Using pathway information to understand genomics results
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Using pathway information to understand genomics results. Chris Evelo BiGCaT Bioinformatics. 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.

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Using pathway information to understand genomics results

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Using pathway information to understand genomics results

Using pathway information to understand genomics results

Chris EveloBiGCaT Bioinformatics

the European Nutrigenomics Organisation


Understanding array data

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


Using pathway information to understand genomics results

the European Nutrigenomics Organisation


Understanding array data1

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


Understanding array data2

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)

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Functional mapping

Functional Mapping

Annotation/coupling

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Best known genmapp

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)

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Genmapp full go content

GenMAPP: Full GO content

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Genmapp textbook like maps

GenMAPP:Textbook like maps

Extensive backpages present with links to online databases

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Genmapp visualize anything numerical

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

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Mappfinder

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)

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Genmapp full go content1

GenMAPP: Full GO content

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Scientist know genmapp

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) visualization

  • Content could be better!

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Datasources 1

Datasources 1

GenMapp local mapps:

Created by a single postdoc (Kam Dahlquist).

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Datasources 2

Datasources 2

KEGG:

Older pathway database (Kyoto Japan), on enzyme code (EC) level.

Annotation problems for automatic annotation (no absolute match between EC and SP ID)

Contributed and converted Mapps

See example

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Datasources 3

Datasources 3

Gene Ontology Database:

Simple tree structure database with a lot biological content (biologist know and like it).

Automatic annotation possible even for EST’s

See structure in GenMapp 1 (or use Go browser)

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Datasources 4

Datasources 4

Alternative programs like GeneGo:

Based on expert knowledge (20 Russian biochemists).

Allows pathway connection (explain)

Primitive views of multiple conditions

See example results

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Using pathway information to understand genomics results

Fatty acid oxidation - II

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Using pathway information to understand genomics results

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Datasources 5

Datasources 5

Reactome:

Curated reactions database (with n-dimensional interconnections) from EBI e.a.

Still lacks views and export options

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Proposed workflow

Started as a NuGO and IOP gut health initiative. Waiting for expert response.

(Add another map)

Proposed workflow

Combine and forwardexisting mapsto limited group of experts

Evaluated some commercial tools (pathway assist).

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

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Storing pathway data

Rachel van Haaften (BiGCaT/NuGO) and Marjan van Erk (TNO/NuGO) will test this and give user feedback

Storing pathway data

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 do we make that happen?

This step has not been taken care off as of yet…

Current GenMapp

BioPAX Plus/GMML 2

BiGCaT Tue students created GenMapp 2 – GMML converter 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

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Reactome to genmapp

Reactome to GenMapp

Current status:

Export via MS-Access shows only some content (reaction numbers) in GenMapp.

Example

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Views on reactions 1

Views on reactions 1

Reaction databases are build from interconnected reactions (pathways).

Some of these reactions may connect to other known pathways.

Combined pathways may form knew pathways that we didn’t know.

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Views on reactions 2

Views on reactions 2

Unknown pathways may not be connected in the database yet.

But they may:

  • Show co-regulation

  • Plus contain regulatory elements (We can’t analyze those from scratch…)

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Challenges

Challenges

Biological concepts and available tools do not yet meet!

Biologists:

  • know what they want

  • don’t know how to do it or what problems are involved.

    Computer science people:

  • know how it should be done

  • but not what should be done.

    BMT students have shown to be able to work on the interface

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