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Representing Biological Processes: The Reactome Database

Representing Biological Processes: The Reactome Database Gopal Gopinathrao 1 & Peter D’Eustachio 1,2 1 Cold Spring Harbor Laboratory 2 NYU School of Medicine gopinath@cshl.edu deustp01@med.nyu.edu. Reactome is

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Representing Biological Processes: The Reactome Database

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  1. Representing Biological Processes: The Reactome Database Gopal Gopinathrao1 & Peter D’Eustachio1,2 1Cold Spring Harbor Laboratory 2NYU School of Medicine gopinath@cshl.edu deustp01@med.nyu.edu

  2. Reactome is • - reductionist. All of biology can be represented as events that convert input physical entities into output physical entities. • - a generic parts list. Tissue and state specificity of events are not captured. • - qualitative. Kinetic parameters and data are not captured. • - human-centric. Experiments can use reagents from diverse sources, but most biological processes take place in single species, and our focus is on human biological processes. • manually curated. Events are annotated by expert curators, and linked to published data. • open source. All data and software are freely downloadable and reusable.

  3. Regulation Input 1 Output 1 Reaction Pathway Pathway Reaction Reaction Input 2 Output 2 CatalystActivity Data model in a nutshell

  4. Annotating more details • post-translational modifications of proteins • exact locations of entities and events • Annotating more ambiguities • sets of entities - defined, open, and candidate • incompletely specified entities • “black box” reactions

  5. A geometrical compartment set for locating molecules in human cells

  6. The starry sky view of all of Reactome Nucleotidemetabolism Cell cycle& DNA replication Lipid metabolism Notchsignal-ing Carbohydratemetabolism Translation Amino acidmetabolism Apop-tosis DNArepair Transcription Hemo-stasis TCAcycle Posttransla-tional modifi-cations HIV & Influenza life cycles Sterol metab- olism Insulinsignal-ing Glucagonsignaling Xenobioticmetabolism

  7. Reactome Home Page http://brie8.cshl.edu/cgi-bin/frontpage?DB=gk_central

  8. Reactome Event Page http://brie8.cshl.edu/cgi-bin/eventbrowser?DB=gk_central&ID=163767&

  9. Export Formats <owl:Ontology rdf:about=""> <owl:imports rdf:resource="http://www.biopax.org/release/biopax-level2.owl" /> <rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string">BioPAX pathway converted from "DNA Replication" in the Reactome database.</rdfs:comment> </owl:Ontology> <bp:pathway rdf:ID="DNA_Replication"> <bp:PATHWAY-COMPONENTS rdf:resource="#Regulation_of_DNA_replicationStep" /> <bp:PATHWAY-COMPONENTS rdf:resource="#DNA_strand_elongationStep" /> <bp:PATHWAY-COMPONENTS rdf:resource="#DNA_replication_initiationStep" /> <bp:PATHWAY-COMPONENTS rdf:resource="#Switching_of_origins_to_a_post_replicative_stateStep" /> <bp:PATHWAY-COMPONENTS rdf:resource="#DNA_Replication_Pre_InitiationStep" /> <bp:ORGANISM rdf:resource="#Homo_sapiens" /> <bp:NAME rdf:datatype="http://www.w3.org/2001/XMLSchema#string">DNA Replication</bp:NAME> <bp:SHORT-NAME rdf:datatype="http://www.w3.org/2001/XMLSchema#string">DNA Replication</bp:SHORT-NAME> <bp:XREF rdf:resource="#Reactome69306" /> <bp:XREF rdf:resource="#REACT_383.2" /> <bp:COMMENT rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Studies in the past decade have suggested that the basic mechanism of DNA replication initiation is conserved in all kingdoms of life. Initiation in unicellular eukaryotes, in particular Saccharomyces cerevisiae (budding yeast), is well

  10. Bioinformatics Access • BioMart API • MySQL/Perl API • MySQL/Java API • SOAP/WSDL Interface (multiple languages) • Flat files • Database dumps • Local site install (instructions going into CPBI)

  11. direct curation underway Inference Statistics

  12. Validation of inference • Comparison of manually curated yeast reactions from YBP with inferred reactions from human Reactome • Sensitivity: 72% • Specificity: 78%

  13. Inferring chicken reactions from curated human ones

  14. Gaps in Reactome Gopal Gopinathrao, PhD Reactome, CSHL

  15. 1) Gaps in Reactome annotation 2) Gaps in annotate-able information 3) What a network / pathway ontology can do to fill this gap?

  16. Information Cell ular P a thogens

  17. Information Metabolism P a thogens

  18. Signaling Signaling Information

  19. Domains of Biology waiting to be Reactomized Protozoan/Host interactions Developmental pathways Transcriptional regulation Feedback loops Neuroscience topics Degenerative diseases Synaptic processes Cancer processes OMIM-functional (biochemical) Complex diseases Cellular differentiation, Regulation

  20. Unique human proteins used in pathways (in March 2008) 2500 ~16,000 Swissprot section of UniProt Pathogens/ Host interactions 376 Cellular housekeeping 414 476 Metabolism 600 Information Signaling 755

  21. Gaps in Reactome annotation 6000 5000 4000 total proteins 3000 2000 unique proteins unique + isoforms 1000 0 10 20 30 40 release

  22. Some pathway/Int dbs are more equal?

  23. Mind what gets filled in… Are all Swissprot proteins annotatable for pathways/interactions? Can all interactions can be placed in a biologically relevant ‘pathway’ or even sub-graphs of a network? If yes, who is going to validate and how, the biological ‘truth’ of any subgraphs derived from a network? [Terms of biological truth - tissue, regulation, developmental stage, expression …]

  24. Watching the gap… Adding in pathway data decomposed to interactions … Adding PPI data to the above …

  25. NBC Predictions in Reactome

  26. How a network / pathway ontology may help to fill the gap in pathway annotations..

  27. ABCD complex A+B+C+D Feedback loop C<----->D Novel A<----->B C<----->A C<----->B A<-----| B A<----->D Known New regulatory event

  28. Updated model for curation would be: 1. A+B+C+D ABCD complex Feedback loop C<----->D Novel A<----->B A<-----| B New regulatory event Interaction of C and D may regulate ABCD complex formation Evidence from a network ontology 3. Post-translational inhibition of B by A may result in down regulation of A, there by affecting the stability of complex ABCD Evidence from a network ontology in a model organism

  29. The Team • CSHL • Lincoln Stein (PI) • Gopal Gopinathrao (managing editor) • Marc Gillespie, Lisa Matthews, Bruce May, Mike Caudy (curators) • Guanming Wu, Alex Kanapin (developer) • EBI • Ewan Birney (coPI) • Esther Schmidt, Imre Vastrik, David Croft (developers) • Bernard de Bono, Bijay Jassal, Phani Garapati (curators) • NYU • Peter D’Eustachio (co-PI; editor-in-chief) • Shahana Mahajan (curator) P41 HG003751

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