From genes to pathways; or why we do data

							     From genes to pathways;
or why we do data mining to couple microarray
    reporter info to known protein functions




  Rachel van Haaften
  BiGCaT Bioinformatics – BMT-TU/e & UM
  Maastricht; May 19 2004
        Two types of omics
Transcriptomics                 Proteomics

Microarrays             Currently only 2D+MS

Values for 20 K genes              Only 20-50
                           identified proteins

Annotation difficult               Annotation
                              is identification

                           Plus modifications
                  Transcriptomics:
               gene expression arrays


Microarrays:
   relative fluorescense signals.
   Identification.
                  Proteomics:
                  2D-gel + MS
                Phosphorylation?   Alternative splicing?




Alternative
splicing?                                   Phosphorylation?
Modification?                               Modification?




    Protein variants derived from single genes
        Functional mapping
                 -GenMAPP-

* Gene Ontology (GO) MAPPS
     contain related genes from the public Gene Ontology
 Project



* Local MAPPs
     contain pathways made by various research institutes
  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
Gene Ontology (GO) levels (II)
Example GO MAPP
Example Local MAPP
Local MAPP with backpage
Visualize expression results
   Visualize expression results




SwissProt
  Annotation of genes/proteins
                 Databases
                  BioASP
Array reporter   local copies




Unigene IDs                     SwissProt IDs
Result
Understanding changes
 Steal and smartly adapt a
    transcriptomics tool:

   GenMapp/Mappfinder

I will show some examples