Systems Biology through Pathway Statistics

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Systems Biology throughPathway Statistics

• Chris Evelo
• BiGCaT Bioinformatics Group BMT-TU/e UM
• Diepenbeek May 14 2004

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Where the cat hunts
BiGCaT Bioinformatics
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BiGCaT Bioinformatics, bridge between two
universities
TU/eIdeas Experience in Data Handling
Universiteit Maastricht Patients,
Experiments,Arrays and Loads of Data
BiGCaT
LUC DiepenbeekStatistical Foundations
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BiGCaT Bioinformatics,between two research fields
Nutritional EnvironmentalResearch
CardiovascularResearch
BiGCaT
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Our usual preygene expression arrays

• Microarrays relative fluorescense signals.
  Identification.

Macroarrays absolute radioactive signal.
Validation.
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Transcriptomics

• The study of genome wide gene
• expression on the transcriptional level
• Where genome wide means gt20K genes.
• And transcriptional level means that somehow
  gt20K mRNA sequences have to be analyzed
• And gt20K expression values have to befiltered,
  normalized, replicate treated,clustered and
  understood
• Thus no transcriptomics without bioinformatics

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No separate statistics?

• Previous slide have to be filtered,
  normalized, replicate treated, clustered and
  understood
• Dont we have to know which genes really changed?

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Changed?

• We need statistical prove of genes changing
  because
• Scientist ask for it.
• Journals ask for it.
• But do we really need it?

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No we dont!

• Biologist will double check anyway
• Largest problem are false positives 1 in 1000
  means 20 on an array!Replicate filtering gets
  rid of that, loosing very little power off
  course that needed statistical proof
• To understand we need pathways not single genes
  (or proteins)

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Two types of arrays
Single longer (gt60 mer) cDNA reporters Agilent,
Incyte,custom 1 value per reporter Reference
variabilityor multi array stats
Multi short(25 mer) oligoreporters Affymetrix
16-20 values perreporterSingle array
statistics
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Systems Biology Triangle
Transcriptomics
microarrays, 20 k (available)
SystemsBiology
Large scale analytical chemistry (developing
outside)
2D-gels, antibody techniques(developing inside)
Metabolomics
Proteomics
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Proteomics would be

• The study of genome wide gene expression
• on the translational level
• Where genome wide would mean gt20K proteins.
• Then proteomics does not yet exist!

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Protein variants derived from single genes
Alternative splicing?
Phosphorylation?
Alternative splicing? Modification?
Phosphorylation? Modification?
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Two types of omics
Transcriptomics Microarrays Values for 20 K
genes Annotation difficult
Proteomics Currently only 2DMS Only
20-50identified proteins Annotationis
identification Plus modifications
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Gene Ontology (GO) levels (I)

The Gene Ontology (GO) project gives a consistent
descriptions of gene products from different
databases.
Amigo browser http//www.godatabase.org/cgi-bin/go
.cgi GO consortium http//www.geneontology.org
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Gene Ontology (GO) levels (II)
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Use of GO classification-GenMAPP-

• GenMAPP Gene MicroArray Pathway Profiler
• Program to visualize Gene Expression Data on
  MAPPs representing biological pathways and
  grouping of genes
• Local MAPPs
• contain pathways made by specific research
  institutes
• Gene Ontology (GO) MAPPS
• contain pathways with functionally related
  genes from the public Gene Ontology Project

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Example Local MAPP
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Example GO MAPP
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Local MAPP
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GO MAPP
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Understanding changes

• Map changed genes/proteins (quantitatively or
  qualitatively) to known pathways.
• Or use information from the Gene Ontology (GO)
  database
• Steal and smartly adapt a transcriptomics tool
• GenMapp/Mappfinder
• Rachel will show some examples