Using GenMapp for pathway analysis Firenze

nullPathway analysis using GenMapp How to understand your omics dataPathway analysis using GenMapp How to understand your omics dataChris Evelo NuGO WP7 BiGCaT Bioinformatics MaastrichtUnderstanding Array dataUnderstanding Array dataTypical procedure Annotate the reporters with something useful (UniProt!) Sort based on fold change Search for your favorite genes/proteins Throw away 95% of the arraynullUnderstanding Array dataUnderstanding Array dataTypical procedure Annotate the reporters with something useful (UniProt!) Sort based on fold change Search for your favorite genes/proteins Throw away 95% of the arrayUnderstanding Array dataUnderstanding 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)MappingMappingAnnotation/ couplingBest known: GenMAPPBest known: GenMAPPFree, academic initiative with editable mapps, collaborates with NuGOBest known: GenMAPPBest known: GenMAPPFull 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 yourselfGenMAPP: Full GO contentGenMAPP: Full GO contentGenMAPP: Textbook like mapsGenMAPP: Textbook like mapsExtensive backpages present with links to online databasesGenMAPP: visualize anything numericalGenMAPP: visualize anything numericalExample 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 QTLsUpdate mapps yourselfUpdate mapps yourselfYou 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)nullMAPPfinderMAPPfinderRanks 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)GenMAPP: Full GO contentGenMAPP: Full GO contentScientist know GenMappScientist know GenMappAdvantages: 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. Scientist know GenMappScientist know GenMappDisadvantages: 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! Datasources 1Datasources 1GenMapp local mapps: Created by a single postdoc (Kam Dahlquist).Datasources 2Datasources 2KEGG: 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.Datasources 2Datasources 2KEGG 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)Datasources 2Datasources 2KEGG: Another example: Apoptosis KEGG Mapp A contributed Mapp Somebody manually converted this Mapp! Great work… But, there are only four of these Datasources 3Datasources 3Gene 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)Datasources 4Datasources 4Alternative programs like GeneGo: Based on expert knowledge (20 Russian biochemists). Allows pathway connection (explained by Rob?) Primitive views of multiple conditions See example resultsGeneGo: primitive view of multiple conditionsGeneGo: primitive view of multiple conditionsCan you really see what happens?NuGO data pathway data collection workflowNuGO data pathway data collection workflowCombine and forward existing maps to limited group of expertsText mining from key genes/metabolitesForward improved maps to limited group of expertsCollect back page infoForward new draft to a larger group of experts within NuGODevelop storage format plus toolsThink of best way to store pathway informationDevelop/adapt entry tools plus convertersTest resulting mapsMake maps availableWorking with Reactome, GenMapp and BioPaxWorking with Reactome, GenMapp and BioPaxBioPAX Plus/GMML 2Expert dataReactomeBioPAXGMMLCurrent GenMapp GenMapp 2NUGO/EBIEBIMDP4/GenMappWith Philippe Rocca and Imre Vastrik (EBI/Reactome) we will define a way to get Reactome views and export them to GenMapp2BiGCaT studentsl created GenMapp 2 – GMML converters with help from Lynn Ferrante (GenMapp.org)Rachel van Haaften (BiGCaT/NuGO) and Marjan van Erk (TNO/NuGO) visited EBI early 2005 to learn doing thisThis step has not been taken care off as of yet… Rachel van Haaften (BiGCaT/NuGO) and Marjan van Erk (TNO/NuGO) will test this and give user feedbackGMML (GenMapp Markup Language) is a superset of BioPAX 1. BioPAX could contain graphical views. (GMML 2 = BioPAX2). But, how doe we make that happen?Can it help you?Can it help you?Seeing the errors and getting useful information A NuGO example Red Wine Polyphenols (Dr Cristina Luceri) Clusters in control group representing pathwaysClusters in control group representing pathwaysCaused by bad technology and bad designAfter adapted normalization:After adapted normalization:The bioinformaticsThe bioinformaticsBiGCaT 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 BiologyThe BiologyProteomics 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) Financial contributions: UM, TUe, Senter IOP, WCFS/ICN, Dutch Heart Foundation, NuGO