1. IBU MAD 29 nov 2004, Frans Verster 1 Generic Tools and Methods for Data Handling Generic vs. Specific

2. IBU MAD 29 nov 2004, Frans Verster 2 Generic Models for “-omics” experiments Models for “-omics” experiments Generate database scheme from model Generate database scheme from model Generate XML schema from model Generate XML schema from model Generate access code from model Generate access code from model Special Interest Group Generic Tools and Methods Data Handling Special Interest Group Generic Tools and Methods Data Handling Using ontologies Using ontologies ‘Investment for the future’ ‘Investment for the future’

3. IBU MAD 29 nov 2004, Frans Verster 3 Specific Micro Array Gene Expression (MAGE) data Micro Array Gene Expression (MAGE) data MAGE-stk toolkit: MAGE-stk toolkit:  MAGE-ML import/export  MAGE-OM api access ‘Quick and dirty’ ‘Quick and dirty’

4. IBU MAD 29 nov 2004, Frans Verster 4 SIG Generic Tools and Methods Data Handling

5. IBU MAD 29 nov 2004, Frans Verster 5 Status ‘generic’ Get the best from 3 worlds: doc+DB+code Get the best from 3 worlds: doc+DB+code Unified tools not yet found Unified tools not yet found Evaluate part-to-part connections: Evaluate part-to-part connections: Databases Documents XML Programming Castor JDO Hibernate Code generation X / R / OO Data / Doc centric Castor JDO Hibernate Code generation Mt, XTables

6. IBU MAD 29 nov 2004, Frans Verster 6 Framework so far UML XMIODL DDL OO DBMS Matisse) models VL OO programs XQuery Bridge ? Xquery ad hoc queries GraphViz Graphical output Tabular output converterMAGE-VL file code Mt. exe XML Schema Xforms XGUI MAGE-ML file

7. IBU MAD 29 nov 2004, Frans Verster 7 Visualization using Xquery,dotty and Graphviz

8. IBU MAD 29 nov 2004, Frans Verster 8 Matisse+J2EE+SVG data viewer Objects are Nodes Objects are Nodes Relations are Edges Relations are Edges Looks like graph visualization with few algorithms and parameters: Looks like graph visualization with few algorithms and parameters:  Too many neighbors: pruning  Hubs and authorities; clusters; networks  Multiple views: IsA, HasA, etc.

9. IBU MAD 29 nov 2004, Frans Verster 9 Example ‘select * from Person’

10. IBU MAD 29 nov 2004, Frans Verster 10 Summary, The End Nice ideas Nice ideas  Model independent  Re-use of tools in VLE Progress (too ?) slow on my own Progress (too ?) slow on my own  Crappy tools  More resources  Or skip parts

11. IBU MAD 29 nov 2004, Frans Verster 11 Normalization project VLE (too much?) Focus on ‘generic’ (too much?) Focus on ‘generic’ Build VLE like ‘-omics’ framework. Build VLE like ‘-omics’ framework. Using Matisse OO-DBMS Using Matisse OO-DBMS With web-access data viewer With web-access data viewer Java and R access Java and R access (specific) MAGE-ML I/O (specific) MAGE-ML I/O Web-services Web-services

12. IBU MAD 29 nov 2004, Frans Verster 12 Status Matisse OO-DBMS MAGE-OM model to Matisse OO DBMS MAGE-OM model to Matisse OO DBMS  Lose some information (packages export MAGE-ML)  Matisse is ‘cripple-ware’  Almost no support MAGE-ML data converter for import MAGE-ML data converter for import  But Matisse gives no error message

13. IBU MAD 29 nov 2004, Frans Verster 13 Status web-access data viewer Access with J2EE+HTML in tables Access with J2EE+HTML in tables  Data is too complex for tables J2EE+Form based editor for mage-ml J2EE+Form based editor for mage-ml  Have DTD but need XSD J2EE+SVG data viewer J2EE+SVG data viewer  Nice, wait for 2 nd presentation  Need tighter integration  Tools (dot) are crashy

14. IBU MAD 29 nov 2004, Frans Verster 14 Status Java and R access R coupling of data files via J2EE+XML R coupling of data files via J2EE+XML  Too slow Java code generation Java code generation  Matisse works well  Reflection also (everything is just a generic object)

15. IBU MAD 29 nov 2004, Frans Verster 15 Status MAGE-ML I/O Import Import  Matisse gives no error message Export Export  Lose some information (packages export MAGE-ML)

16. IBU MAD 29 nov 2004, Frans Verster 16 Status Web-services and Ontologies Kept in mind but no work done Kept in mind but no work done  Connection with Taverna should be possible  What the heck are ontologies?