1. Understanding Proteomics through Bioinformatics Chris Evelo BiGCaT Bioinformatics Group – BMT-TU/e & UM Masterclass Nutrigenomics; May 11 2004

2. BiGCaT Bioinformatics Where the cat hunts

3. BiGCaT Bioinformatics, bridge between two universities Universiteit Maastricht Patients, Experiments, Arrays and Loads of Data TU/e Ideas & Experience in Data Handling BiGCaT

4. BiGCaT Bioinformatics, between two research fields Cardiovascular Research Nutritional & Environmental Research BiGCaT

5. If transcriptomics is: The study of genome wide gene expression 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

6. Gene expression arrays Microarrays: relative fluorescense signals. Identification. Macroarrays: absolute radioactive signal. Validation.

7. Then 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! Does it already need bioinformatics?

8. Identification of proteins found (method annotation) Antibody techniques: build in. You know what the antigen is or you wouldn’t use it. Mass identification: Fragment libraries derived from Swissprot Not normally a user (scientist) problem. Or practically build in as well. No current need for bioinformatics But please use Swissprot ID’s!!

9. Data filtering and normalization Appears to become a problem on antibody arrays (see yesterdays presentation by Rachelle van Haaften). Start with expertise from mRNA microarrays. Use bioinformatics to improve techniques Not to cover up problems

10. 2 time Expr. level Clustering: find proteins with same expression patterns T1 signal T2 signal Left hand picture shows expression patterns for 2 proteins (these should probably end up in the same cluster). Right hand picture shows the expression vector for one protein for the first 2 dimensions. Can be normalized by amplitude (circle) or relatively (square).

11. Clustering and grouping of proteins with parallel expressions Fancy techniques clustering, principal component analysis, self organizing maps, etc. etc. But… Only useful for high numbers (and maybe not even then) Currently not important for proteomics But might be useful in combined mRNA/protein studies

12. Two things left Functional understanding of proteomics results Understanding protein modifications

13. Functional understanding Map changed proteins (quantitatively or qualitatively) to known pathways. Or use information from the Gene Ontology (GO) database Steal and smartly adapt a transcriptomics tool: GenMapp/MappfinderMappfinder Let me show you an example from a simple nutrigenomics (starvation) study. Data from Johan Renes.

14. Understanding protein modifications Map changed proteins (quantitatively or qualitatively) to known pathways. Or use information from the Gene Ontology (GO) database Steal and smartly adapt a transcriptomics tool: GenMapp/MappfinderMappfinder Let me show you an example from a simple nutrigenomics (starvation) study. Data from Johan Renes.

15. Protein variants derived from single genes Phosphorylation? Modification? Alternative splicing?Phosphorylation? Alternative splicing? Modification?

16. Understanding modifications Look up the protein in SwissProt For instance: – Glyceraldehyde 3-phosphate dehydrogenase Glyceraldehyde 3-phosphate dehydrogenase – Pyruvate kinase (note splice variants) Pyruvate kinase Or use Prosite Search For instance:Prosite Search – Glyceraldehyde 3-phosphate dehydrogenase with: PKC phosphorylation site and: its own GAPDH pattern Glyceraldehyde 3-phosphate dehydrogenasePKC phosphorylation siteits own GAPDH pattern Bioinformatics helps to see the possibilities

17. We should start developing Bioinformatics for Proteomics Now - To help improve the techniques - To make the most of the data - To prevent drowning in data in the future - And to really understand all that transcriptomics stuff