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Using pathway information to understand omics data

Using pathway information to understand omics data. Chris Evelo NuGO WP7 BiGCaT Bioinformatics Maastricht. Nu. G O. Un Oslo. Rowett. Un. Ulster. Un Newcastle. Un Lund. Trinity. DiFE. IFR. Un Cork. EBI. Rivm. Rikilt. TNO. Un Reading. Un Wageningen. Un Maastricht. Un Krakow.

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Using pathway information to understand omics data

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  1. Using pathway information to understand omics data Chris EveloNuGO WP7 BiGCaT Bioinformatics Maastricht the European Nutrigenomics Organisation

  2. Nu GO Un Oslo Rowett Un. Ulster Un Newcastle Un Lund Trinity DiFE IFR Un Cork EBI Rivm Rikilt TNO Un Reading Un Wageningen Un Maastricht Un Krakow Un Munich Un Florence Inserm Marseille Un Balearic Illes the European Nutrigenomics Organisation

  3. 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

  4. the European Nutrigenomics Organisation

  5. 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

  6. 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

  7. Mapping Annotation/coupling the European Nutrigenomics Organisation

  8. Best known: GenMAPP Free, academic initiative with editable mapps, collaborates with NuGO the European Nutrigenomics Organisation

  9. 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) • Update mapps yourself the European Nutrigenomics Organisation

  10. GenMAPP: Full GO content the European Nutrigenomics Organisation

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

  12. 2D gels of 3T3-L1 (pre)-adipocytes Enlarged sections gels derived from: A: 3T3-L1 pre-adipocytes, B: 3T3-L1 adipocytes, C: 3T3-L1 adipocytes with caloric restriction D: 3T3-L1 adipocytes with caloric restriction and TNF-a. the European Nutrigenomics Organisation

  13. 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

  14. Update mapps yourself You can do anything.E.g. add genes, annotation, backpage information, graphics Next page shows a combination of metabolic mapps. “The Nutrigenomics Masterpiece” created by Milka Sokolović (AMC Amsterdam) the European Nutrigenomics Organisation

  15. MAPPfinder • Ranks mapps where relatively many changes occur • Useful to find unexpected pathways • Statistics hardly developed the European Nutrigenomics Organisation

  16. MAPPfinder z-score Number of genes/proteins changed on this mapp Expected number of changes Standard deviation of observed number many dependencies to overcome the European Nutrigenomics Organisation

  17. MAPPfinder • Next example from heart failure study(Schroen et al. Circ Res; 2004 95: 506-514) the European Nutrigenomics Organisation

  18. GenMAPP: Full GO content the European Nutrigenomics Organisation

  19. Scientist know GenMapp Advantages: • Free, • Runs on (high end) MS Windows, • Relatively easy to use, • Reasonable visualization, • Some pathway statistics, • Interesting content (Including GO, KEGG), • Content editable, • Adopting standards (e.g. BioPax), • Soon to become open source. the European Nutrigenomics Organisation

  20. Scientist know GenMapp Disadvantages: • Small academic initiative, uncertain lifespan • No info on reactions, metabolites, location • No change (e.g. time course) visualization • Hard to cope with ambiguous reporters(we are working on that) • Content could be better! the European Nutrigenomics Organisation

  21. Metacore example www.genego.com the European Nutrigenomics Organisation GeneGo, Inc Systems ReconstructionTM Technology

  22. Concurrent visualization of different data types Agilent Affymetrix Proteomic SAGE the European Nutrigenomics Organisation

  23. GeneGo: primitive view of multiple conditions Can you really see what happens? the European Nutrigenomics Organisation

  24. Build new networkusing MetacoreTM from GeneGO • Around p53 protein • Making us of biological DB • Filtered to reduce complexity: • for ‘rat ortholog’ • for ‘transcriptional regulation’ • for ‘liver’ the European Nutrigenomics Organisation

