoz U, McWeeney S, Aladjem M, Birney E, Collado-Vides J, Goto S, Hucka M, Le Novère N, Maltsev N, Pandey A, Thomas P, Wingender E, Karp PD, Sander C, Bader GD. The BioPAX community standard for pathway data sharing. Nat Biotechnol 2010; 28:935-42. [PMID: 20829833 PMCID: PMC3001121 DOI: 10.1038/nbt.1666] [Citation(s) in RCA: 439] [Impact Index Per Article: 31.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery.
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Jassal B, Jupe S, Caudy M, Birney E, Stein L, Hermjakob H, D'Eustachio P. The systematic annotation of the three main GPCR families in Reactome. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2010; 2010:baq018. [PMID: 20671204 PMCID: PMC2945921 DOI: 10.1093/database/baq018] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Reactome is an open-source, freely available database of human biological pathways and processes. A major goal of our work is to provide an integrated view of cellular signalling processes that spans from ligand–receptor interactions to molecular readouts at the level of metabolic and transcriptional events. To this end, we have built the first catalogue of all human G protein-coupled receptors (GPCRs) known to bind endogenous or natural ligands. The UniProt database has records for 797 proteins classified as GPCRs and sorted into families A/1, B/2 and C/3 on the basis of amino accid sequence. To these records we have added details from the IUPHAR database and our own manual curation of relevant literature to create reactions in which 563 GPCRs bind ligands and also interact with specific G-proteins to initiate signalling cascades. We believe the remaining 234 GPCRs are true orphans. The Reactome GPCR pathway can be viewed as a detailed interactive diagram and can be exported in many forms. It provides a template for the orthology-based inference of GPCR reactions for diverse model organism species, and can be overlaid with protein–protein interaction and gene expression datasets to facilitate overrepresentation studies and other forms of pathway analysis. Database URL:http://www.reactome.org
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Haw R, Caudy M, Croft D, Garapati P, Gillespie M, Jassal B, Jupe S, Kanapin A, Mahajan S, Matthews L, May B, O'Kelly G, Schmidt E, Shamovsky V, Wu G, Birney E, Hermjakob H, D'Eustachio P, Stein L. Innate Immune Signaling Pathways in Reactome (94.13). THE JOURNAL OF IMMUNOLOGY 2010. [DOI: 10.4049/jimmunol.184.supp.94.13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Abstract
Toll-like receptors (TLR) play a key role in the innate immune system, providing vertebrates with a critical first line of defense against invading microbial pathogens. With the increasing body of molecular data relating to TLR function, organizing this information is useful to biologists and immunologists. We have used Reactome (www.reactome.org), a manually curated pathway database, to organize TLR data so that it can be viewed like an electronic textbook. TLR signaling cascades were authored by experts, maintained by the Reactome editorial staff and cross-referenced to publicly available bioinformatics resources. The Reactome data model generalizes the concept of a biochemical reaction to encompass any transformation of an input set of physical entities into an output set, and thus allows us to integrate TLR cascades with other innate immune processes, adaptive immunity, metabolic pathways, and other signaling processes. Since TLR-signaling molecules and pathways and the signaling pathways they initiate are highly conserved, Reactome provides pathway inference for 22 species based upon improved orthology prediction methods. A new entity-level pathway viewer and pathway analysis tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. All data content and software can be freely used and redistributed under open source terms.
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Krane DE, Bahn V, Balding D, Barlow B, Cash H, Desportes BL, D'Eustachio P, Devlin K, Doom TE, Dror I, Ford S, Funk C, Gilder J, Hampikian G, Inman K, Jamieson A, Kent PE, Koppl R, Kornfield I, Krimsky S, Mnookin J, Mueller L, Murphy E, Paoletti DR, Petrov DA, Raymer M, Risinger DM, Roth A, Rudin N, Shields W, Siegel JA, Slatkin M, Song YS, Speed T, Spiegelman C, Sullivan P, Swienton AR, Tarpey T, Thompson WC, Ungvarsky E, Zabell S. Time for DNA disclosure. Science 2010; 326:1631-2. [PMID: 20019271 DOI: 10.1126/science.326.5960.1631] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Vastrik I, D'Eustachio P, Schmidt E, Gopinath G, Croft D, de Bono B, Gillespie M, Jassal B, Lewis S, Matthews L, Wu G, Birney E, Stein L. Reactome: a knowledge base of biologic pathways and processes. Genome Biol 2009. [PMCID: PMC2688280 DOI: 10.1186/gb-2009-10-2-402] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, de Bono B, Garapati P, Hemish J, Hermjakob H, Jassal B, Kanapin A, Lewis S, Mahajan S, May B, Schmidt E, Vastrik I, Wu G, Birney E, Stein L, D'Eustachio P. Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 2008; 37:D619-22. [PMID: 18981052 PMCID: PMC2686536 DOI: 10.1093/nar/gkn863] [Citation(s) in RCA: 602] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms.
