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      BioPAX – A community standard for pathway data sharing

      research-article
      1 , 2 , 1 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 12 , 14 , 15 , 16 , 17 , , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 1 ,   1 , 2 , 29 , 30 , 31 , 18 , 30 , 32 , 21 , 33 ,   34 , 35 , 36 , 37 , 64 , 38 , 39 , 40 , 11 , 41 , 42 , 43 , 44 , 45 , 41 , 46 , 47 , 5 , 48 , 49 , 50 , 40 , 51 , 52 , 53 , 54 , 55 , 14 , 56 , 33 , 41 , 41 , 57 , 58 ,   59 , 60 , 24 , 2 ,   26 , 33 , 6 , 12 , 61 , 62 , 6 , 45 , 15 , 17 , 63 , 3 , 1 , 18
      Nature biotechnology
      pathway data integration, pathway database, standard exchange format, ontology, information system

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          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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          Most cited references61

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          Integration of biological networks and gene expression data using Cytoscape.

          Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
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            The genetic landscape of a cell.

            A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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              Is Open Access

              Network-based classification of breast cancer metastasis

              Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
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                Author and article information

                Journal
                9604648
                20305
                Nat Biotechnol
                Nature biotechnology
                1087-0156
                1546-1696
                11 August 2010
                9 September 2010
                September 2010
                9 March 2011
                : 28
                : 9
                : 935-942
                Affiliations
                [1 ]Computational Biology, Memorial Sloan-Kettering Cancer Center, New York NY, USA.
                [2 ]Center for Bioinformatics and Computer Engineering Department, Bilkent University, Ankara, Turkey.
                [3 ]SRI International,Menlo Park CA, USA.
                [4 ]Institute for Bioinformatics Research and Development Japan Science and Technology Agency, Tokyo, Japan.
                [5 ]Université libre de Bruxelles, Bruxelles, Belgium.
                [6 ]European Bioinformatics Institute, Hinxton, Cambridge, UK.
                [7 ]Ontario Institute for Cancer Research, Toronto ON, Canada.
                [8 ]NYU School of Medicine, New York NY, USA.
                [9 ]National Cancer Institute, Center for Biomedical Informatics and Information Technology, Rockville MD, USA.
                [10 ]Predictive Medicine, Belmont MA, USA.
                [11 ]BIOBASE Corporation, Beverly MA, USA.
                [12 ]Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico.
                [13 ]Biomolecular Systems Laboratory, Boston University, Boston MA, USA.
                [14 ]Cold Spring Harbor Laboratory, Cold Spring Harbor NY, USA.
                [15 ]McKusick-Nathans Institute of Genetic Medicine and the Departments of Biological Chemistry, Pathology and Oncology, Johns Hopkins University, Baltimore MD , USA.
                [16 ]Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico.
                [17 ]Artificial Intelligence Center, SRI International, Menlo Park CA, USA.
                [18 ]Donnelly Center for Cellular and Biomolecular Research, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada.
                [19 ]Faculté de Médecine, Université Rennes 1, Rennes, France.
                [20 ]Rothamsted Research, Harpenden, UK.
                [21 ]Cell Signaling Technology, Inc. Danvers, MA, USA.
                [22 ]Broad Institute, Cambridge MA, USA.
                [23 ]Center for Food Safety and Applied Nutrition, US Food and Drug Adminsitration, Laurel MD, USA.
                [24 ]Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg VA, USA.
                [25 ]Neurobiology, Neurodegeneration and repair laboratory, National Eye Institute, NIH, Bethesda, MD, USA.
                [26 ]Department of Behavioral Neuroscience. Oregon Health & Science University, Portland OR, USA.
                [27 ]U.S. Environmental Protection Agency Durham, NC USA.
                [28 ]Mathematics & Computer Science Division, Argonne National Laboratory, Argonne, IL, USA.
                [29 ]University of Connecticut Health Center, Farmington, CT, USA.
                [30 ]Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston MA, USA.
                [31 ]Lexikos Corporation, Boston MA, USA
                [32 ]Biotechnology Division, National Institute of Standards and Technology, Gaithersburg MD, USA.
                [33 ]Center for Cancer Research, NCI, NIH, Bethesda MD, USA.
                [34 ]Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Cambridge UK.
                [35 ]Clinical Semantics Group, Lexington MA, USA.
                [36 ]Center for Cell Analysis and Modeling, University of Connecticut Health Center, Storrs CT, USA.
                [37 ]Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland.
                [38 ]Department of Bioinformatics, Maastricht University, Maastricht, Netherlands.
                [39 ]University of Auckland
                [40 ]Syngenta Biotech Inc., Research Triangle Park, North Carolina, USA.
                [41 ]Department of Genetics, Stanford University, Stanford CA, USA.
                [42 ]Loyola Marymount University, Los Angeles CA, USA.
                [43 ]Physiomics PLC, Magdalen Centre, Oxford Science Park Oxford, UK
                [44 ]St. John’s University, Queens NY, USA
                [45 ]Mathematics & Computer Science Division, Argonne National Laboratory, Argonne IL, USA.
                [46 ]The Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
                [47 ]Columbia University, New York NY, USA.
                [48 ]SRA International, USA.
                [49 ]Novartis Knowledge Center, Cambridge MA, USA
                [50 ]University of Ottawa, Ottawa Ontario, Canada
                [51 ]Department of Systems Biology, Harvard Medical School, Boston, MA, USA
                [52 ]Vertex Pharmaceuticals, Cambridge MA, USA.
                [53 ]Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee WI, USA.
                [54 ]Gladstone Institute of Cardiovascular Disease, San Francisco CA, USA.
                [55 ]Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA.
                [56 ]Centre for Biomedical Informatics, School of Medicine, Stanford University, Stanford CA, USA
                [57 ]Computational Sciences, Informatics, Millennium Pharmaceuticals Inc., Cambridge MA, USA
                [58 ]Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda MD, USA.
                [59 ]Institute for Genomics and Systems Biology, The University of Chicago and Argonne National Laboratory, Chicago IL, USA
                [60 ]Total Gas & Power
                [61 ]Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan
                [62 ]Biological Network Modeling Center, California Institute of Technology, Pasadena, CA, USA.
                [63 ]Department of Bioinformatics, Göttingen, Germany.
                [64 ]Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg, Austria.
                Author notes
                Correspondence should be addressed to Gary D. Bader ( biopax-paper@ 123456biopax.org ).

                Author contributions All authors helped develop the BioPAX language, ontology, documentation and examples by participating in workshops or on mailing lists and/or provided data in BioPAX format and/or wrote software that supports BioPAX. See Supplementary Table S1 for a full list of author contributions.

                Article
                nihpa216985
                10.1038/nbt.1666
                3001121
                20829833
                cafadc62-e078-4ce0-8369-c12828f2d8ec

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                History
                Funding
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: U24 GM077678-18 ||GM
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: R13 GM076939-01 ||GM
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: P41 HG004118-01A1 ||HG
                Categories
                Article

                Biotechnology
                ontology,information system,standard exchange format,pathway database,pathway data integration

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