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Systems Biology throughSystems Biology throughPathway StatisticsPathway Statistics
Chris EveloBiGCaT Bioinformatics Group – BMT-TU/e & UMDiepenbeek; May 14 2004
BiGCaT BioinformaticsWhere the cat hunts
BiGCaT Bioinformatics, BiGCaT Bioinformatics, bridge between two universitiesbridge between two universities
Universiteit MaastrichtPatients, Experiments,
Arrays and Loads of Data
TU/eIdeas & Experience in Data Handling
BiGCaT
LUC DiepenbeekStatistical Foundations
BiGCaT Bioinformatics,BiGCaT Bioinformatics,between two research fieldsbetween two research fields
CardiovascularResearch
Nutritional &Environmental
Research
BiGCaT
Our usual prey:Our usual prey:gene expression gene expression arraysarrays
Microarrays: relative fluorescense signals. Identification.
Macroarrays: absolute radioactive signal. Validation.
Transcriptomics:Transcriptomics:
The study of genome wide geneexpression on the transcriptional level
Where genome wide means: >20K genes. And transcriptional level means that somehow
>20K mRNA sequences have to be analyzed And >20K expression values have to be
filtered, normalized, replicate treated,clustered and understood
Thus no transcriptomics without bioinformatics
No separate statistics?:No separate statistics?:
Previous slide: “…have to be: filtered, normalized, replicate treated, clustered and understood”
Don’t we have to know which genes really changed?
Changed?Changed?
We need statistical prove of genes changing because…
Scientist ask for it.Journals ask for it.
But do we really need it?
No we don’t!No we don’t!
Biologist will double check anyway
Largest problem are false positives1 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)
Two types of arraysTwo types of arrays
Single longer (>60 mer) cDNA reporters
Agilent, Incyte,custom
1 value per reporter
Reference variabilityor multi array stats
Multi short(25 mer) oligo
reporters
Affymetrix
16-20 values perreporter
Single array statistics
Systems Biology TriangleSystems Biology Triangle
SystemsBiology
Transcriptomics
MetabolomicsProteomics
microarrays, 20 k(available)
Large scale analytical chemistry
(developing outside)
2D-gels, antibody techniques
(developing inside)
Proteomics would be:Proteomics would be:
The study of genome wide gene expression on the translational level
Where genome wide would mean: >20K proteins.
Then proteomics does not yet exist!
Protein variants derived from single genes
Phosphorylation?Modification?
Alternative splicing?Phosphorylation?
Alternative splicing?Modification?
Two types of omicsTwo types of omics
Transcriptomics
Microarrays
Values for 20 K genes
Annotation difficult
Proteomics
Currently only 2D+MS
Only 20-50identified proteins
Annotationis identification
Plus modifications
Gene Ontology (GO) levels (I)Gene Ontology (GO) levels (I)
Amigo browser http://www.godatabase.org/cgi-bin/go.cgiGO consortium: http://www.geneontology.org
The Gene Ontology (GO) project gives a consistent descriptions of gene products from different databases.
Gene Ontology (GO) levels (II)Gene Ontology (GO) levels (II)
Use of GO classificationUse of GO classification-GenMAPP--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
Example Local MAPPExample Local MAPP
Example GO MAPPExample GO MAPP
Local MAPPLocal MAPP
GO MAPPGO MAPP
Understanding changesUnderstanding 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
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