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Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
Using the Ondex system for exploring Arabidopsis regulatory networks
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Using the Ondex system for exploring Arabidopsis regulatory networks

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  • 1. Using the Ondex system for exploring Arabidopsis regulatory networks
    Artem Lysenko
    AAB conference, 14-15 April 2011
    artem.lysenko@bbsrc.ac.uk
  • 2. Biological data in network representation
    ontologies
    protein interactions
    metabolic pathways
  • 3. Ondex system overview
    Data input& transformation
    Data integration
    Visualisation
    Clients/Tools
    Heterogeneous
    data sources
    ONDEX
    Integration
    Methods
    ONDEX
    Visualization
    Tool Kit
    UniProt
    Generalized Object Data Model
    Database Layer
    Accession
    Parser
    Name based
    Web Client
    AraCyc
    Parser
    Transitive
    Taverna
    GO
    Blast
    Parser
    ProteinFamily
    Pfam
    Data Exchange
    Parser
    Pfam2GO
    OXL/RDF
    PDB
    Lucene
    Parser
    WebService
    Source: Ondex SABR project
  • 4. Sparseness of plant data
  • 5. Motivation
    Information about regulation in plants is limited
    KEGG – two maps with 232 and 48 genes related to signalling
    AtRegNet – currently only covers 69 transcription factors in Arabidopsis, however data fro 9375 regulated genes
    Other types of data are more abundant
    Functional annotation
    Protein-protein interactions
    Gene expression
    Use the latter to compensate for the lack of the former
  • 6. More resources = better coverage
    Proteins
    Interactions
  • 7. Inference methods
    Analysis of microarray data
    Meta-coexpression networks from NASC, ArrayExpress and GEO data
    Databases: ATTED-II, CoexpressDB
    Inter species comparison
    Ortholog detection methods: OrthoMCL, Inparanoid
    Databases: resources supporting OrthoXML format
    Prediction of interactions
    “Interolog” and domain-domain approaches
    Databases: AtPID, TAIR predicted interactome
    Prediction of functional role
    Experimentally-determined interaction
    Species A
    Orthology
    Species B
    Inferred interaction
  • 8. The datasets for these application cases
    Functional annotation – Gene Ontology
    GOA EBI
    TAIR
    UniProtKB
    Interaction
    Experimental – BioGrid, IntAct, TAIR
    Predicted – interolog approach
    Expression data – gene coexpression networks
    Targeted subsets from NASC, ArrayExpress and GEO data
  • 9. Example 1: NAR2.1-knockout microarray
    NAR2.1 is required to target the high-affinity nitrate transporter NRT2.1 to plasma membrane
    NRT2.1 is required to take up nitrate at low internal concentrations
    Possible involvement of NAR2.1 in nitrate sensing
    Another nitrate transporter (NRT1.1) have now been demonstrated to also function as a sensor
    Image source: Miller et. al. (2007)
  • 10. From clusters to regulatory relationships
    Meta-coexpression network
    ~140 nitrogen-relevant arrays
    Gene list – nitrogen uptake mutant, grown under low nitrogen
    Mutant versus wild-type
  • 11. From clusters to regulatory relationships
    Localisation: chloroplast
    Component of ribosome
    Regulation of transcription
    Markov clustering
    Functions at 50% coverage
  • 12. From clusters to regulatory relationships
    AT1G11850.1
    LBD38
    AT2G15880.1
    NARS2
    AT1G25550.1
    ATBZIP3
    AT1G06040.1
    TGA1
    AT3G02790.1
    ARR6
    AT2G15880.1
    ATERF13
    WRKY40
    ORA47
    ATERF-1
    AT5G51190.1
    ERF104
    ERF-5
    Identify transcription factors in clusters
    ATSZF2
    AT1G06040.1
    AT3G02790.1
    ATMYB34
  • 13. Example 2: nitrogen-responsive gene list
    Nitrogen-responsive gene list from Gutiérrezet. al. (2007)
    Only N-responsive genes selected
  • 14. PPI-driven signalling/regulation
    • Integrated PPI network:
    • 15. Experimental and predicted PPIs
    • 16. Pull out the PPI links of regulatory significance using GO annotation
    GO: regulation
    Gene list(s)
  • 17. PPI-driven signalling/regulation
    Oxidative stress response
    Cytokinin
    Circadian rhythm
    Auxin
    Gibberellin
  • 18. Nitrogen and phytohormones
    • Cytokinin (CK) and auxin (AUX) are key signals of nitrogen status
    • 19. Regulation of uptake
    • 20. Different regulatory mechanisms in the shoot versus the root
    Image source: Kibaet. al. (2006)
  • 21. Cytokinin, nitrogen and oxidative stress
    • Nitrogen deficiency lead to lower biomass and oxidative stress
    • 22. Cytokinin identified as important for these processes
    • 23. Additional cytokinin in the transgenic plant reduced the effects
  • Acknowledgements
    • The Ondex team
    • 24. Senior colleagues and supervisors:
    • 25. Chris Rawlings, MansoorSaqi, Michael Defoin-Platel, Tony Miller and Charlie Hodgman
    • 26. Funding:
    • 27. PhD studentship: BBSRC (BBS/S/E/2006/13205)
    • 28. Ondex development:
    • 29. Ondex SABR project: BBSRC (BB/F006039/1)

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