1. Larry Lam Southern California Bioinformatics Summer Institute 2009 Graeber Lab – Crump Institute for Molecular Imaging UCLA A Data Management and Analysis Software Platform for Phospho-Proteomics Data

2. Outline Graeber Lab Background Project Objective My Experimental Project (Example Dataset) Software Design Software Demo Conclusion / Future Work Acknowledgements

3. Systems Biology of Cancer Signaling Lab Goals –Understand Cancer Signaling Through Systems Biology Approaches –[long term] Improve Cancer Treatment Signaling Pathway Modeling Through –Kinetics –Phospho-Profiling –Adaptor Complex Analysis

4. Project Objective Develop a Software Platform for Convenient Storage and Analysis of Large-Scale Data Sets -Design Database to Collect and Store Large Scale Proteomic Data Sets -Allow for Comprehensive Meta Information -Simplify Access to Multiple Data Sets -Simplify The Use of Common Tools of Analysis

5. BCR/Abl Leukemia BCR/Abl fusion protein found in - 90% - 95% of chronic myleoid leukemia - 20% of adult acute lymphoblastic leukemia - 5% of children acute lymphoblastic leukemia Analyze the adaptor proteins in BCR/Abl signaling - Adaptor proteins mediate protein interactions http://www.annals.org/cgi/content/full/138/10/819 BaitBait PreyPreyPreyPrey Complex Capture Protein Interacting Protein

6. Experimental Workflow Experimental Protocol Mass Spectometry Quantitation Pipeline Mass Spectometry Quantitation Pipeline IPI Proteomics Database [Complex] NS Filter/ Consolidation Complex Phospho Profiling Quantitation Output File Manual Organization/ Analysis Purification Current Workflow

7. Identifying Interactions of the Crk Adaptor Proteins 1.Genetic modification of pro-B-lymphocytes (Baf3) Express adaptor + streptavidin binding peptide(SBP) 2.Culture 3.Lyse each culture for protein complex purification Crk I LysateCrk L LysateCrk II LysateNTAP Lysate

8. 1.Separation of protein complex with streptavidin beads 2.Trypsin digestion from proteins to peptides 3.Separation of phosphorylated peptides with Fe(III)-NTA beads 4.Liquid Chromotography + Mass Spectometry 5.Quantitation Pipeline Protein Complex Purification P P P P

9. Quantitation Output File Consolidation of quantified peptides and associated proteins per sample All peptides identified All adaptor proteins used Phosphorylation position within the peptide [optional] Peptide SequenceDescription/ IPI Accession Crk ICrk LCrk IINTAP K.ADAAEFWR.KCBL IPI00027269 16568291180239528210190 R.QEAVALLQGQR.HIsoform Crk-II IPI00307991 28593819099245414663281130

10. NS Filter/Consolidation Quantitation Output File Collapse Peptides To Protein Quantity Remove Insignificant Proteins Heatmap Analysis Remove Known Contaminants Peptide SequenceDescription/ IPI Accession Crk ICrk LCrk IINTAP K.ADAAEFWR.KCBL IPI00027269 1706858281012715100 K.ALVIAHNNIEMAK.NCBL IPI00027269 134461139897107514990 R.QEAVALLQGQR.HIsoform Crk-II IPI00307991 79341858314530813908854085523 K.IHYLDTTTLIEPVAR.SIsoform Crk-II IPI00307991 1606297352134941732239600344617083 Quantity Is Normalized For Each Row

11. NS Filter/Consolidation Quantitation Output File Collapse Peptides To Protein Quantity Remove Insignificant Proteins Heatmap Analysis Remove Known Contaminants

12. NS Filter/Consolidation Quantitation Output File Collapse Peptides To Protein Quantity Remove Insignificant Proteins Heatmap Analysis Remove Known Contaminants Protein Enrichment Factor = (Median – NTAP Median)/  Protein NTAP

13. NS Filter/Consolidation Quantitation Output File Collapse Peptides To Protein Quantity Remove Insignificant Proteins Heatmap Analysis Remove Known Contaminants Configuration File of Known Contaminants

14. Statistical Analysis: Peptide Quantity Heatmap Java TreeView High Quantity Low Quantity Crk I Crk L CrkII NTAP Cbl Peptides Crk I Peptides

15. Experimental Workflow Experimental Protocol Mass Spectometry Quantitation Pipeline Mass Spectometry Quantitation Pipeline IPI Proteomics Database [Complex] NS Filter/ Consolidation Complex Phospho Profiling Quantitation Output File Manual Organization/ Analysis Purification Current Workflow Quantitation Import Local DB Statistical Analysis ExternalSources ExternalSources ExternalSources New Workflow

16. Program Design C# GUI Application Quantitation Output File DATA IMPORT MySQL Database DATA QUERY Quantitation Data Set R Statistical Function Programming Language: C# Database: MySQL –Free Statistical Computing: R –Free, Accessible to C#

17. Data Import Methodology 1.Define Meta Data (Descriptors) And Relationships About The Quantitation Values 2.Create The Tables In MySQL 3.Access Using MySQL Connector/Net http://dev.mysql.com/downloads/connector/

18. Statistical Analysis Methodology R Language and Environment for Statistical Computing and Graphics -Modeling -Statistical Tests -Clustering -Heatmaps Develop a Graphical User Interface To R Functions - Access R Functions Through R-(D)COM Interface http://cran.r-project.org/contrib/extra/dcom/

19. Software Demo

20. Conclusion Management Software –Standardized approach in maintaining lab data Analyze Data Sets –Analysis tools highly accessible to biologists of various technical levels Combine Data Sets –Potentially lead to new discoveries

21. Future Work Add More Links To External Database Enhance Data Query Include More Analysis Functions

22. Acknowledgments Graeber Lab Members –Dr. Thomas Graeber –Dr. Björn Titz SoCalBSI Faculty and Members –Dr. Jamil Momand –Dr. Sandy Sharp –Dr. Nancy Warter-Perez –Dr. Wendie Johnston –Dr. Beverly Krilowicz –Ronnie Cheng Funding

23. Main Window

24. Main Window: Options

25. Batch Import

26. Batch Information

27. Sample Information

28. Sample Information: Technical Replicates

29. Feature Type

30. Features

31. Project Assignment

32. Batch Prtotocol Assignment

33. Biological System Assignment

34. Import

35. Batch Query

36. Feature Type Selection

37. Matrix/Heatmap Dialog

38. Heatmap Options

40. Data Import Design Methodology BatchBatch FeatureFeature Label Description Experimenter Date Label Description Feature Type SampleSample Label Description Quality 1.Define Meta Data (Descriptors) About The Quantitation Values - Define Relationships 2.Create The Tables In MySQL 3.Develop Support for MySQL Access - MySQL Connector Feature Value Value Value Type V V V