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Status report of ongoing Metware development.

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  1. 1. MetWare Aiding metabolite identification (and more) Egon Willighagen et al. Wageningen University – Plant Research International / Biometris 2008-05-08, AB@PRI
  2. 2. Who are we? Applied Bioinformatics Group Plant Research International Jan van Haarst (JSF pages) Joost de Groot (ProtoRAW) Mark Fiers (BioMoby services, MetWare integration) Velitchka Mihaleva (RT/RI prediction modeling) Egon Willighagen (SKOS, integration of data preprocessing) Roeland van Ham (group leader) 2008-05-08, AB@PRI
  3. 3. The Setting Metabolite identity accurate mass, retention time isotope pattern full mass spectrum Systems Biology if we change this, what happens to the metabolome? Where, this := genotype, mutations transcription growing conditions geographical location ... 2008-05-08, AB@PRI
  4. 4. The Problems Biochemical biological variation detection limits rough chemical characterization Bio- and Chemoinformatics No uniform access Many manual steps involved Data sizes Diversity of standards and formats Information loss! 2008-05-08, AB@PRI
  5. 5. The Solutions Biochemical wait for next generation devices (not) know your errors maximize information extraction Bio- and Chemoinformatics improve data extraction standardized programming interfaces standardized ontologies standardized data exchange formats (did you spot the pattern?) 2008-05-08, AB@PRI
  6. 6. Our Solution: MetWare + MetFlow 2008-05-08, AB@PRI
  7. 7. MetWare: the building blocks The Design backed up with an ontology conform standards like ArMet Interfaces ... ... towards humans: website (JSF-based) ... towards databases: SQL ... towards softare: Java Beans ... towards workflows: BioMart + webservices ... towards statistics software: BioMart ... towards other databases: Life Science ID 2008-05-08, AB@PRI
  8. 8. MetWare Design: Modules MetRAW storage facility for RAW metabolomics data MetMETA metadata about DoE, genotypes, illness, conditions, ... MetOBSERV observed metabolites, characterized by RT, mass spectrum, NMR, ... MetID know metabolites MetCHAR (putative) identity assignments 2008-05-08, AB@PRI
  9. 9. MetWare Design: Graphical... 2008-05-08, AB@PRI
  10. 10. MetMETA: annotating the wet side 2008-05-08, AB@PRI
  11. 11. MetOBSERV: annotating the dry side 2008-05-08, AB@PRI
  12. 12. MetWare Ontology Extends the ArMet Ontology <skos:Concept rdf:about=quot;Emailquot;> <skos:prefLabel>e-mail</skos:prefLabel> <skos:definition>The email address of the user.</skos:definition> <skos:broader rdf:resource=quot;Peoplequot;/> <skos:broader rdf:resource=quot;&armet;Emailquot;/> </skos:Concept> (No, it does not have to be XML; N3 is fine too. It’s RDF, that’s important.) 2008-05-08, AB@PRI
  13. 13. Resource Description Framework triple the subject is a resource the object might be a resource depending on that, two types of predicates resource predicate resource resource predicate literal See also wp:Resource Description Framework 2008-05-08, AB@PRI
  14. 14. Simple Knowledge Organisation System XML for ontology definition based on RDF and the Web Ontology Language (OWL) unlike OWL it has loose relations (no is a) skos:broader skos:narrower skos:related has a few basic resource-predicate-literal relations skos:prefLabel skos:definition See also wp:SKOS 2008-05-08, AB@PRI
  15. 15. SPARQL Protocol and RDF Query Language See also wp:SPARQL 2008-05-08, AB@PRI
  16. 16. Query Results... See Sesame ( 2008-05-08, AB@PRI
  17. 17. Autogeneration of a Java API and unit tests Why? hides the SKOS/XML hides the SQL Advantages of autogeneration always synchronized to the ontology and the SQL automatically generation of unit tests who said our group didn’t do unit tests ?!?! Where are these Java beans used? the web GUI: JavaServer Faces ... 2008-05-08, AB@PRI
  18. 18. JavaServer Faces: split layout from data model 2008-05-08, AB@PRI
  19. 19. JavaServer Faces: the source 2008-05-08, AB@PRI
  20. 20. MetWare Interface 2: BioMart Integration: Taverna, R, EBI/CSHL 2008-05-08, AB@PRI
  21. 21. ProtoRAW: the RAW data storage Joost can tell you all about this... 2008-05-08, AB@PRI
  22. 22. Collaboration MetWare development Steffen Neumann, Leibniz-Institut f¨r Pflanzenbiochemie, u Halle, Germany our MetWare co-founder in September 2007 BioAssist/NMC, Utrecht/Leiden and Mark/Wageningen joined in April 2008 will focus on clinical data spectral trees integration with workflow environments 2008-05-08, AB@PRI
  23. 23. Conclusions... Provides the tools to automate data analysis Provides storage for your metabolomics data Decreases information loss 2008-05-08, AB@PRI
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