Pathway content improvement. How to store an expert’s brain and use it to understand omics.

1. Pathway content improvement.How to store an expert’s brain and use it to understand omics. Chris EveloNuGO WP7 BiGCaT Bioinformatics the European Nutrigenomics Organisation

2. Understanding Array data • Typical procedure • Annotate the reporters with something useful (UniProt!) • Sort based on fold change • Search for your favorite genes/proteins • Throw away 95% of the array the European Nutrigenomics Organisation

3. the European Nutrigenomics Organisation

4. Understanding Array data • Typical procedure • Annotate the reporters with something useful (UniProt!) • Sort based on fold change • Search for your favorite genes/proteins • Throw away 95% of the array the European Nutrigenomics Organisation

5. Understanding Array data • “Advanced” procedures • Gene clustering or principal component analysis • Get groups of genes with parallel expression patterns • Useful for diagnosis • Not adding much to understanding (unless combined) the European Nutrigenomics Organisation

6. Functional Mapping Annotation/coupling the European Nutrigenomics Organisation

7. Best known: GenMAPP • Full content of GO database • Textbook like local mapps • Geneboxes with active backpages, coupled to online databases • Visualize anything numerical(fold changes on arrays, p-values, present calls, proteomics results) the European Nutrigenomics Organisation

8. GenMAPP: Full GO content the European Nutrigenomics Organisation

9. GenMAPP:Textbook like maps Extensive backpages present with links to online databases the European Nutrigenomics Organisation

10. GenMAPP: visualize anything numerical Example Proteomics results (2D gels with GC-MS identification). Fasting/feeding study shows regulation of glycolysis (data from Johan Renes, UM). Other useful things:- p-values, present calls- presence in clusters- presence in QTLs the European Nutrigenomics Organisation

11. MAPPfinder • Ranks mapps where relatively many changes occur • Useful to find unexpected pathways • Statistics hardly developed(many dependencies to overcome) • Next example from heart failure study(Schroen et al. Circ Res; 2004 95: 506-514) the European Nutrigenomics Organisation

12. GenMAPP: Full GO content the European Nutrigenomics Organisation

13. Scientist know GenMapp Advantages: • Easy to use, • Reasonable visualization • Some pathway statistics • Interesting content Disadvantages: • Small academic initiative, uncertain lifespan • No info on reactions, metabolites, location • No change (e.g. time course) visualisation the European Nutrigenomics Organisation

14. IOP gut health comment Nice tool but … Content of the maps is not OK! Improve maps! Starting with fatty acid metabolism. the European Nutrigenomics Organisation

15. Proposed workflow Combine and forwardexisting mapsto limited group of experts Think of best way to storepathway information Text miningfrom key genes/metabolites Forward improved mapsto limited group of experts Develop storage format plus tools Collect back page info Forward new draft to alarger group of expertswithin NuGO Develop/adapt entry toolsplus converters Test resulting maps Make maps available the European Nutrigenomics Organisation

16. Text mining Step 1Ask limited group of experts for map layout and central entities Step 2Use text mining starting from there Step 3Combine mining and expert results Expert feedback allows for evaluation of text mining quality. the European Nutrigenomics Organisation

17. Loosing information GenMapp format does not: • Know about reaction types • Know about reaction input and output • Know about location • Etc… And we will learn a lot about those things the European Nutrigenomics Organisation

18. GenMapp/BioPAX Current GenMapp Not a very elegant solution...GenMapp (gene oriented) simply doesn’t fit intoBioPAX (reaction centered) And a lot of format specific work… But it does store extra informationabout interactions, reactions metabolites, localizations etc in BioPAX format. BioPAX BioPAX plus editor Layout data Expert data New GenMapp the European Nutrigenomics Organisation

19. Hinxton meeting with Reactome & GO Current GenMapp • Using Reactome could allow us to: • store everything • use high quality entry tools • ad an extra round of curation (referees) • develop Reactome – BioPAX converters together • convince BioPAX about “plus” • to work with GenMapp on a more general problem Expert data And…use in Reactome itself BioPax plus BioPAX Reactome ?? Layout data New GenMapp the European Nutrigenomics Organisation

20. Adaptation at EBI Current GenMapp Expert data BioPax plus BioPAX Reactome ?? Layout data (EBI/Reactome) will define a way to get Reactome views and export them to GenMapp2 New GenMapp the European Nutrigenomics Organisation

21. Rachel van Haaften (BiGCaT/NuGO) and Marjan van Erk (TNO/NuGO) will test this and give user feedback Adaptation BiGCaT/GenMapp Rachel van Haaften (BiGCaT/NuGO) and Marjan van Erk (TNO/NuGO) will visit EBI early 2005 to learn doing this GMML (GenMapp Markup Language) is a superset of BioPAX 1. BioPAX could contain graphical views. (GMML 2 = BioPAX2). But, how doe we make that happen? This step has not been taken care off as of yet… Current GenMapp BioPAX Plus/GMML 2 BiGCaT students will create GenMapp 2 – GMML converters with help from Lynn Ferrante (GenMapp.org) BioPAX Expert data Philippe Rocca and Imre Vastrik (EBI/Reactome) will define a way to get Reactome views and export them to GenMapp2 NUGO/EBI Reactome GMML BiGCaT/GenMapp EBI GenMapp 2 the European Nutrigenomics Organisation

22. BiGCaT Bioinformatics Chris Evelo Rachel van Haaften Kitty ter Stege Thomasz Kelder Gijs Huisman TNO Zeist Rob Stierum Marjan van Erk EBI Hinxton Susanna Sansone Philippe Rocca Imre Vastrik GenMAPP.org Lynn Ferrante Bruce Conklin Participants the European Nutrigenomics Organisation