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Tsesmetzis N, Couchman M, Higgins J, Smith A, Doonan JH, Seifert GJ, Schmidt EE, Vastrik I, Birney E, Wu G, D'Eustachio P, Stein LD, Morris RJ, Bevan MW, Walsh SV. Arabidopsis reactome: a foundation knowledgebase for plant systems biology. THE PLANT CELL 2008; 20:1426-36. [PMID: 18591350 PMCID: PMC2483364 DOI: 10.1105/tpc.108.057976] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
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Matthews L, D'Eustachio P, Croft D, de Bono B, Gopinath G, Jassal B, Lewis S, Schmidt E, Vastrik I, Wu G, Birney E, Stein L. An Introduction to the Reactome Knowledgebase of Human Biological Pathways and Processes. ACTA ACUST UNITED AC 2007. [DOI: 10.1038/pid.2007.3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Brueckner M, McGrath J, D'Eustachio P, Horwich AL. Establishment of left-right asymmetry in vertebrates: genetically distinct steps are involved. CIBA FOUNDATION SYMPOSIUM 2007; 162:202-12; discussion 212-8. [PMID: 1802643 DOI: 10.1002/9780470514160.ch12] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Vertebrates exhibit a characteristic pattern of asymmetrical positioning of the visceral organs along the left-right axis. A remarkable developmental step establishes this pattern--primitive organs migrate from symmetrical midline positions of origin into lateral positions. The first organ to pursue such movement is the cardiac tube, which forms a rightward 'D' loop; other organs follow concordantly. The signals and mechanisms directing such organ migration can be studied by analysis of heritable defects of humans and mice. In general, these defects behave as loss-of-function mutations that lead to random determination of visceral situs: for an affected embryo there is an equal chance of correct situs or situs inversus. Distinct phenotypes and patterns of inheritance of these defects suggest that at least three genes are involved in left-right determination, apparently members of a developmental pathway. These genes should be amenable to molecular analysis. We are studying a recessive allele of the mouse called inversus viscerum (iv). Using linkage analysis with cloned restriction fragment length polymorphism markers, we have genetically mapped the iv gene to the distal portion of mouse chromosome 12. We are now pursuing isolation of the gene using methods of positional cloning. Analysis of the iv gene product and of its site and timing of expression may offer clues to how left-right lateralization occurs.
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Vastrik I, D'Eustachio P, Schmidt E, Joshi-Tope G, Gopinath G, Croft D, de Bono B, Gillespie M, Jassal B, Lewis S, Matthews L, Wu G, Birney E, Stein L. Reactome: a knowledge base of biologic pathways and processes. Genome Biol 2007; 8:R39. [PMID: 17367534 PMCID: PMC1868929 DOI: 10.1186/gb-2007-8-3-r39] [Citation(s) in RCA: 409] [Impact Index Per Article: 24.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2006] [Revised: 12/19/2006] [Accepted: 03/16/2007] [Indexed: 11/16/2022] Open
Abstract
Reactome, an online curated resource for human pathway data, can be used to infer equivalent reactions in non-human species and as a tool to aid in the interpretation of microarrays and other high-throughput data sets. Reactome http://www.reactome.org, an online curated resource for human pathway data, provides infrastructure for computation across the biologic reaction network. We use Reactome to infer equivalent reactions in multiple nonhuman species, and present data on the reliability of these inferred reactions for the distantly related eukaryote Saccharomyces cerevisiae. Finally, we describe the use of Reactome both as a learning resource and as a computational tool to aid in the interpretation of microarrays and similar large-scale datasets.
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Delano DL, Montesinos MC, D'Eustachio P, Wiltshire T, Cronstein BN. An interaction between genetic factors and gender determines the magnitude of the inflammatory response in the mouse air pouch model of acute inflammation. Inflammation 2006; 29:1-7. [PMID: 16502340 DOI: 10.1007/s10753-006-8962-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
The widely used mouse air pouch model of acute inflammation is inducible in a variety of inbred strains, but the potential influence of genetic background and gender on inflammation severity has never been examined. We directly compared the degree of inflammation induced in the air pouch model across four commonly utilized inbred strains in both male and female mice. We then applied an in silico mapping method to identify loci potentially associated with determining inflammation severity for each gender. Air pouches were induced by subcutaneous injection 3 (3 cc) and 5 (1.5 cc) days prior to the experiment. 4h after carrageenan injection, exudates were retrieved and leukocyte concentration quantified using a hemocytometer. The in silico mapping method was applied as described below. The strain order for mean leukocyte count/mL in inflamed exudates differed between genders. In males, the order was C57BL/6J > BALB/cByJ > DBA/2J > DBA/1J, while in females the order was BALB/cByJ > DBA/2J > C57BL/6J > DBA/1J. The difference in inflammation severity between genders reached significance only in C57BL/6J mice. Independent in silico analysis based on phenotypic data from male versus female mice identified distinct sets of loci as potentially associated with the exudate count reached. We conclude that the degree of inflammation induced in the mouse air pouch model of inflammation is strain-specific and, therefore, genetically based, and the pattern of interstrain differences is altered in male relative to female mice. The loci identified by in silico mapping likely contain genes with differential roles in determining this phenotype between genders.
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