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Signaling, Microarrays, and Annotations

Signaling, Microarrays, and Annotations. Michael Ochs Information Science and Technology, Fox Chase Cancer Center School of Biomedical Engineering, Drexel University. Microarrays and Biology. Models by Physics Bayesian Decomposition - An Approach to Solve the Problem

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Signaling, Microarrays, and Annotations

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

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

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

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

  5. MakingProteins Fox Chase Cancer Center

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

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

  8. A B C D 3 1 2 A B C D Identifying Pathways 1 2 3 www.promega.com Fox Chase Cancer Center

  9. Take measurements of thousands of genes, some of which are responding to stimuli of interest 3 1 2 And find the correct set of basis vectors that link to pathways * * * * * * then identify the pathways Goal of Analysis Fox Chase Cancer Center

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

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

  12. 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) Fox Chase Cancer Center

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

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

  15. Deletion Mutant Data Set (Hughes et al, Cell, 102, 109, 2000) • 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 Fox Chase Cancer Center

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

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

  18. 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 Fox Chase Cancer Center

  19. Location Function Process Gene Ontology Fox Chase Cancer Center

  20. 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 Fox Chase Cancer Center

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

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

  23. Annotating Genes Fox Chase Cancer Center

  24. Gene Ontology (Process) Fox Chase Cancer Center

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

  26. 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 Fox Chase Cancer Center

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

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