  25. the European Nutrigenomics Organisation

  26. Filtering needed to reduce complexity the European Nutrigenomics Organisation

  27. the European Nutrigenomics Organisation

  28. Datasources 1 GenMAPP local MAPPs: Largely created by a single postdoc (Dr.Kam Dahlquist). the European Nutrigenomics Organisation

  29. Datasources 2 KEGG: Older pathway database (Kyoto Japan), on enzyme code (EC) level. Example… The Homo Sapiens Urea cycle Mapp A converted KEGG Mapp Note that not all EC’s were converted and that they don’t have backpages. the European Nutrigenomics Organisation

  30. Datasources 2 KEGG Conversion: = How would you convert EC codes to Swissprot codes? • Go to Swissprot, look for EC code • Add all proteins with that EC code to GenMapp backpage Example: Superoxide dismutase function reaction would have:Cu/Zn-SOD, Mn-SOD and Ex-SOD in backpage… (and that is not what we usually want. Note that many other tools use KEGG converted pathways (e.g. Spotfire Decissionsite, GeneGo, Ingenuity) the European Nutrigenomics Organisation

  31. Datasources 2 KEGG: Another example: Apoptosis KEGG Mapp A contributed Mapp Somebody manually converted this Mapp! Great work… But, there are only four of these the European Nutrigenomics Organisation

  32. Datasources 3 Gene Ontology Database: Simple tree structure database with a of lot biological content (biologist know and like it). Automatic annotation possible even for EST’s See structure in MappFinder (1) (or use Go browser) the European Nutrigenomics Organisation

  33. Datasources 4 Alternative programs like GeneGo: Based on expert knowledge (20 Russian biochemists). the European Nutrigenomics Organisation

  34. NuGO data pathway data collection 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

  35. Rachel van Haaften (BiGCaT/NuGO) and Marjan van Erk (TNO/NuGO) will test this and give user feedback Working with Reactome, GenMapp and BioPax Rachel van Haaften (BiGCaT/NuGO) and Marjan van Erk (TNO/NuGO) visited 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 created GenMapp 2 – GMML converters with help from Lynn Ferrante (GenMapp.org) BioPAX Expert data With Philippe Rocca and Imre Vastrik (EBI/Reactome) we will define a way to get Reactome views and export them to GenMapp2 NUGO/EBI Reactome GMML MDP4/GenMapp EBI GenMapp 2 the European Nutrigenomics Organisation

  36. Can it help you? Seeing the errors and getting useful information A NuGO example Red Wine Polyphenols (Dr Cristina Luceri) the European Nutrigenomics Organisation

  37. Clusters in control grouprepresenting pathways Caused by bad technology and bad design the European Nutrigenomics Organisation

  38. After adapted normalization: the European Nutrigenomics Organisation

  39. BiGCaT Bioinformatics Chris Evelo Rachel van Haaften Arie van Erk Stan Gaj Magali Jaillard Kitty ter Stege Thomasz Kelder Gijs Huisman TNO Zeist Rob Stierum Marjan van Erk EBI Hinxton Susanna Sansone Philippe Rocca Imre Vastrik University Firenze Duccio Cavalieri GenMAPP.org Bruce Conklin Lynn Ferrante The bioinformatics the European Nutrigenomics Organisation

  40. Proteomics Johan Renes (UM) Chris Evelo (BiGCaT) The masterpiece Milka Sokolović (AMC) Wout Lamers (AMC) Magali Jaillard (BiGCaT) Heart Failure Blanche Schroen (UM) Yigal Pinto (UM) Arie van Erk (BiGCaT) Red Wine Polyphenols Cristina Luceri (Firenze) At BiGCaT! RhoA Stolen from Rob Stierum(TNO) The Biology Financial contributions: UM, TUe, Senter IOP, WCFS/ICN, Dutch Heart Foundation, NuGO the European Nutrigenomics Organisation

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