1. BioinformaticsFox Chase Cancer Center Signaling, Microarrays, and Annotations Michael Ochs Information Science and Technology, Fox Chase Cancer Center School of Biomedical Engineering, Drexel University

2. BioinformaticsFox Chase Cancer Center Microarrays and Biology Models by Physics Bayesian Decomposition - An Approach to Solve the Problem Results from Deletion Mutant Data

3. BioinformaticsFox Chase Cancer Center What a Model Means to Me

4. BioinformaticsFox Chase Cancer Center Signalling Pathways Downward, Nature, 411, 759, 2001 mRNA Stimulus Signal Transduction Transcription

5. BioinformaticsFox Chase Cancer Center Making Proteins

6. BioinformaticsFox Chase Cancer Center A Closer Look at Translation RNA Splicing miRNA Post-Trans- lational Modification

7. BioinformaticsFox Chase Cancer Center Model Block Protein-Protein Interaction Leads to Loss of Some Transcripts, Reduction of Others Depending on Active Signaling Pathways But the Gene Lists are Incomplete as are the Network Diagrams!

8. BioinformaticsFox Chase Cancer Center Identifying Pathways www.promega.com ABCD ABCDABCD 1 2 3 123

9. BioinformaticsFox Chase Cancer Center Goal of Analysis Take measurements of thousands of genes, some of which are responding to stimuli of interest ** 12 3 * * * * then identify the pathways And find the correct set of basis vectors that link to pathways

10. BioinformaticsFox Chase Cancer Center Microarrays and Biology Models by Physics Bayesian Decomposition - An Approach to Perform Analysis Results from Deletion Mutant Data

11. BioinformaticsFox Chase Cancer Center BD: Matrix Decomposition * * * * * Data X gene 1 gene N * * gene 1 gene N pattern 1 pattern k condition 1 condition M * * * * * pattern 1 pattern k condition 1 condition M Distribution of Patterns Patterns of Behavior = The behavior of one gene can be explained as a mixture of patterns with different behaviors

12. BioinformaticsFox Chase Cancer Center The Model Pathways Linked to Multiply Regulated Genes Positivity (No Negative Expression) Classification –Group 1 is Tumor –Group 2 is Normal Regulation –Genes Regulated by a Single Transcription Factor –Genes Known to be Coregulated (e.g., ribosomal proteins)

13. BioinformaticsFox Chase Cancer Center Correlations and Biology * * gene 1 gene N pattern 1 pattern k * * * * * pattern 1 pattern k condition 1 condition M Distribution of Patterns Patterns of Behavior

14. BioinformaticsFox Chase Cancer Center Microarrays and Biology Models by Physics Bayesian Decomposition - An Approach to Perform Analysis Results from Deletion Mutant Data

15. BioinformaticsFox Chase Cancer Center Deletion Mutant Data Set 300 Deletion Mutants in S. cerevisiae –Biological/Technical Replicates with Gene Specific Error Model –Filter Genes >25% Data Missing in Ratios or Uncertainties < 2 Experiments with 3 Fold Change –Filter Experiments < 2 Genes Changing by 3 Fold 228 Experiments/764 Genes (Hughes et al, Cell, 102, 109, 2000)

16. BioinformaticsFox Chase Cancer Center BD: Matrix Decomposition * * * * * Data X gene 1 gene N * * gene 1 gene N pattern 1 pattern k Mutant 1 Mutant M * * * * * pattern 1 pattern k Mutant 1 Mutant M Distribution of Patterns (what genes are in patterns) Patterns of Behavior (does mutant contain pattern) =

17. BioinformaticsFox Chase Cancer Center Genes in Patterns Pattern 1 –403 Genes Pattern 2 –410 Genes Pattern 3 –390 Genes Pattern 4 –276 Genes Pattern 5 –355 Genes Pattern 6 –297 Genes Pattern 7 –223 Genes

18. BioinformaticsFox Chase Cancer Center Annotating Genes Goals Being Left Behind –Identifying a List of Differentially Expressed Genes –Discriminating Classes Goals Now of Interest –Identifying Changes in Pathways –Identifying Active Biological Processes –Identifying Active Biological Functions

19. BioinformaticsFox Chase Cancer Center Gene Ontology Location Function Process

20. BioinformaticsFox Chase Cancer Center Those are all PROTEINS! ESTs and Oligonucleotides –Short Sequences, Not Proteins, Not Genes –Need to Link these to Genes Clustering Sequences –UNIGENE/LocusLink –TIGR Gene Indices –BLAST Annotating Genes –Experimental –Computational

21. BioinformaticsFox Chase Cancer Center UNIGENE Take ESTs, Align Together –EST ~400 nucleotides –Mismatch Allowed Reasonably High 123,995 “Genes” –~10,000 Experimental Genes –~few thousand Estimated Genes

22. BioinformaticsFox Chase Cancer Center TIGR Take ESTs, Align Together into TC –EST ~400 nucleotides –Highly Restrictive Match 40 bp, 90% match, max 30 bp gap

23. BioinformaticsFox Chase Cancer Center Annotating Genes

24. BioinformaticsFox Chase Cancer Center Gene Ontology (Process)

25. BioinformaticsFox Chase Cancer Center Mating Response (Posas, et al, Curr Opin Microbiology, 1, 175, 1998) Amount of Behavior Explained by Mating Pathway for Mutants Ste2 Ste20 Ste5 Ste11 Ste7 Fus3 Ste12 P

26. BioinformaticsFox Chase Cancer Center Conclusions BD Identifies Patterns Related to Underlying Physiology BD Uses Prior Knowledge to Guide Data Analysis With Adequate Information, BD Links Expression Changes to Pathway Activity Proteomics, TF Binding Data, and Future Data Types are Easily Included

27. BioinformaticsFox Chase Cancer Center Acknowledgements Tom Moloshok Jeffrey Grant Yue Zhang Elizabeth Goralczyk Luke Somers Michael Slifker Collaborators A.Godwin (FCCC) B. Eisenberg (FCCC > Dartmouth) J.-M. Claverie (CNRS) G. Parmigiani (JHU) E. Korotkov (RAS) Ghislain Bidaut Andrew Kossenkov Vladimir Minayev Garo Toby Bill Speier (Johns Hopkins) Daniel Chung DJ Datta (UCSF) Frank Manion Bob Beck Fox Chase