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Pathway content improvement. How to store an expert’s brain and use it to understand omics. Chris Evelo NuGO WP7 BiGCaT Bioinformatics. Understanding Array data. Typical procedure Annotate the reporters with something useful (UniProt!) Sort based on fold change - PowerPoint PPT Presentation
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the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO
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
NuNuGOGONuNuGOGO Understanding Array data
Typical procedure1. Annotate the reporters
with something useful (UniProt!)
2. Sort based on fold change
3. Search for your favorite genes/proteins
4. Throw away 95% of the array
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO Understanding Array data
Typical procedure1. Annotate the reporters
with something useful (UniProt!)
2. Sort based on fold change
3. Search for your favorite genes/proteins
4. Throw away 95% of the array
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO Understanding Array data
“Advanced” procedureso Gene clustering or
principal component analysis
o Get groups of genes with parallel expression patterns
o Useful for diagnosiso Not adding much to
understanding (unless combined)
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO Functional Mapping
Annotation/coupling
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO 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
NuNuGOGONuNuGOGO GenMAPP: Full GO content
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGOGenMAPP:
Textbook like maps
Extensive backpages
present with links to
online databases
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGOGenMAPP: 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
NuNuGOGONuNuGOGO 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
NuNuGOGONuNuGOGO GenMAPP: Full GO content
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO 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
NuNuGOGONuNuGOGO 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
NuNuGOGONuNuGOGO Proposed workflowCombine and forward
existing mapsto limited group of experts
Text miningfrom key genes/metabolites
Forward improved mapsto limited group of experts
Collect back page info
Forward new draft to alarger group of experts
within NuGO
Develop storage format plus tools
Think of best way to storepathway information
Develop/adapt entry toolsplus converters
Test resulting maps
Make maps available
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO 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
NuNuGOGONuNuGOGO 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
NuNuGOGONuNuGOGO GenMapp/BioPAX
Expert data
Current GenMapp
BioPAX
Layout data
BioPAX plus editor
New 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.
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO
BioPax plus
Hinxton meeting with Reactome & GO
Expert data
ReactomeBioPAX
Layout data
Current GenMapp
New 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
And…use in Reactome
itself
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO
BioPax plus
Adaptation at EBI
Expert data
ReactomeBioPAX
Layout data
Current GenMapp
New GenMapp
??
(EBI/Reactome) will define a way to get Reactome views and export
them to GenMapp2
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO
BioPAX Plus/GMML 2
Adaptation BiGCaT/GenMapp
Expert data
Reactome
BioPAX
GMML
Current GenMapp
GenMapp 2
NUGO/EBI
EBI BiGCaT/GenMapp
Rachel van Haaften
(BiGCaT/NuGO) and Marjan van Erk (TNO/NuGO)
will visit EBI early 2005 to
learn doing thisPhilippe Rocca and Imre Vastrik (EBI/Reactome)
will define a way to get Reactome views and export
them to GenMapp2
Rachel van Haaften
(BiGCaT/NuGO) and Marjan van Erk (TNO/NuGO) will test this and
give user feedback BiGCaT students
will create GenMapp 2 –
GMML converters with help from Lynn
Ferrante (GenMapp.org)
This step has not been taken care off as of yet…
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?
the European Nutrigenomics Organisation
NuNuGOGONuNuGOGO Participants
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
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